From 626d91b745084a55d1f80e873430add11b38b84d Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 10 Apr 2026 15:16:58 +0100
Subject: [PATCH 01/68] feat: add pilot sweep

---
 cspell/library-words.txt |   6 +
 cspell/project-words.txt |   2 +
 pyproject.toml           |  15 +-
 sweep/infer.py           | 179 ++++++++++++++++++++
 sweep/master_config.yaml | 100 +++++++++++
 sweep/sweep.py           | 357 +++++++++++++++++++++++++++++++++++++++
 6 files changed, 658 insertions(+), 1 deletion(-)
 create mode 100644 sweep/infer.py
 create mode 100644 sweep/master_config.yaml
 create mode 100644 sweep/sweep.py

diff --git a/cspell/library-words.txt b/cspell/library-words.txt
index dc8d8fa..70c464c 100644
--- a/cspell/library-words.txt
+++ b/cspell/library-words.txt
@@ -9,3 +9,9 @@ ylabel
 frameon
 allclose
 linalg
+dotenv
+wandb
+jinja
+adamw
+checkpointing
+proc
diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index 43b5325..64ecef8 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -12,3 +12,5 @@ bonf
 bonferroni
 aeiou
 stylometrically
+lora
+resumability
diff --git a/pyproject.toml b/pyproject.toml
index 685ceb0..daff727 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -77,6 +77,9 @@ convention = "pep257"
     "ARG005",
     "B007",
 ]
+"sweep/**/*.py" = [
+    "ANN401",
+]
 
 [tool.mypy]
 python_version = "3.12"
@@ -103,7 +106,17 @@ module = [
     "matplotlib",
     "matplotlib.*",
     "sentence_transformers",
-    "sentence_transformers.*"
+    "sentence_transformers.*",
+    "peft",
+    "peft.*",
+    "torch",
+    "torch.*",
+    "pandas",
+    "pandas.*",
+    "yaml",
+    "dotenv",
+    "transformers",
+    "transformers.*",
 ]
 ignore_missing_imports = true
 
diff --git a/sweep/infer.py b/sweep/infer.py
new file mode 100644
index 0000000..2c5f09e
--- /dev/null
+++ b/sweep/infer.py
@@ -0,0 +1,179 @@
+"""Inference and test-loss script for a single fine-tuned checkpoint.
+
+Loads the base model with LoRA adapter, runs greedy generation on the Bush
+test split, and computes token-weighted NLL loss over assistant turns only.
+
+Outputs a single JSON object to stdout::
+
+    {"eval_loss": float, "completions": [{messages, generated_response}, ...]}
+
+All progress logs go to stderr so stdout stays clean for sweep.py to parse.
+
+Usage::
+
+    python infer.py --checkpoint /path/to/checkpoint-300
+"""
+
+import argparse
+import json
+import os
+import sys
+from typing import Any
+
+import torch
+from datasets import load_dataset
+from dotenv import load_dotenv
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+load_dotenv()
+
+BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
+DATASET = "AccelerateScience/bush-dataset"
+MAX_NEW_TOKENS = 512
+
+
+def _log(msg: str) -> None:
+    print(msg, file=sys.stderr, flush=True)
+
+
+def load_model_and_tokenizer(
+    checkpoint: str,
+    hf_token: str,
+) -> tuple[Any, Any]:
+    """Load the base model with LoRA adapter from a checkpoint directory.
+
+    :param checkpoint: path to the epoch checkpoint directory.
+    :param hf_token: HuggingFace API token for private model access.
+    :returns: (model, tokenizer) tuple ready for inference.
+    """
+    _log(f"Loading tokenizer from {BASE_MODEL}")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    _log("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+        token=hf_token,
+    )
+
+    _log(f"Loading LoRA adapter from {checkpoint}")
+    model = PeftModel.from_pretrained(base, checkpoint)
+    model.eval()
+    return model, tokenizer
+
+
+def compute_loss_and_generate(
+    model: Any,
+    tokenizer: Any,
+    examples: list[dict[str, Any]],
+) -> tuple[float, list[dict[str, Any]]]:
+    """Compute token-level NLL loss and generate greedy completions.
+
+    Loss is computed only over assistant tokens; system and user tokens
+    are masked with -100. Generation is greedy for reproducibility.
+
+    :param model: fine-tuned model (base + LoRA adapter).
+    :param tokenizer: tokenizer matching the base model.
+    :param examples: list of dataset examples from the test split.
+    :returns: (token_weighted_avg_loss, completions) tuple.
+    """
+    total_loss = 0.0
+    total_tokens = 0
+    completions: list[dict[str, Any]] = []
+
+    for i, example in enumerate(examples):
+        messages = example["messages"]
+        prompt_messages = messages[:-1]
+
+        full_ids = tokenizer.apply_chat_template(
+            messages,
+            tokenize=True,
+            return_tensors="pt",
+            add_generation_prompt=False,
+        ).to(model.device)
+
+        prompt_ids = tokenizer.apply_chat_template(
+            prompt_messages,
+            tokenize=True,
+            return_tensors="pt",
+            add_generation_prompt=True,
+        ).to(model.device)
+
+        prompt_len = prompt_ids.shape[1]
+        labels = full_ids.clone()
+        labels[0, :prompt_len] = -100
+
+        with torch.no_grad():
+            out = model(input_ids=full_ids, labels=labels)
+
+        n_tokens = (labels[0] != -100).sum().item()
+        total_loss += out.loss.item() * n_tokens
+        total_tokens += n_tokens
+
+        with torch.no_grad():
+            generated_ids = model.generate(
+                prompt_ids,
+                max_new_tokens=MAX_NEW_TOKENS,
+                do_sample=False,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+
+        new_token_ids = generated_ids[0, prompt_len:]
+        response = tokenizer.decode(new_token_ids, skip_special_tokens=True)
+
+        completions.append(
+            {
+                "messages": [
+                    {"role": m["role"], "content": m["content"]}
+                    for m in prompt_messages
+                ],
+                "generated_response": response,
+            }
+        )
+
+        if (i + 1) % 20 == 0:
+            _log(f"  {i + 1}/{len(examples)} examples processed")
+
+    eval_loss = total_loss / total_tokens if total_tokens > 0 else float("nan")
+    return eval_loss, completions
+
+
+def main() -> None:
+    """Parse arguments, run inference and loss computation, print JSON.
+
+    :returns: None
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--checkpoint",
+        required=True,
+        help="Path to epoch checkpoint directory",
+    )
+    args = parser.parse_args()
+
+    hf_token = (
+        os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
+    )
+
+    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
+
+    _log(f"Loading test split from {DATASET}")
+    ds = load_dataset(DATASET, split="test", token=hf_token)
+    examples = list(ds)
+    _log(f"Running inference on {len(examples)} test examples")
+
+    eval_loss, completions = compute_loss_and_generate(
+        model, tokenizer, examples
+    )
+
+    _log(f"eval_loss={eval_loss:.4f}  n_completions={len(completions)}")
+
+    print(json.dumps({"eval_loss": eval_loss, "completions": completions}))
+
+
+if __name__ == "__main__":
+    main()
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
new file mode 100644
index 0000000..e37c894
--- /dev/null
+++ b/sweep/master_config.yaml
@@ -0,0 +1,100 @@
+# =============================================================================
+# Master sweep configuration
+#
+# Two top-level sections:
+#   sweep:   defines the hyperparameter grid (read by sweep.py)
+#   axolotl: shared axolotl settings merged into every per-run config
+#
+# sweep.py expands the grid into per-run axolotl YAML files, injecting
+# per-run values (learning_rate, lora_r, lora_alpha, lora_target_modules,
+# micro_batch_size, gradient_accumulation_steps, output_dir, wandb_name)
+# on top of the axolotl section below.
+#
+# Epoch checkpoints are reused: each run trains to epoch 3 and saves a
+# checkpoint after each epoch. sweep.py runs inference + eval at each
+# checkpoint, yielding 3 data points per training run without re-training.
+# Grid: 4 ranks × 3 LRs × 2 batch sizes × 2 target layers = 48 training
+# runs → 144 data points.
+#
+# No validation during training. Test loss is computed post-training from
+# each epoch checkpoint and used for the NLL-style correlation analysis.
+# =============================================================================
+
+# --- Sweep grid -------------------------------------------------------------
+sweep:
+  learning_rate: [1.0e-4, 5.0e-4, 1.0e-3]
+  lora_r: [4, 8, 16, 32]
+  effective_batch_size: [4, 16]   # sweep.py fixes micro_batch_size=4 and
+                                   # derives gradient_accumulation_steps
+  target_layers:
+    - name: attention
+      modules: [q_proj, k_proj, v_proj, o_proj]
+    - name: attention_mlp
+      modules: [q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj]
+
+# lora_alpha is set to 2 * lora_r per run by sweep.py (not swept separately).
+
+# --- Shared axolotl settings ------------------------------------------------
+axolotl:
+
+  # Model
+  base_model: meta-llama/Llama-3.1-8B-Instruct
+
+  # Dataset — expects 'train' and 'test' splits only (no validation).
+  datasets:
+    - path: AccelerateScience/bush-dataset
+      type: chat_template
+      field_messages: messages
+      roles_to_train:
+        - assistant
+      train_on_eos: last
+
+  hf_use_auth_token: true
+
+  # Llama 3.1 Instruct ships with its own Jinja chat template in the
+  # tokenizer config; axolotl picks it up automatically.
+  chat_template: tokenizer_default
+  val_set_size: 0.0
+
+  # test_datasets is used only for the post-training loss pass (axolotl
+  # evaluate), not during training. Gives the single eval_loss per run
+  # used for the NLL-style correlation.
+  test_datasets:
+    - path: AccelerateScience/bush-dataset
+      split: test
+      type: chat_template
+      field_messages: messages
+
+  # Sequence
+  # Bush answers are long (~700-900 tokens including system + question).
+  # 2048 gives comfortable headroom without wasting VRAM.
+  sequence_len: 2048
+  sample_packing: true
+
+  # Adapter — lora_r, lora_alpha, lora_target_modules injected per run.
+  adapter: lora
+  lora_dropout: 0.0
+
+  # Precision & performance (H100)
+  bf16: true
+  tf32: true
+  flash_attention: true
+  gradient_checkpointing: true
+
+  # Optimiser
+  optimizer: adamw_torch_fused
+  lr_scheduler: cosine
+  warmup_ratio: 0.03
+  weight_decay: 0.0
+
+  # No evaluation during training — test loss computed post-training only.
+  eval_strategy: no
+
+  # Keep all epoch checkpoints (up to 3) so sweep.py can run inference at
+  # each epoch without re-training. 48 training runs × 3 epochs = 144 rows.
+  save_strategy: epoch
+  save_total_limit: 3
+
+  # W&B — wandb_name injected per run by sweep.py.
+  use_wandb: true
+  wandb_project: VOICE
diff --git a/sweep/sweep.py b/sweep/sweep.py
new file mode 100644
index 0000000..b89598e
--- /dev/null
+++ b/sweep/sweep.py
@@ -0,0 +1,357 @@
+"""Sweep orchestrator for the Llama 3.1 8B stylometric pilot.
+
+Reads master_config.yaml, expands the hyperparameter grid, and for each run:
+  1. Writes a per-run axolotl config YAML
+  2. Trains with axolotl (always 3 epochs, saving a checkpoint each epoch)
+  3. For each epoch checkpoint: runs infer.py to get completions + test loss
+  4. Appends 3 rows to outputs/results.parquet
+  5. Updates state.json for resumability
+
+Usage (inside tmux on the H100)::
+
+    python sweep.py
+    python sweep.py --dry-run   # print grid without training
+"""
+
+import argparse
+import itertools
+import json
+import logging
+import subprocess
+import sys
+from pathlib import Path
+from typing import Any, cast
+
+import pandas as pd
+import yaml
+
+# ---------------------------------------------------------------------------
+# Paths
+# ---------------------------------------------------------------------------
+SWEEP_DIR = Path(__file__).parent
+CONFIGS_DIR = SWEEP_DIR / "configs"
+CHECKPOINTS_DIR = SWEEP_DIR / "checkpoints"
+OUTPUTS_DIR = SWEEP_DIR / "outputs"
+STATE_FILE = SWEEP_DIR / "state.json"
+MASTER_CONFIG = SWEEP_DIR / "master_config.yaml"
+LOG_FILE = SWEEP_DIR / "sweep.log"
+
+# Fixed micro batch size; gradient_accumulation_steps derived from
+# the effective_batch_size sweep axis.
+MICRO_BATCH_SIZE: int = 4
+
+log = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Logging
+# ---------------------------------------------------------------------------
+def setup_logging() -> None:
+    """Configure logging to both stdout and sweep.log.
+
+    :returns: None
+    """
+    fmt = "%(asctime)s [%(levelname)s] %(message)s"
+    logging.basicConfig(
+        level=logging.INFO,
+        format=fmt,
+        handlers=[
+            logging.StreamHandler(sys.stdout),
+            logging.FileHandler(LOG_FILE),
+        ],
+    )
+
+
+# ---------------------------------------------------------------------------
+# Grid expansion
+# ---------------------------------------------------------------------------
+def expand_grid(sweep_cfg: dict[str, Any]) -> list[dict[str, Any]]:
+    """Expand the sweep section into a flat list of per-run configs.
+
+    :param sweep_cfg: the ``sweep`` section of master_config.yaml.
+    :returns: list of run config dicts, one per hyperparameter combination.
+    """
+    runs: list[dict[str, Any]] = []
+    combos = itertools.product(
+        sweep_cfg["learning_rate"],
+        sweep_cfg["lora_r"],
+        sweep_cfg["effective_batch_size"],
+        sweep_cfg["target_layers"],
+    )
+    for idx, (lr, rank, bs, tl) in enumerate(combos):
+        lr_str = f"{lr:.0e}".replace("e-0", "e-").replace("e+0", "e")
+        run_name = f"run_{idx:03d}_lr{lr_str}_r{rank}_bs{bs}_{tl['name']}"
+        runs.append(
+            {
+                "run_idx": idx,
+                "run_name": run_name,
+                "learning_rate": lr,
+                "lora_r": rank,
+                "lora_alpha": rank,
+                "effective_batch_size": bs,
+                "target_layers_name": tl["name"],
+                "lora_target_modules": tl["modules"],
+                "micro_batch_size": MICRO_BATCH_SIZE,
+                "gradient_accumulation_steps": bs // MICRO_BATCH_SIZE,
+            }
+        )
+    return runs
+
+
+# ---------------------------------------------------------------------------
+# State management
+# ---------------------------------------------------------------------------
+def load_state() -> dict[str, Any]:
+    """Load sweep state from state.json, or return a fresh state dict.
+
+    :returns: state dict with ``completed`` and ``failed`` keys.
+    """
+    if STATE_FILE.exists():
+        return cast(dict[str, Any], json.loads(STATE_FILE.read_text()))
+    return {"completed": [], "failed": {}}
+
+
+def save_state(state: dict[str, Any]) -> None:
+    """Persist sweep state to state.json.
+
+    :param state: state dict to serialise.
+    :returns: None
+    """
+    STATE_FILE.write_text(json.dumps(state, indent=2))
+
+
+# ---------------------------------------------------------------------------
+# Per-run axolotl config
+# ---------------------------------------------------------------------------
+def write_run_config(
+    run: dict[str, Any],
+    axolotl_base: dict[str, Any],
+) -> Path:
+    """Merge shared axolotl settings with per-run values and write a YAML.
+
+    :param run: per-run hyperparameter dict from expand_grid.
+    :param axolotl_base: shared axolotl settings from master_config.yaml.
+    :returns: path to the written per-run config YAML.
+    """
+    config = dict(axolotl_base)
+    config.update(
+        {
+            "learning_rate": run["learning_rate"],
+            "lora_r": run["lora_r"],
+            "lora_alpha": run["lora_alpha"],
+            "lora_target_modules": run["lora_target_modules"],
+            "num_epochs": 3,
+            "micro_batch_size": run["micro_batch_size"],
+            "gradient_accumulation_steps": (
+                run["gradient_accumulation_steps"]
+            ),
+            "output_dir": str(CHECKPOINTS_DIR / run["run_name"]),
+            "wandb_name": run["run_name"],
+        }
+    )
+    config_path = CONFIGS_DIR / f"{run['run_name']}.yaml"
+    with open(config_path, "w") as f:
+        yaml.dump(config, f, default_flow_style=False, sort_keys=False)
+    return config_path
+
+
+# ---------------------------------------------------------------------------
+# Checkpoint discovery
+# ---------------------------------------------------------------------------
+def find_epoch_checkpoints(run_name: str) -> dict[int, Path]:
+    """Map epoch numbers to checkpoint paths via trainer_state.json.
+
+    :param run_name: name of the run, used to locate the checkpoint dir.
+    :returns: dict mapping epoch int to the corresponding checkpoint Path.
+    """
+    checkpoint_dir = CHECKPOINTS_DIR / run_name
+    epoch_map: dict[int, Path] = {}
+    for ckpt in sorted(checkpoint_dir.glob("checkpoint-*")):
+        state_file = ckpt / "trainer_state.json"
+        if not state_file.exists():
+            continue
+        trainer_state = json.loads(state_file.read_text())
+        epoch = round(trainer_state["epoch"])
+        epoch_map[epoch] = ckpt
+    return epoch_map
+
+
+# ---------------------------------------------------------------------------
+# Training
+# ---------------------------------------------------------------------------
+def run_training(config_path: Path) -> None:
+    """Launch axolotl training for the given config.
+
+    :param config_path: path to the per-run axolotl YAML config.
+    :returns: None
+    """
+    log.info(f"Training: axolotl train {config_path.name}")
+    subprocess.run(
+        ["python", "-m", "axolotl.cli.train", str(config_path)],
+        check=True,
+    )
+
+
+# ---------------------------------------------------------------------------
+# Inference + loss
+# ---------------------------------------------------------------------------
+def run_inference(checkpoint_path: Path) -> dict[str, Any]:
+    """Run infer.py for a checkpoint and return eval_loss + completions.
+
+    :param checkpoint_path: path to the epoch checkpoint directory.
+    :returns: dict with ``eval_loss`` (float) and ``completions`` (list).
+    """
+    proc = subprocess.run(
+        [
+            sys.executable,
+            str(SWEEP_DIR / "infer.py"),
+            "--checkpoint",
+            str(checkpoint_path),
+        ],
+        capture_output=True,
+        text=True,
+        check=True,
+    )
+    if proc.stderr:
+        for line in proc.stderr.strip().splitlines():
+            log.info(f"  [infer] {line}")
+    return cast(dict[str, Any], json.loads(proc.stdout))
+
+
+# ---------------------------------------------------------------------------
+# Output
+# ---------------------------------------------------------------------------
+def append_results(rows: list[dict[str, Any]]) -> None:
+    """Atomically append rows to outputs/results.parquet.
+
+    :param rows: list of result row dicts to append.
+    :returns: None
+    """
+    out_path = OUTPUTS_DIR / "results.parquet"
+    new_df = pd.DataFrame(rows)
+    if out_path.exists():
+        existing = pd.read_parquet(out_path)
+        new_df = pd.concat([existing, new_df], ignore_index=True)
+    tmp = out_path.with_suffix(".tmp.parquet")
+    new_df.to_parquet(tmp, index=False)
+    tmp.rename(out_path)
+
+
+# ---------------------------------------------------------------------------
+# Per-run helpers (private — extracted to keep main() complexity low)
+# ---------------------------------------------------------------------------
+def _collect_rows(
+    run: dict[str, Any],
+    epoch_map: dict[int, Path],
+) -> list[dict[str, Any]]:
+    rows: list[dict[str, Any]] = []
+    for epoch in sorted(epoch_map):
+        ckpt_path = epoch_map[epoch]
+        log.info(f"  [INFER] epoch {epoch} — {ckpt_path.name}")
+        result = run_inference(ckpt_path)
+        loss_val = result["eval_loss"]
+        n_comp = len(result["completions"])
+        rows.append(
+            {
+                "run_name": run["run_name"],
+                "epoch": epoch,
+                "learning_rate": run["learning_rate"],
+                "lora_r": run["lora_r"],
+                "lora_alpha": run["lora_alpha"],
+                "effective_batch_size": run["effective_batch_size"],
+                "target_layers": run["target_layers_name"],
+                "eval_loss": loss_val,
+                "completions": json.dumps(result["completions"]),
+            }
+        )
+        log.info(f"    eval_loss={loss_val:.4f}  n_completions={n_comp}")
+    return rows
+
+
+def _execute_run(
+    run: dict[str, Any],
+    axolotl_base: dict[str, Any],
+    state: dict[str, Any],
+) -> None:
+    name = run["run_name"]
+    try:
+        config_path = write_run_config(run, axolotl_base)
+        run_training(config_path)
+        epoch_map = find_epoch_checkpoints(name)
+        if not epoch_map:
+            raise RuntimeError("No epoch checkpoints found after training")
+        log.info(f"Found checkpoints for epochs: {sorted(epoch_map)}")
+        rows = _collect_rows(run, epoch_map)
+        append_results(rows)
+        state["completed"].append(name)
+        state["failed"].pop(name, None)
+        save_state(state)
+        log.info(f"[DONE] {name}")
+    except Exception as exc:  # noqa: BLE001
+        log.error(f"[FAIL] {name}: {exc}")
+        state["failed"][name] = str(exc)
+        save_state(state)
+
+
+# ---------------------------------------------------------------------------
+# Main
+# ---------------------------------------------------------------------------
+def main() -> None:
+    """Parse arguments, expand the grid, and run the sweep.
+
+    :returns: None
+    """
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--dry-run",
+        action="store_true",
+        help="Print the full grid without training",
+    )
+    args = parser.parse_args()
+
+    setup_logging()
+
+    CONFIGS_DIR.mkdir(exist_ok=True)
+    CHECKPOINTS_DIR.mkdir(exist_ok=True)
+    OUTPUTS_DIR.mkdir(exist_ok=True)
+
+    master = yaml.safe_load(MASTER_CONFIG.read_text())
+    runs = expand_grid(master["sweep"])
+    axolotl_base = master["axolotl"]
+
+    n_runs = len(runs)
+    log.info(f"Grid: {n_runs} training runs -> {n_runs * 3} data points")
+
+    if args.dry_run:
+        for r in runs:
+            log.info(
+                f"  {r['run_name']}  lr={r['learning_rate']}"
+                f"  r={r['lora_r']}  bs={r['effective_batch_size']}"
+                f"  layers={r['target_layers_name']}"
+            )
+        return
+
+    state = load_state()
+    log.info(
+        f"Resuming: {len(state['completed'])} completed, "
+        f"{len(state['failed'])} failed"
+    )
+
+    for run in runs:
+        name = run["run_name"]
+        if name in state["completed"]:
+            log.info(f"[SKIP] {name}")
+            continue
+        log.info(f"\n{'=' * 60}")
+        log.info(f"[START] {name}")
+        log.info(f"{'=' * 60}")
+        _execute_run(run, axolotl_base, state)
+
+    n_done = len(state["completed"])
+    log.info(f"\nSweep complete: {n_done}/{n_runs} runs succeeded.")
+    if state["failed"]:
+        log.warning(f"Failed runs: {list(state['failed'])}")
+
+
+if __name__ == "__main__":
+    main()

From 530f45f1374eff869e21fb840b2e63eeb45fa124 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 10 Apr 2026 15:20:34 +0100
Subject: [PATCH 02/68] feat: add README

---
 cspell/library-words.txt |  2 ++
 sweep/README.md          | 76 ++++++++++++++++++++++++++++++++++++++++
 2 files changed, 78 insertions(+)
 create mode 100644 sweep/README.md

diff --git a/cspell/library-words.txt b/cspell/library-words.txt
index 70c464c..ed50a9b 100644
--- a/cspell/library-words.txt
+++ b/cspell/library-words.txt
@@ -15,3 +15,5 @@ jinja
 adamw
 checkpointing
 proc
+peft
+pyyaml
diff --git a/sweep/README.md b/sweep/README.md
new file mode 100644
index 0000000..55ec0c4
--- /dev/null
+++ b/sweep/README.md
@@ -0,0 +1,76 @@
+# Llama 3.1 8B Stylometric Pilot Sweep
+
+48 training runs × 3 epoch checkpoints = 144 data points across a grid of
+LoRA rank, learning rate, batch size, and target layer configurations.
+
+## Setup on the H100
+
+**1. Clone the repo and install dependencies**
+
+```bash
+git clone <repo-url> && cd voice
+pip install axolotl accelerate peft transformers datasets pandas pyyaml python-dotenv wandb
+```
+
+**2. Set environment variables**
+
+Create a `.env` file in the `sweep/` directory (or export directly):
+
+```bash
+HF_TOKEN=hf_...
+WANDB_API_KEY=...
+```
+
+**3. Log in to Hugging Face and W&B**
+
+```bash
+huggingface-cli login
+wandb login
+```
+
+**4. Verify the grid looks correct**
+
+```bash
+cd sweep
+python sweep.py --dry-run
+```
+
+## Running the sweep
+
+Start a tmux session so the sweep survives SSH disconnection:
+
+```bash
+tmux new -s sweep
+cd sweep
+python sweep.py
+# Ctrl+B, D  →  detach (safe to close laptop)
+# tmux attach -t sweep  →  reconnect later
+```
+
+Progress is logged to `sweep.log` and to the W&B project `VOICE`.
+
+## Resuming after failure
+
+`state.json` tracks completed and failed runs. Simply re-run `sweep.py` and
+it will skip completed runs and retry failed ones:
+
+```bash
+python sweep.py
+```
+
+## Outputs
+
+Results are written to `outputs/results.parquet` after each training run
+completes. Schema mirrors `pilot_results.parquet` used in the notebooks:
+
+| Column | Description |
+|---|---|
+| `run_name` | Unique run identifier |
+| `epoch` | Epoch number (1, 2, or 3) |
+| `learning_rate` | LoRA learning rate |
+| `lora_r` | LoRA rank |
+| `lora_alpha` | LoRA alpha (= rank) |
+| `effective_batch_size` | Effective batch size |
+| `target_layers` | `attention` or `attention_mlp` |
+| `eval_loss` | Test NLL loss at this checkpoint |
+| `completions` | JSON list of generated responses on the test split |

From 1b75325e2eeacd4e776a8300e96dc6fe06d3d4e2 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 10 Apr 2026 15:44:37 +0100
Subject: [PATCH 03/68] chore: update README to select single gpu

---
 sweep/README.md | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sweep/README.md b/sweep/README.md
index 55ec0c4..92002df 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -32,7 +32,7 @@ wandb login
 
 ```bash
 cd sweep
-python sweep.py --dry-run
+CUDA_VISIBLE_DEVICES=3 python sweep.py --dry-run
 ```
 
 ## Running the sweep
@@ -42,7 +42,7 @@ Start a tmux session so the sweep survives SSH disconnection:
 ```bash
 tmux new -s sweep
 cd sweep
-python sweep.py
+CUDA_VISIBLE_DEVICES=3 python sweep.py
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
@@ -55,7 +55,7 @@ Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 it will skip completed runs and retry failed ones:
 
 ```bash
-python sweep.py
+CUDA_VISIBLE_DEVICES=3 python sweep.py
 ```
 
 ## Outputs

From dfa44e84660e8efc0be3f4aed49910d4f064a8d0 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:00:53 +0100
Subject: [PATCH 04/68] chore: migrate all sweep deps to uv

---
 pyproject.toml  |   12 +-
 sweep/README.md |   14 +-
 sweep/sweep.py  |   15 +-
 uv.lock         | 3275 ++++++++++++++++++++++++++++++++++-------------
 4 files changed, 2398 insertions(+), 918 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index daff727..0d3cad6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,17 +3,27 @@ name = "voice"
 version = "0.1.0"
 description = "Fine-tuning for stylistic fidelity"
 readme = "README.md"
-requires-python = ">=3.12"
+requires-python = ">=3.12,<3.14"
 dependencies = [
+    "accelerate",
+    "axolotl>=0.16.1 ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
     "huggingface-hub>=1.4.1",
     "matplotlib>=3.10.8",
+    "pandas",
+    "peft",
+    "pyyaml>=6.0.3",
     "scipy>=1.17.0",
     "seaborn>=0.13.2",
     "sentence-transformers>=5.3.0",
+    "transformers",
+    "wandb>=0.25.1",
 ]
 
+[tool.uv]
+required-environments = ["sys_platform == 'linux'"]
+
 [build-system]
 requires = ["setuptools>=68", "wheel"]
 build-backend = "setuptools.build_meta"
diff --git a/sweep/README.md b/sweep/README.md
index 92002df..e74af0e 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -9,7 +9,7 @@ LoRA rank, learning rate, batch size, and target layer configurations.
 
 ```bash
 git clone <repo-url> && cd voice
-pip install axolotl accelerate peft transformers datasets pandas pyyaml python-dotenv wandb
+uv sync
 ```
 
 **2. Set environment variables**
@@ -24,15 +24,15 @@ WANDB_API_KEY=...
 **3. Log in to Hugging Face and W&B**
 
 ```bash
-huggingface-cli login
-wandb login
+uv run huggingface-cli login
+uv run wandb login
 ```
 
 **4. Verify the grid looks correct**
 
 ```bash
 cd sweep
-CUDA_VISIBLE_DEVICES=3 python sweep.py --dry-run
+uv run python sweep.py --dry-run
 ```
 
 ## Running the sweep
@@ -42,7 +42,9 @@ Start a tmux session so the sweep survives SSH disconnection:
 ```bash
 tmux new -s sweep
 cd sweep
-CUDA_VISIBLE_DEVICES=3 python sweep.py
+uv run python sweep.py
+# Override GPU if needed (default is device 3):
+# uv run python sweep.py --device 0
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
@@ -55,7 +57,7 @@ Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 it will skip completed runs and retry failed ones:
 
 ```bash
-CUDA_VISIBLE_DEVICES=3 python sweep.py
+uv run python sweep.py
 ```
 
 ## Outputs
diff --git a/sweep/sweep.py b/sweep/sweep.py
index b89598e..df5052d 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -9,14 +9,16 @@
 
 Usage (inside tmux on the H100)::
 
-    python sweep.py
-    python sweep.py --dry-run   # print grid without training
+    uv run python sweep.py
+    uv run python sweep.py --dry-run        # print grid without training
+    uv run python sweep.py --device 0       # override GPU (default: 3)
 """
 
 import argparse
 import itertools
 import json
 import logging
+import os
 import subprocess
 import sys
 from pathlib import Path
@@ -187,7 +189,7 @@ def run_training(config_path: Path) -> None:
     """
     log.info(f"Training: axolotl train {config_path.name}")
     subprocess.run(
-        ["python", "-m", "axolotl.cli.train", str(config_path)],
+        [sys.executable, "-m", "axolotl.cli.train", str(config_path)],
         check=True,
     )
 
@@ -307,8 +309,15 @@ def main() -> None:
         action="store_true",
         help="Print the full grid without training",
     )
+    parser.add_argument(
+        "--device",
+        default="3",
+        help="CUDA device index (default: 3)",
+    )
     args = parser.parse_args()
 
+    os.environ.setdefault("CUDA_VISIBLE_DEVICES", args.device)
+
     setup_logging()
 
     CONFIGS_DIR.mkdir(exist_ok=True)
diff --git a/uv.lock b/uv.lock
index dd1c64b..ce89714 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,13 +1,87 @@
 version = 1
 revision = 3
-requires-python = ">=3.12"
+requires-python = ">=3.12, <3.14"
 resolution-markers = [
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-    "python_full_version < '3.14' and sys_platform == 'emscripten'",
-    "python_full_version < '3.14' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'win32'",
+    "python_full_version >= '3.13' and sys_platform == 'emscripten'",
+    "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'win32'",
+    "python_full_version < '3.13' and sys_platform == 'emscripten'",
+    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+]
+required-markers = [
+    "sys_platform == 'linux'",
+]
+
+[[package]]
+name = "absl-py"
+version = "2.4.0"
+source = { registry = "https://pypi.org/simple" }
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From c411fdb02abe92cbff63700ce90d062a39cdebb6 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:08:24 +0100
Subject: [PATCH 05/68] chore: add hf cli

---
 sweep/README.md | 15 ++++++++-------
 sweep/sweep.py  |  6 +++---
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/sweep/README.md b/sweep/README.md
index e74af0e..fb7fd54 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -5,11 +5,12 @@ LoRA rank, learning rate, batch size, and target layer configurations.
 
 ## Setup on the H100
 
-**1. Clone the repo and install dependencies**
+**1. Clone the repo, install dependencies, and activate the venv**
 
 ```bash
 git clone <repo-url> && cd voice
 uv sync
+source .venv/bin/activate
 ```
 
 **2. Set environment variables**
@@ -24,15 +25,15 @@ WANDB_API_KEY=...
 **3. Log in to Hugging Face and W&B**
 
 ```bash
-uv run huggingface-cli login
-uv run wandb login
+huggingface-cli login
+wandb login
 ```
 
 **4. Verify the grid looks correct**
 
 ```bash
 cd sweep
-uv run python sweep.py --dry-run
+python sweep.py --dry-run
 ```
 
 ## Running the sweep
@@ -42,9 +43,9 @@ Start a tmux session so the sweep survives SSH disconnection:
 ```bash
 tmux new -s sweep
 cd sweep
-uv run python sweep.py
+python sweep.py
 # Override GPU if needed (default is device 3):
-# uv run python sweep.py --device 0
+# python sweep.py --device 0
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
@@ -57,7 +58,7 @@ Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 it will skip completed runs and retry failed ones:
 
 ```bash
-uv run python sweep.py
+python sweep.py
 ```
 
 ## Outputs
diff --git a/sweep/sweep.py b/sweep/sweep.py
index df5052d..6a81a9b 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -9,9 +9,9 @@
 
 Usage (inside tmux on the H100)::
 
-    uv run python sweep.py
-    uv run python sweep.py --dry-run        # print grid without training
-    uv run python sweep.py --device 0       # override GPU (default: 3)
+    python sweep.py
+    python sweep.py --dry-run   # print grid without training
+    python sweep.py --device 0  # override GPU (default: 3)
 """
 
 import argparse

From 7df07a6f7e4aa29fbdfbbf1a7da38705dfe1b42c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:15:32 +0100
Subject: [PATCH 06/68] chore: update README.md

---
 sweep/README.md | 17 +++++------------
 1 file changed, 5 insertions(+), 12 deletions(-)

diff --git a/sweep/README.md b/sweep/README.md
index fb7fd54..6653835 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -13,20 +13,13 @@ uv sync
 source .venv/bin/activate
 ```
 
-**2. Set environment variables**
-
-Create a `.env` file in the `sweep/` directory (or export directly):
-
-```bash
-HF_TOKEN=hf_...
-WANDB_API_KEY=...
-```
-
-**3. Log in to Hugging Face and W&B**
+**2. Set environment variables and log in**
 
 ```bash
-huggingface-cli login
-wandb login
+export HF_TOKEN=hf_...
+export WANDB_API_KEY=...
+hf auth login --token $HF_TOKEN
+wandb login $WANDB_API_KEY
 ```
 
 **4. Verify the grid looks correct**

From 61e88f28f0ee75fb0ff5fed451be979bb1b7cf01 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:28:40 +0100
Subject: [PATCH 07/68] chore: add typer as dep

---
 pyproject.toml | 1 +
 uv.lock        | 2 ++
 2 files changed, 3 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 0d3cad6..ebe6a78 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -10,6 +10,7 @@ dependencies = [
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
     "huggingface-hub>=1.4.1",
+    "typer>=0.9.0",
     "matplotlib>=3.10.8",
     "pandas",
     "peft",
diff --git a/uv.lock b/uv.lock
index ce89714..5eabfe7 100644
--- a/uv.lock
+++ b/uv.lock
@@ -4980,6 +4980,7 @@ dependencies = [
     { name = "seaborn" },
     { name = "sentence-transformers" },
     { name = "transformers" },
+    { name = "typer" },
     { name = "wandb" },
 ]
 
@@ -5013,6 +5014,7 @@ requires-dist = [
     { name = "seaborn", specifier = ">=0.13.2" },
     { name = "sentence-transformers", specifier = ">=5.3.0" },
     { name = "transformers" },
+    { name = "typer", specifier = ">=0.9.0" },
     { name = "wandb", specifier = ">=0.25.1" },
 ]
 

From 2aef161dab1db5fd1a4061b08f385058a72cff17 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:42:44 +0100
Subject: [PATCH 08/68] chore: update axolotl dep to download from source

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index ebe6a78..eab2a8b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12,<3.14"
 dependencies = [
     "accelerate",
-    "axolotl>=0.16.1 ; sys_platform == 'linux'",
+    "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@v0.16.1 ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
     "huggingface-hub>=1.4.1",

From 14876841b010c64b0f3cf3e87b805fcffd52b392 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 09:48:55 +0100
Subject: [PATCH 09/68] chore: update eval strategy to be a string rather than
 reserved boolean

---
 sweep/master_config.yaml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index e37c894..33b9c7c 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -88,7 +88,7 @@ axolotl:
   weight_decay: 0.0
 
   # No evaluation during training — test loss computed post-training only.
-  eval_strategy: no
+  eval_strategy: "no"
 
   # Keep all epoch checkpoints (up to 3) so sweep.py can run inference at
   # each epoch without re-training. 48 training runs × 3 epochs = 144 rows.

From 38ac1292e9370b2188fab88e65e73f3d020b2619 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:00:28 +0100
Subject: [PATCH 10/68] fix: update chat template during inference

---
 sweep/infer.py | 12 +++++++++---
 1 file changed, 9 insertions(+), 3 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 2c5f09e..0eea8f6 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -55,7 +55,7 @@ def load_model_and_tokenizer(
     _log("Loading base model")
     base = AutoModelForCausalLM.from_pretrained(
         BASE_MODEL,
-        torch_dtype=torch.bfloat16,
+        dtype=torch.bfloat16,
         device_map="auto",
         token=hf_token,
     )
@@ -94,14 +94,20 @@ def compute_loss_and_generate(
             tokenize=True,
             return_tensors="pt",
             add_generation_prompt=False,
-        ).to(model.device)
+        )
+        if hasattr(full_ids, "input_ids"):
+            full_ids = full_ids.input_ids
+        full_ids = full_ids.to(model.device)
 
         prompt_ids = tokenizer.apply_chat_template(
             prompt_messages,
             tokenize=True,
             return_tensors="pt",
             add_generation_prompt=True,
-        ).to(model.device)
+        )
+        if hasattr(prompt_ids, "input_ids"):
+            prompt_ids = prompt_ids.input_ids
+        prompt_ids = prompt_ids.to(model.device)
 
         prompt_len = prompt_ids.shape[1]
         labels = full_ids.clone()

From 87d23a8cc617b7608dfca2276f7cd97880c66cd4 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:06:34 +0100
Subject: [PATCH 11/68] fix: update infer.py to manually compute loss

---
 sweep/infer.py | 19 +++++++++++++++----
 1 file changed, 15 insertions(+), 4 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 0eea8f6..9c04788 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -114,10 +114,21 @@ def compute_loss_and_generate(
         labels[0, :prompt_len] = -100
 
         with torch.no_grad():
-            out = model(input_ids=full_ids, labels=labels)
-
-        n_tokens = (labels[0] != -100).sum().item()
-        total_loss += out.loss.item() * n_tokens
+            logits = model(input_ids=full_ids).logits
+
+        # Shift to align predictions with targets (same as transformers internals)
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous().view(-1)
+        valid = shift_labels != -100
+        n_tokens = valid.sum().item()
+        if n_tokens > 0:
+            loss_val = torch.nn.functional.cross_entropy(
+                shift_logits.view(-1, shift_logits.size(-1))[valid],
+                shift_labels[valid],
+            ).item()
+        else:
+            loss_val = float("nan")
+        total_loss += loss_val * n_tokens
         total_tokens += n_tokens
 
         with torch.no_grad():

From 4caf6a971c03a8106325cb4b9de350dbf0bfdfc5 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:18:31 +0100
Subject: [PATCH 12/68] fix: force inference to pin to same GPU as training

---
 sweep/sweep.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sweep/sweep.py b/sweep/sweep.py
index 6a81a9b..3138c58 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -316,7 +316,7 @@ def main() -> None:
     )
     args = parser.parse_args()
 
-    os.environ.setdefault("CUDA_VISIBLE_DEVICES", args.device)
+    os.environ["CUDA_VISIBLE_DEVICES"] = args.device
 
     setup_logging()
 

From 4c9122d31d13b859d542b85a372d8796dbd61f38 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:34:45 +0100
Subject: [PATCH 13/68] chore: add vllm

---
 pyproject.toml |    1 +
 sweep/infer.py |  224 +++++-----
 uv.lock        | 1150 +++++++++++++++++++++++++++++++++++++++++-------
 3 files changed, 1114 insertions(+), 261 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index eab2a8b..cd8cc3e 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,6 +20,7 @@ dependencies = [
     "sentence-transformers>=5.3.0",
     "transformers",
     "wandb>=0.25.1",
+    "vllm>=0.11.0 ; sys_platform == 'linux'",
 ]
 
 [tool.uv]
diff --git a/sweep/infer.py b/sweep/infer.py
index 9c04788..3531e83 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,7 +1,8 @@
 """Inference and test-loss script for a single fine-tuned checkpoint.
 
-Loads the base model with LoRA adapter, runs greedy generation on the Bush
-test split, and computes token-weighted NLL loss over assistant turns only.
+Uses vLLM for batched generation and prompt logprobs for NLL loss.
+All 200 test examples are processed in two bulk passes (generation + loss),
+making this dramatically faster than sequential per-example inference.
 
 Outputs a single JSON object to stdout::
 
@@ -20,147 +21,100 @@
 import sys
 from typing import Any
 
-import torch
 from datasets import load_dataset
 from dotenv import load_dotenv
-from peft import PeftModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
 
 load_dotenv()
 
 BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
 DATASET = "AccelerateScience/bush-dataset"
 MAX_NEW_TOKENS = 512
+# Must be >= the largest lora_r in the sweep grid (32).
+MAX_LORA_RANK = 32
 
 
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr, flush=True)
 
 
-def load_model_and_tokenizer(
-    checkpoint: str,
-    hf_token: str,
-) -> tuple[Any, Any]:
-    """Load the base model with LoRA adapter from a checkpoint directory.
-
-    :param checkpoint: path to the epoch checkpoint directory.
-    :param hf_token: HuggingFace API token for private model access.
-    :returns: (model, tokenizer) tuple ready for inference.
-    """
-    _log(f"Loading tokenizer from {BASE_MODEL}")
-    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.eos_token
-
-    _log("Loading base model")
-    base = AutoModelForCausalLM.from_pretrained(
-        BASE_MODEL,
-        dtype=torch.bfloat16,
-        device_map="auto",
-        token=hf_token,
-    )
-
-    _log(f"Loading LoRA adapter from {checkpoint}")
-    model = PeftModel.from_pretrained(base, checkpoint)
-    model.eval()
-    return model, tokenizer
-
-
-def compute_loss_and_generate(
-    model: Any,
-    tokenizer: Any,
+def build_token_ids(
     examples: list[dict[str, Any]],
-) -> tuple[float, list[dict[str, Any]]]:
-    """Compute token-level NLL loss and generate greedy completions.
-
-    Loss is computed only over assistant tokens; system and user tokens
-    are masked with -100. Generation is greedy for reproducibility.
+    tokenizer: Any,
+) -> tuple[list[list[int]], list[list[int]], list[int]]:
+    """Tokenize all examples into full and prompt token ID lists.
 
-    :param model: fine-tuned model (base + LoRA adapter).
-    :param tokenizer: tokenizer matching the base model.
     :param examples: list of dataset examples from the test split.
-    :returns: (token_weighted_avg_loss, completions) tuple.
+    :param tokenizer: tokenizer matching the base model.
+    :returns: (full_token_ids, prompt_token_ids, prompt_lens) where
+        full_token_ids includes the assistant completion and
+        prompt_token_ids ends with the generation prompt.
     """
-    total_loss = 0.0
-    total_tokens = 0
-    completions: list[dict[str, Any]] = []
+    full_token_ids: list[list[int]] = []
+    prompt_token_ids: list[list[int]] = []
+    prompt_lens: list[int] = []
 
-    for i, example in enumerate(examples):
-        messages = example["messages"]
+    for example in examples:
+        messages: list[dict[str, Any]] = example["messages"]
         prompt_messages = messages[:-1]
 
         full_ids = tokenizer.apply_chat_template(
             messages,
             tokenize=True,
-            return_tensors="pt",
             add_generation_prompt=False,
         )
         if hasattr(full_ids, "input_ids"):
-            full_ids = full_ids.input_ids
-        full_ids = full_ids.to(model.device)
+            full_ids = list(full_ids.input_ids[0])
 
-        prompt_ids = tokenizer.apply_chat_template(
+        p_ids = tokenizer.apply_chat_template(
             prompt_messages,
             tokenize=True,
-            return_tensors="pt",
             add_generation_prompt=True,
         )
-        if hasattr(prompt_ids, "input_ids"):
-            prompt_ids = prompt_ids.input_ids
-        prompt_ids = prompt_ids.to(model.device)
-
-        prompt_len = prompt_ids.shape[1]
-        labels = full_ids.clone()
-        labels[0, :prompt_len] = -100
-
-        with torch.no_grad():
-            logits = model(input_ids=full_ids).logits
-
-        # Shift to align predictions with targets (same as transformers internals)
-        shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous().view(-1)
-        valid = shift_labels != -100
-        n_tokens = valid.sum().item()
-        if n_tokens > 0:
-            loss_val = torch.nn.functional.cross_entropy(
-                shift_logits.view(-1, shift_logits.size(-1))[valid],
-                shift_labels[valid],
-            ).item()
-        else:
-            loss_val = float("nan")
-        total_loss += loss_val * n_tokens
-        total_tokens += n_tokens
-
-        with torch.no_grad():
-            generated_ids = model.generate(
-                prompt_ids,
-                max_new_tokens=MAX_NEW_TOKENS,
-                do_sample=False,
-                pad_token_id=tokenizer.eos_token_id,
-            )
-
-        new_token_ids = generated_ids[0, prompt_len:]
-        response = tokenizer.decode(new_token_ids, skip_special_tokens=True)
+        if hasattr(p_ids, "input_ids"):
+            p_ids = list(p_ids.input_ids[0])
+
+        full_token_ids.append(list(full_ids))
+        prompt_token_ids.append(list(p_ids))
+        prompt_lens.append(len(p_ids))
+
+    return full_token_ids, prompt_token_ids, prompt_lens
 
-        completions.append(
-            {
-                "messages": [
-                    {"role": m["role"], "content": m["content"]}
-                    for m in prompt_messages
-                ],
-                "generated_response": response,
-            }
-        )
 
-        if (i + 1) % 20 == 0:
-            _log(f"  {i + 1}/{len(examples)} examples processed")
+def compute_nll(
+    loss_outputs: list[Any],
+    full_token_ids: list[list[int]],
+    prompt_lens: list[int],
+) -> float:
+    """Compute token-weighted NLL loss from vLLM prompt logprobs.
 
-    eval_loss = total_loss / total_tokens if total_tokens > 0 else float("nan")
-    return eval_loss, completions
+    :param loss_outputs: vLLM RequestOutput list from the loss pass.
+    :param full_token_ids: tokenised full sequences (prompt + completion).
+    :param prompt_lens: length of the prompt portion for each example.
+    :returns: token-weighted average NLL loss across all examples.
+    """
+    total_loss = 0.0
+    total_tokens = 0
+
+    for i, out in enumerate(loss_outputs):
+        pl = out.prompt_logprobs  # List[Optional[Dict[int, Logprob]]]
+        full_ids = full_token_ids[i]
+        p_len = prompt_lens[i]
+
+        for j in range(p_len, len(full_ids)):
+            if pl is None or j >= len(pl) or pl[j] is None:
+                continue
+            token_id = full_ids[j]
+            if token_id in pl[j]:
+                total_loss += -pl[j][token_id].logprob
+                total_tokens += 1
+
+    return total_loss / total_tokens if total_tokens > 0 else float("nan")
 
 
 def main() -> None:
-    """Parse arguments, run inference and loss computation, print JSON.
+    """Parse arguments, run batched inference and loss, print JSON.
 
     :returns: None
     """
@@ -175,20 +129,68 @@ def main() -> None:
     hf_token = (
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
-
-    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
+    if hf_token:
+        os.environ["HF_TOKEN"] = hf_token
 
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
     examples = list(ds)
-    _log(f"Running inference on {len(examples)} test examples")
+    _log(f"{len(examples)} test examples")
+
+    _log("Initialising vLLM engine")
+    llm = LLM(
+        model=BASE_MODEL,
+        enable_lora=True,
+        max_model_len=2048,
+        dtype="bfloat16",
+        gpu_memory_utilization=0.9,
+        max_lora_rank=MAX_LORA_RANK,
+    )
+    lora_request = LoRARequest("adapter", 1, args.checkpoint)
+    tokenizer = llm.get_tokenizer()
 
-    eval_loss, completions = compute_loss_and_generate(
-        model, tokenizer, examples
+    _log("Tokenising examples")
+    full_token_ids, prompt_token_ids, prompt_lens = build_token_ids(
+        examples, tokenizer
     )
 
-    _log(f"eval_loss={eval_loss:.4f}  n_completions={len(completions)}")
+    _log("Batched generation pass")
+    gen_outputs = llm.generate(
+        prompt_token_ids=prompt_token_ids,
+        sampling_params=SamplingParams(
+            max_tokens=MAX_NEW_TOKENS,
+            temperature=0,
+        ),
+        lora_request=lora_request,
+    )
+
+    _log("Batched loss pass (prompt logprobs)")
+    loss_outputs = llm.generate(
+        prompt_token_ids=full_token_ids,
+        sampling_params=SamplingParams(
+            max_tokens=1,
+            temperature=0,
+            prompt_logprobs=1,
+        ),
+        lora_request=lora_request,
+    )
 
+    eval_loss = compute_nll(loss_outputs, full_token_ids, prompt_lens)
+
+    completions: list[dict[str, Any]] = []
+    for example, gen_out in zip(examples, gen_outputs):
+        prompt_messages = example["messages"][:-1]
+        completions.append(
+            {
+                "messages": [
+                    {"role": m["role"], "content": m["content"]}
+                    for m in prompt_messages
+                ],
+                "generated_response": gen_out.outputs[0].text,
+            }
+        )
+
+    _log(f"eval_loss={eval_loss:.4f}  n_completions={len(completions)}")
     print(json.dumps({"eval_loss": eval_loss, "completions": completions}))
 
 
diff --git a/uv.lock b/uv.lock
index 5eabfe7..4dc9c12 100644
--- a/uv.lock
+++ b/uv.lock
@@ -28,8 +28,9 @@ version = "1.13.0"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13'" },
-    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and sys_platform == 'emscripten') or (python_full_version < '3.13' and sys_platform == 'win32')" },
+    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.13' and sys_platform == 'emscripten') or (python_full_version >= '3.13' and sys_platform == 'win32')" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -270,6 +271,15 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/67/29/57b06fdb3abdf52c621d3ca3caea735e2db4c8d48288ebd26af448e8e247/art-6.5-py3-none-any.whl", hash = "sha256:70706408144c45c666caab690627d5c74aea7b6c7ce8cc968408ddeef8d84afd", size = 610382, upload-time = "2025-04-12T17:02:21.97Z" },
 ]
 
+[[package]]
+name = "astor"
+version = "0.8.1"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/5a/21/75b771132fee241dfe601d39ade629548a9626d1d39f333fde31bc46febe/astor-0.8.1.tar.gz", hash = "sha256:6a6effda93f4e1ce9f618779b2dd1d9d84f1e32812c23a29b3fff6fd7f63fa5e", size = 35090, upload-time = "2019-12-10T01:50:35.51Z" }
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/c3/88/97eef84f48fa04fbd6750e62dcceafba6c63c81b7ac1420856c8dcc0a3f9/astor-0.8.1-py2.py3-none-any.whl", hash = "sha256:070a54e890cefb5b3739d19f30f5a5ec840ffc9c50ffa7d23cc9fc1a38ebbfc5", size = 27488, upload-time = "2019-12-10T01:50:33.628Z" },
+]
+
 [[package]]
 name = "asttokens"
 version = "3.0.1"
@@ -339,8 +349,8 @@ wheels = [
 
 [[package]]
 name = "axolotl"
-version = "0.16.1"
-source = { registry = "https://pypi.org/simple" }
+version = "0.16.0.dev0"
+source = { git = "https://github.com/axolotl-ai-cloud/axolotl.git?rev=v0.16.1#08fc7de87e79f38c367f6776c5111b40a914062e" }
 dependencies = [
     { name = "accelerate", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "addict", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
@@ -349,7 +359,6 @@ dependencies = [
     { name = "art", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "axolotl-contribs-lgpl", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "axolotl-contribs-mit", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "bitsandbytes", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "colorama", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "datasets", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "einops", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
@@ -366,13 +375,11 @@ dependencies = [
     { name = "immutabledict", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "kernels", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "langdetect", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "liger-kernel", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "lm-eval", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "mistral-common", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "modal", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "numba", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "nvidia-ml-py", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "ocifs", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "openenv-core", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
@@ -390,18 +397,14 @@ dependencies = [
     { name = "sentencepiece", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "tensorboard", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "tokenizers", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "torch", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "torchao", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "trackio", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "transformers", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "triton", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "trl", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "typing-extensions", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "wandb", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "xformers", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "zstandard", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
 ]
-sdist = { url = "https://files.pythonhosted.org/packages/d4/8d/1e3263a1fc108b5ac1c6035b892d4939147f4766a1af10e8fa644c0d7c60/axolotl-0.16.1.tar.gz", hash = "sha256:0c3f279db8c8799de638fc3d0a7860e55446b539a1d455c727c4ca531571bee9", size = 717631, upload-time = "2026-04-02T21:51:20.244Z" }
 
 [[package]]
 name = "axolotl-contribs-lgpl"
@@ -409,8 +412,7 @@ version = "0.0.7"
 source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "datasets", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
     { name = "torch", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
 ]
 sdist = { url = "https://files.pythonhosted.org/packages/09/60/d9687c5057dfc0ebbccd7c6996cf0f6337794c04a501a92eb6f37709e26b/axolotl_contribs_lgpl-0.0.7.tar.gz", hash = "sha256:f797b3fd0adb5cbf93ed7070c8de61f90fd2f42cb93713e394970ab0e0a77a4d", size = 10922, upload-time = "2025-10-29T17:46:16.215Z" }
@@ -524,18 +526,35 @@ wheels = [
 ]
 
 [[package]]
-name = "bitsandbytes"
-version = "0.49.1"
+name = "blake3"
+version = "1.0.8"
 source = { registry = "https://pypi.org/simple" }
-dependencies = [
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "packaging", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "torch", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-]
+sdist = { url = "https://files.pythonhosted.org/packages/75/aa/abcd75e9600987a0bc6cfe9b6b2ff3f0e2cb08c170addc6e76035b5c4cb3/blake3-1.0.8.tar.gz", hash = "sha256:513cc7f0f5a7c035812604c2c852a0c1468311345573de647e310aca4ab165ba", size = 117308, upload-time = "2025-10-14T06:47:48.83Z" }
 wheels = [
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-    { url = "https://files.pythonhosted.org/packages/1d/4f/02d3cb62a1b0b5a1ca7ff03dce3606be1bf3ead4744f47eb762dbf471069/bitsandbytes-0.49.1-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:e7940bf32457dc2e553685285b2a86e82f5ec10b2ae39776c408714f9ae6983c", size = 59054193, upload-time = "2026-01-08T14:31:31.743Z" },
+    { url = "https://files.pythonhosted.org/packages/ee/7d/85a4c0782f613de23d114a7a78fcce270f75b193b3ff3493a0de24ba104a/blake3-1.0.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:269f255b110840e52b6ce9db02217e39660ebad3e34ddd5bca8b8d378a77e4e1", size = 371296, upload-time = "2025-10-14T06:45:49.674Z" },
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+]
+
 [[package]]
 name = "voice"
 version = "0.1.0"
@@ -4981,6 +5793,7 @@ dependencies = [
     { name = "sentence-transformers" },
     { name = "transformers" },
     { name = "typer" },
+    { name = "vllm", marker = "sys_platform == 'linux'" },
     { name = "wandb" },
 ]
 
@@ -5002,7 +5815,7 @@ dev = [
 [package.metadata]
 requires-dist = [
     { name = "accelerate" },
-    { name = "axolotl", marker = "sys_platform == 'linux'", specifier = ">=0.16.1" },
+    { name = "axolotl", marker = "sys_platform == 'linux'", git = "https://github.com/axolotl-ai-cloud/axolotl.git?rev=v0.16.1" },
     { name = "datasets", specifier = ">=4.5.0" },
     { name = "dotenv", specifier = ">=0.9.9" },
     { name = "huggingface-hub", specifier = ">=1.4.1" },
@@ -5015,6 +5828,7 @@ requires-dist = [
     { name = "sentence-transformers", specifier = ">=5.3.0" },
     { name = "transformers" },
     { name = "typer", specifier = ">=0.9.0" },
+    { name = "vllm", marker = "sys_platform == 'linux'", specifier = ">=0.11.0" },
     { name = "wandb", specifier = ">=0.25.1" },
 ]
 
@@ -5133,6 +5947,23 @@ wheels = [
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 ]
 
+[[package]]
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+version = "16.0"
+source = { registry = "https://pypi.org/simple" }
+sdist = { url = "https://files.pythonhosted.org/packages/04/24/4b2031d72e840ce4c1ccb255f693b15c334757fc50023e4db9537080b8c4/websockets-16.0.tar.gz", hash = "sha256:5f6261a5e56e8d5c42a4497b364ea24d94d9563e8fbd44e78ac40879c60179b5", size = 179346, upload-time = "2026-01-10T09:23:47.181Z" }
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+
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 name = "werkzeug"
 version = "3.1.8"
@@ -5170,16 +6001,35 @@ wheels = [
 
 [[package]]
 name = "xformers"
-version = "0.0.32.post2"
+version = "0.0.32.post1"
 source = { registry = "https://pypi.org/simple" }
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+    { name = "pydantic", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "torch", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
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+    { name = "triton", marker = "platform_machine == 'x86_64' and sys_platform == 'linux'" },
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+]
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 [[package]]

From 8a2b5bac21bf2dc0098cccaa030579ee60bbb4bd Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:39:52 +0100
Subject: [PATCH 14/68] chore: add bnb

---
 pyproject.toml |  1 +
 uv.lock        | 16 ++++++++++++++++
 2 files changed, 17 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index cd8cc3e..24564b3 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -21,6 +21,7 @@ dependencies = [
     "transformers",
     "wandb>=0.25.1",
     "vllm>=0.11.0 ; sys_platform == 'linux'",
+    "bitsandbytes>=0.49.2 ; sys_platform == 'linux'",
 ]
 
 [tool.uv]
diff --git a/uv.lock b/uv.lock
index 4dc9c12..9909630 100644
--- a/uv.lock
+++ b/uv.lock
@@ -525,6 +525,20 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/1a/39/47f9197bdd44df24d67ac8893641e16f386c984a0619ef2ee4c51fbbc019/beautifulsoup4-4.14.3-py3-none-any.whl", hash = "sha256:0918bfe44902e6ad8d57732ba310582e98da931428d231a5ecb9e7c703a735bb", size = 107721, upload-time = "2025-11-30T15:08:24.087Z" },
 ]
 
+[[package]]
+name = "bitsandbytes"
+version = "0.49.2"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "packaging", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+    { name = "torch", marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
+]
+wheels = [
+    { url = "https://files.pythonhosted.org/packages/29/71/acff7af06c818664aa87ff73e17a52c7788ad746b72aea09d3cb8e424348/bitsandbytes-0.49.2-py3-none-manylinux_2_24_aarch64.whl", hash = "sha256:2fc0830c5f7169be36e60e11f2be067c8f812dfcb829801a8703735842450750", size = 31442815, upload-time = "2026-02-16T21:26:06.783Z" },
+    { url = "https://files.pythonhosted.org/packages/19/57/3443d6f183436fbdaf5000aac332c4d5ddb056665d459244a5608e98ae92/bitsandbytes-0.49.2-py3-none-manylinux_2_24_x86_64.whl", hash = "sha256:54b771f06e1a3c73af5c7f16ccf0fc23a846052813d4b008d10cb6e017dd1c8c", size = 60651714, upload-time = "2026-02-16T21:26:11.579Z" },
+]
+
 [[package]]
 name = "blake3"
 version = "1.0.8"
@@ -5781,6 +5795,7 @@ source = { editable = "." }
 dependencies = [
     { name = "accelerate" },
     { name = "axolotl", marker = "sys_platform == 'linux'" },
+    { name = "bitsandbytes", marker = "sys_platform == 'linux'" },
     { name = "datasets" },
     { name = "dotenv" },
     { name = "huggingface-hub" },
@@ -5816,6 +5831,7 @@ dev = [
 requires-dist = [
     { name = "accelerate" },
     { name = "axolotl", marker = "sys_platform == 'linux'", git = "https://github.com/axolotl-ai-cloud/axolotl.git?rev=v0.16.1" },
+    { name = "bitsandbytes", marker = "sys_platform == 'linux'", specifier = ">=0.49.2" },
     { name = "datasets", specifier = ">=4.5.0" },
     { name = "dotenv", specifier = ">=0.9.9" },
     { name = "huggingface-hub", specifier = ">=1.4.1" },

From 89bd4c0cb7de8523d8964e92284a76e39df166bb Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:48:41 +0100
Subject: [PATCH 15/68] fix: migrate to slow tokenizer for vllm

---
 sweep/infer.py | 1 +
 sweep/sweep.py | 5 ++++-
 2 files changed, 5 insertions(+), 1 deletion(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 3531e83..4c9090d 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -145,6 +145,7 @@ def main() -> None:
         dtype="bfloat16",
         gpu_memory_utilization=0.9,
         max_lora_rank=MAX_LORA_RANK,
+        tokenizer_mode="slow",
     )
     lora_request = LoRARequest("adapter", 1, args.checkpoint)
     tokenizer = llm.get_tokenizer()
diff --git a/sweep/sweep.py b/sweep/sweep.py
index 3138c58..2241cbe 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -212,11 +212,14 @@ def run_inference(checkpoint_path: Path) -> dict[str, Any]:
         ],
         capture_output=True,
         text=True,
-        check=True,
     )
     if proc.stderr:
         for line in proc.stderr.strip().splitlines():
             log.info(f"  [infer] {line}")
+    if proc.returncode != 0:
+        raise RuntimeError(
+            f"infer.py exited with code {proc.returncode}"
+        )
     return cast(dict[str, Any], json.loads(proc.stdout))
 
 

From f70bfc96163121eed3104a3212b42d40ee8c52ed Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 10:55:19 +0100
Subject: [PATCH 16/68] fix: migrate to torch batched inference

---
 sweep/infer.py | 310 ++++++++++++++++++++++++++++++-------------------
 1 file changed, 193 insertions(+), 117 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 4c9090d..4e07386 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,8 +1,8 @@
 """Inference and test-loss script for a single fine-tuned checkpoint.
 
-Uses vLLM for batched generation and prompt logprobs for NLL loss.
-All 200 test examples are processed in two bulk passes (generation + loss),
-making this dramatically faster than sequential per-example inference.
+Loads the base model with LoRA adapter, runs greedy generation in batches
+on the Bush test split, and computes token-weighted NLL loss over assistant
+turns only.
 
 Outputs a single JSON object to stdout::
 
@@ -21,94 +21,204 @@
 import sys
 from typing import Any
 
+import torch
 from datasets import load_dataset
 from dotenv import load_dotenv
-from vllm import LLM, SamplingParams
-from vllm.lora.request import LoRARequest
+from peft import PeftModel
+from transformers import AutoModelForCausalLM, AutoTokenizer
 
 load_dotenv()
 
 BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
 DATASET = "AccelerateScience/bush-dataset"
 MAX_NEW_TOKENS = 512
-# Must be >= the largest lora_r in the sweep grid (32).
-MAX_LORA_RANK = 32
+GEN_BATCH_SIZE = 8
+LOSS_BATCH_SIZE = 16
 
 
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr, flush=True)
 
 
-def build_token_ids(
-    examples: list[dict[str, Any]],
+def _apply_template(tokenizer: Any, messages: list[dict[str, Any]], add_generation_prompt: bool) -> list[int]:
+    """Apply chat template and return a flat list of token IDs.
+
+    :param tokenizer: tokenizer to apply the template with.
+    :param messages: list of message dicts with ``role`` and ``content`` keys.
+    :param add_generation_prompt: whether to append the generation prompt.
+    :returns: list of integer token IDs.
+    """
+    ids = tokenizer.apply_chat_template(
+        messages,
+        tokenize=True,
+        add_generation_prompt=add_generation_prompt,
+    )
+    if hasattr(ids, "input_ids"):
+        ids = ids.input_ids
+        if hasattr(ids, "tolist"):
+            ids = ids[0].tolist()
+    return list(ids)
+
+
+def load_model_and_tokenizer(
+    checkpoint: str,
+    hf_token: str,
+) -> tuple[Any, Any]:
+    """Load the base model with LoRA adapter from a checkpoint directory.
+
+    :param checkpoint: path to the epoch checkpoint directory.
+    :param hf_token: HuggingFace API token for private model access.
+    :returns: (model, tokenizer) tuple ready for inference.
+    """
+    _log(f"Loading tokenizer from {BASE_MODEL}")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+
+    _log("Loading base model")
+    base = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL,
+        dtype=torch.bfloat16,
+        device_map="auto",
+        token=hf_token,
+    )
+
+    _log(f"Loading LoRA adapter from {checkpoint}")
+    model = PeftModel.from_pretrained(base, checkpoint)
+    model.eval()
+    return model, tokenizer
+
+
+def batched_generate(
+    model: Any,
     tokenizer: Any,
-) -> tuple[list[list[int]], list[list[int]], list[int]]:
-    """Tokenize all examples into full and prompt token ID lists.
+    examples: list[dict[str, Any]],
+) -> list[str]:
+    """Run greedy generation in batches over all examples.
 
-    :param examples: list of dataset examples from the test split.
+    Prompts are left-padded so all sequences in a batch end at the same
+    position, which is required for correct batched generation.
+
+    :param model: fine-tuned model (base + LoRA adapter).
     :param tokenizer: tokenizer matching the base model.
-    :returns: (full_token_ids, prompt_token_ids, prompt_lens) where
-        full_token_ids includes the assistant completion and
-        prompt_token_ids ends with the generation prompt.
+    :param examples: list of dataset examples from the test split.
+    :returns: list of generated response strings, one per example.
     """
-    full_token_ids: list[list[int]] = []
-    prompt_token_ids: list[list[int]] = []
-    prompt_lens: list[int] = []
-
-    for example in examples:
-        messages: list[dict[str, Any]] = example["messages"]
-        prompt_messages = messages[:-1]
-
-        full_ids = tokenizer.apply_chat_template(
-            messages,
-            tokenize=True,
-            add_generation_prompt=False,
-        )
-        if hasattr(full_ids, "input_ids"):
-            full_ids = list(full_ids.input_ids[0])
-
-        p_ids = tokenizer.apply_chat_template(
-            prompt_messages,
-            tokenize=True,
-            add_generation_prompt=True,
+    responses: list[str] = []
+    pad_id = tokenizer.pad_token_id
+
+    for start in range(0, len(examples), GEN_BATCH_SIZE):
+        batch = examples[start : start + GEN_BATCH_SIZE]
+
+        prompt_ids_list = [
+            _apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True)
+            for ex in batch
+        ]
+        max_len = max(len(ids) for ids in prompt_ids_list)
+
+        # Left-pad so all prompts end at the same position
+        input_ids = torch.tensor(
+            [[pad_id] * (max_len - len(ids)) + ids for ids in prompt_ids_list],
+            dtype=torch.long,
+        ).to(model.device)
+        attention_mask = torch.tensor(
+            [[0] * (max_len - len(ids)) + [1] * len(ids) for ids in prompt_ids_list],
+            dtype=torch.long,
+        ).to(model.device)
+
+        with torch.no_grad():
+            output_ids = model.generate(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                max_new_tokens=MAX_NEW_TOKENS,
+                do_sample=False,
+                pad_token_id=tokenizer.eos_token_id,
+            )
+
+        for ids in output_ids:
+            new_ids = ids[max_len:]
+            responses.append(tokenizer.decode(new_ids, skip_special_tokens=True))
+
+        _log(
+            f"  [gen] {min(start + GEN_BATCH_SIZE, len(examples))}"
+            f"/{len(examples)}"
         )
-        if hasattr(p_ids, "input_ids"):
-            p_ids = list(p_ids.input_ids[0])
-
-        full_token_ids.append(list(full_ids))
-        prompt_token_ids.append(list(p_ids))
-        prompt_lens.append(len(p_ids))
 
-    return full_token_ids, prompt_token_ids, prompt_lens
+    return responses
 
 
-def compute_nll(
-    loss_outputs: list[Any],
-    full_token_ids: list[list[int]],
-    prompt_lens: list[int],
+def batched_loss(
+    model: Any,
+    tokenizer: Any,
+    examples: list[dict[str, Any]],
 ) -> float:
-    """Compute token-weighted NLL loss from vLLM prompt logprobs.
+    """Compute token-weighted NLL loss over assistant turns in batches.
 
-    :param loss_outputs: vLLM RequestOutput list from the loss pass.
-    :param full_token_ids: tokenised full sequences (prompt + completion).
-    :param prompt_lens: length of the prompt portion for each example.
+    Loss is computed only over assistant tokens; prompt and padding tokens
+    are masked with -100.
+
+    :param model: fine-tuned model (base + LoRA adapter).
+    :param tokenizer: tokenizer matching the base model.
+    :param examples: list of dataset examples from the test split.
     :returns: token-weighted average NLL loss across all examples.
     """
     total_loss = 0.0
     total_tokens = 0
 
-    for i, out in enumerate(loss_outputs):
-        pl = out.prompt_logprobs  # List[Optional[Dict[int, Logprob]]]
-        full_ids = full_token_ids[i]
-        p_len = prompt_lens[i]
-
-        for j in range(p_len, len(full_ids)):
-            if pl is None or j >= len(pl) or pl[j] is None:
-                continue
-            token_id = full_ids[j]
-            if token_id in pl[j]:
-                total_loss += -pl[j][token_id].logprob
-                total_tokens += 1
+    for start in range(0, len(examples), LOSS_BATCH_SIZE):
+        batch = examples[start : start + LOSS_BATCH_SIZE]
+
+        full_ids_list = [
+            _apply_template(tokenizer, ex["messages"], add_generation_prompt=False)[:2048]
+            for ex in batch
+        ]
+        prompt_lens = [
+            len(_apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True))
+            for ex in batch
+        ]
+        max_len = max(len(ids) for ids in full_ids_list)
+        pad_id = tokenizer.pad_token_id
+
+        # Right-pad full sequences; labels mask prompt + padding with -100
+        input_ids = torch.zeros(len(batch), max_len, dtype=torch.long)
+        attention_mask = torch.zeros(len(batch), max_len, dtype=torch.long)
+        labels = torch.full((len(batch), max_len), -100, dtype=torch.long)
+
+        for i, (full_ids, p_len) in enumerate(zip(full_ids_list, prompt_lens)):
+            seq_len = len(full_ids)
+            input_ids[i, :seq_len] = torch.tensor(full_ids, dtype=torch.long)
+            input_ids[i, seq_len:] = pad_id
+            attention_mask[i, :seq_len] = 1
+            labels[i, p_len:seq_len] = torch.tensor(
+                full_ids[p_len:], dtype=torch.long
+            )
+
+        input_ids = input_ids.to(model.device)
+        attention_mask = attention_mask.to(model.device)
+        labels = labels.to(model.device)
+
+        with torch.no_grad():
+            logits = model(
+                input_ids=input_ids, attention_mask=attention_mask
+            ).logits
+
+        shift_logits = logits[..., :-1, :].contiguous()
+        shift_labels = labels[..., 1:].contiguous()
+        valid = shift_labels != -100
+        n_tokens = int(valid.sum().item())
+
+        if n_tokens > 0:
+            loss_val = torch.nn.functional.cross_entropy(
+                shift_logits.view(-1, shift_logits.size(-1))[valid.view(-1)],
+                shift_labels.view(-1)[valid.view(-1)],
+            ).item()
+            total_loss += loss_val * n_tokens
+            total_tokens += n_tokens
+
+        _log(
+            f"  [loss] {min(start + LOSS_BATCH_SIZE, len(examples))}"
+            f"/{len(examples)}"
+        )
 
     return total_loss / total_tokens if total_tokens > 0 else float("nan")
 
@@ -129,67 +239,33 @@ def main() -> None:
     hf_token = (
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
-    if hf_token:
-        os.environ["HF_TOKEN"] = hf_token
+
+    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
 
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
     examples = list(ds)
-    _log(f"{len(examples)} test examples")
-
-    _log("Initialising vLLM engine")
-    llm = LLM(
-        model=BASE_MODEL,
-        enable_lora=True,
-        max_model_len=2048,
-        dtype="bfloat16",
-        gpu_memory_utilization=0.9,
-        max_lora_rank=MAX_LORA_RANK,
-        tokenizer_mode="slow",
+    _log(
+        f"Running inference on {len(examples)} examples "
+        f"(gen_batch={GEN_BATCH_SIZE}, loss_batch={LOSS_BATCH_SIZE})"
     )
-    lora_request = LoRARequest("adapter", 1, args.checkpoint)
-    tokenizer = llm.get_tokenizer()
 
-    _log("Tokenising examples")
-    full_token_ids, prompt_token_ids, prompt_lens = build_token_ids(
-        examples, tokenizer
-    )
-
-    _log("Batched generation pass")
-    gen_outputs = llm.generate(
-        prompt_token_ids=prompt_token_ids,
-        sampling_params=SamplingParams(
-            max_tokens=MAX_NEW_TOKENS,
-            temperature=0,
-        ),
-        lora_request=lora_request,
-    )
-
-    _log("Batched loss pass (prompt logprobs)")
-    loss_outputs = llm.generate(
-        prompt_token_ids=full_token_ids,
-        sampling_params=SamplingParams(
-            max_tokens=1,
-            temperature=0,
-            prompt_logprobs=1,
-        ),
-        lora_request=lora_request,
-    )
-
-    eval_loss = compute_nll(loss_outputs, full_token_ids, prompt_lens)
-
-    completions: list[dict[str, Any]] = []
-    for example, gen_out in zip(examples, gen_outputs):
-        prompt_messages = example["messages"][:-1]
-        completions.append(
-            {
-                "messages": [
-                    {"role": m["role"], "content": m["content"]}
-                    for m in prompt_messages
-                ],
-                "generated_response": gen_out.outputs[0].text,
-            }
-        )
+    _log("Generation pass")
+    responses = batched_generate(model, tokenizer, examples)
+
+    _log("Loss pass")
+    eval_loss = batched_loss(model, tokenizer, examples)
+
+    completions: list[dict[str, Any]] = [
+        {
+            "messages": [
+                {"role": m["role"], "content": m["content"]}
+                for m in ex["messages"][:-1]
+            ],
+            "generated_response": response,
+        }
+        for ex, response in zip(examples, responses)
+    ]
 
     _log(f"eval_loss={eval_loss:.4f}  n_completions={len(completions)}")
     print(json.dumps({"eval_loss": eval_loss, "completions": completions}))

From e580e04a3d461049c482088d8cc35e2cc4f3fed6 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 14:57:21 +0100
Subject: [PATCH 17/68] chore: fix triton kernels

---
 sweep/infer.py           | 7 +++++++
 sweep/master_config.yaml | 8 ++++++++
 2 files changed, 15 insertions(+)

diff --git a/sweep/infer.py b/sweep/infer.py
index 4e07386..b07a2ea 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -126,6 +126,12 @@ def batched_generate(
             dtype=torch.long,
         ).to(model.device)
 
+        # Llama 3.1 chat template ends turns with <|eot_id|> (128009), not
+        # <|end_of_text|> (128001). Both must be EOS terminators or the model
+        # blows past the natural stopping point and loops.
+        eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
+        eos_ids = list({tokenizer.eos_token_id, eot_id})
+
         with torch.no_grad():
             output_ids = model.generate(
                 input_ids=input_ids,
@@ -133,6 +139,7 @@ def batched_generate(
                 max_new_tokens=MAX_NEW_TOKENS,
                 do_sample=False,
                 pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=eos_ids,
             )
 
         for ids in output_ids:
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 33b9c7c..e4b6c73 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -81,6 +81,14 @@ axolotl:
   flash_attention: true
   gradient_checkpointing: true
 
+  # Disable axolotl's auto-enabled LoRA kernel optimizations. The MLP SwiGLU
+  # Triton kernel fails to compile on this host (gcc cannot link libcuda),
+  # which crashes all attention_mlp runs. Disabling here keeps training stable
+  # at the cost of a small throughput regression.
+  lora_mlp_kernel: false
+  lora_qkv_kernel: false
+  lora_o_kernel: false
+
   # Optimiser
   optimizer: adamw_torch_fused
   lr_scheduler: cosine

From b005b6723f47b09f66b81868631a0f145ca76eba Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 16:31:05 +0100
Subject: [PATCH 18/68] chore(deps): update vllm

---
 pyproject.toml |   4 +-
 uv.lock        | 920 ++-----------------------------------------------
 2 files changed, 33 insertions(+), 891 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index 24564b3..38a4682 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -3,7 +3,7 @@ name = "voice"
 version = "0.1.0"
 description = "Fine-tuning for stylistic fidelity"
 readme = "README.md"
-requires-python = ">=3.12,<3.14"
+requires-python = ">=3.12,<3.13"
 dependencies = [
     "accelerate",
     "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@v0.16.1 ; sys_platform == 'linux'",
@@ -21,7 +21,7 @@ dependencies = [
     "transformers",
     "wandb>=0.25.1",
     "vllm>=0.11.0 ; sys_platform == 'linux'",
-    "bitsandbytes>=0.49.2 ; sys_platform == 'linux'",
+    "bitsandbytes>=0.49.1 ; sys_platform == 'linux'",
 ]
 
 [tool.uv]
diff --git a/uv.lock b/uv.lock
index 9909630..5c2fedd 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,13 +1,11 @@
 version = 1
 revision = 3
-requires-python = ">=3.12, <3.14"
+requires-python = "==3.12.*"
 resolution-markers = [
-    "python_full_version >= '3.13' and sys_platform == 'win32'",
-    "python_full_version >= '3.13' and sys_platform == 'emscripten'",
-    "python_full_version >= '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'win32'",
-    "python_full_version < '3.13' and sys_platform == 'emscripten'",
-    "python_full_version < '3.13' and sys_platform != 'emscripten' and sys_platform != 'win32'",
+    "python_version < '0'",
+    "sys_platform == 'win32'",
+    "sys_platform == 'emscripten'",
+    "sys_platform != 'emscripten' and sys_platform != 'win32'",
 ]
 required-markers = [
     "sys_platform == 'linux'",
@@ -29,8 +27,7 @@ source = { registry = "https://pypi.org/simple" }
 dependencies = [
     { name = "huggingface-hub" },
     { name = "numpy", version = "2.2.6", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform != 'emscripten' and sys_platform != 'win32'" },
-    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version < '3.13' and sys_platform == 'emscripten') or (python_full_version < '3.13' and sys_platform == 'win32')" },
-    { name = "numpy", version = "2.4.2", source = { registry = "https://pypi.org/simple" }, marker = "(python_full_version >= '3.13' and sys_platform == 'emscripten') or (python_full_version >= '3.13' and sys_platform == 'win32')" },
+    { name = "numpy", version = "2.3.5", source = { registry = "https://pypi.org/simple" }, marker = "sys_platform == 'emscripten' or sys_platform == 'win32'" },
     { name = "packaging" },
     { name = "psutil" },
     { name = "pyyaml" },
@@ -126,23 +123,6 @@ wheels = [
     { url = "https://files.pythonhosted.org/packages/9d/f2/a07a75173124f31f11ea6f863dc44e6f09afe2bca45dd4e64979490deab1/aiohttp-3.13.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:87797e645d9d8e222e04160ee32aa06bc5c163e8499f24db719e7852ec23093a", size = 1722379, upload-time = "2026-01-03T17:30:41.081Z" },
     { url = "https://files.pythonhosted.org/packages/3c/4a/1a3fee7c21350cac78e5c5cef711bac1b94feca07399f3d406972e2d8fcd/aiohttp-3.13.3-cp312-cp312-win32.whl", hash = "sha256:b04be762396457bef43f3597c991e192ee7da460a4953d7e647ee4b1c28e7046", size = 428253, upload-time = "2026-01-03T17:30:42.644Z" },
     { url = "https://files.pythonhosted.org/packages/d9/b7/76175c7cb4eb73d91ad63c34e29fc4f77c9386bba4a65b53ba8e05ee3c39/aiohttp-3.13.3-cp312-cp312-win_amd64.whl", hash = "sha256:e3531d63d3bdfa7e3ac5e9b27b2dd7ec9df3206a98e0b3445fa906f233264c57", size = 455407, upload-time = "2026-01-03T17:30:44.195Z" },
-    { url = "https://files.pythonhosted.org/packages/97/8a/12ca489246ca1faaf5432844adbfce7ff2cc4997733e0af120869345643a/aiohttp-3.13.3-cp313-cp313-macosx_10_13_universal2.whl", hash = "sha256:5dff64413671b0d3e7d5918ea490bdccb97a4ad29b3f311ed423200b2203e01c", size = 734190, upload-time = "2026-01-03T17:30:45.832Z" },
-    { url = "https://files.pythonhosted.org/packages/32/08/de43984c74ed1fca5c014808963cc83cb00d7bb06af228f132d33862ca76/aiohttp-3.13.3-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:87b9aab6d6ed88235aa2970294f496ff1a1f9adcd724d800e9b952395a80ffd9", size = 491783, upload-time = "2026-01-03T17:30:47.466Z" },
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From 7153cc1afa574902dde95e9c60d23b2efbe5cfb1 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 16:36:50 +0100
Subject: [PATCH 19/68] feat: migrate back to vLLM

---
 sweep/infer.py | 150 ++++++++++++++++++++++---------------------------
 1 file changed, 68 insertions(+), 82 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index b07a2ea..594cbc8 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,8 +1,7 @@
 """Inference and test-loss script for a single fine-tuned checkpoint.
 
-Loads the base model with LoRA adapter, runs greedy generation in batches
-on the Bush test split, and computes token-weighted NLL loss over assistant
-turns only.
+Uses vLLM with a LoRA adapter for fast generation, then loads the model
+via PyTorch/PEFT to compute token-weighted NLL loss over assistant turns.
 
 Outputs a single JSON object to stdout::
 
@@ -16,6 +15,7 @@
 """
 
 import argparse
+import gc
 import json
 import os
 import sys
@@ -26,21 +26,76 @@
 from dotenv import load_dotenv
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
 
 load_dotenv()
 
 BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
 DATASET = "AccelerateScience/bush-dataset"
-MAX_NEW_TOKENS = 512
-GEN_BATCH_SIZE = 8
+MAX_NEW_TOKENS = 1024
+TEMPERATURE = 0.7
 LOSS_BATCH_SIZE = 16
+# Must cover the largest lora_r in the sweep grid (32).
+MAX_LORA_RANK = 32
 
 
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr, flush=True)
 
 
-def _apply_template(tokenizer: Any, messages: list[dict[str, Any]], add_generation_prompt: bool) -> list[int]:
+def generate_with_vllm(
+    checkpoint: str,
+    examples: list[dict[str, Any]],
+) -> list[str]:
+    """Run generation with vLLM using a LoRA adapter.
+
+    vLLM handles batching and padding internally via PagedAttention,
+    so no manual left-padding is required here.
+
+    :param checkpoint: path to the LoRA adapter checkpoint directory.
+    :param examples: list of dataset examples from the test split.
+    :returns: list of generated response strings, one per example.
+    """
+    _log(f"Loading vLLM engine with LoRA from {checkpoint}")
+    llm = LLM(
+        model=BASE_MODEL,
+        enable_lora=True,
+        max_model_len=4096,
+        dtype="bfloat16",
+        tensor_parallel_size=1,
+        max_lora_rank=MAX_LORA_RANK,
+    )
+
+    lora_request = LoRARequest("adapter", 1, checkpoint)
+    sampling_params = SamplingParams(
+        temperature=TEMPERATURE,
+        max_tokens=MAX_NEW_TOKENS,
+    )
+
+    prompts = [
+        [{"role": m["role"], "content": m["content"]} for m in ex["messages"][:-1]]
+        for ex in examples
+    ]
+
+    _log(f"Generating {len(prompts)} responses")
+    outputs = llm.chat(prompts, sampling_params, lora_request=lora_request)
+    responses = [output.outputs[0].text.strip() for output in outputs]
+
+    # Free GPU memory before loading the PyTorch model for the loss pass.
+    del llm
+    gc.collect()
+    torch.cuda.empty_cache()
+    _log("vLLM engine unloaded")
+
+    return responses
+
+
+def _apply_template(
+    tokenizer: Any,
+    messages: list[dict[str, Any]],
+    add_generation_prompt: bool,
+) -> list[int]:
     """Apply chat template and return a flat list of token IDs.
 
     :param tokenizer: tokenizer to apply the template with.
@@ -60,11 +115,11 @@ def _apply_template(tokenizer: Any, messages: list[dict[str, Any]], add_generati
     return list(ids)
 
 
-def load_model_and_tokenizer(
+def load_pytorch_model(
     checkpoint: str,
     hf_token: str,
 ) -> tuple[Any, Any]:
-    """Load the base model with LoRA adapter from a checkpoint directory.
+    """Load the base model with LoRA adapter for the loss pass.
 
     :param checkpoint: path to the epoch checkpoint directory.
     :param hf_token: HuggingFace API token for private model access.
@@ -78,7 +133,7 @@ def load_model_and_tokenizer(
     _log("Loading base model")
     base = AutoModelForCausalLM.from_pretrained(
         BASE_MODEL,
-        dtype=torch.bfloat16,
+        torch_dtype=torch.bfloat16,
         device_map="auto",
         token=hf_token,
     )
@@ -89,71 +144,6 @@ def load_model_and_tokenizer(
     return model, tokenizer
 
 
-def batched_generate(
-    model: Any,
-    tokenizer: Any,
-    examples: list[dict[str, Any]],
-) -> list[str]:
-    """Run greedy generation in batches over all examples.
-
-    Prompts are left-padded so all sequences in a batch end at the same
-    position, which is required for correct batched generation.
-
-    :param model: fine-tuned model (base + LoRA adapter).
-    :param tokenizer: tokenizer matching the base model.
-    :param examples: list of dataset examples from the test split.
-    :returns: list of generated response strings, one per example.
-    """
-    responses: list[str] = []
-    pad_id = tokenizer.pad_token_id
-
-    for start in range(0, len(examples), GEN_BATCH_SIZE):
-        batch = examples[start : start + GEN_BATCH_SIZE]
-
-        prompt_ids_list = [
-            _apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True)
-            for ex in batch
-        ]
-        max_len = max(len(ids) for ids in prompt_ids_list)
-
-        # Left-pad so all prompts end at the same position
-        input_ids = torch.tensor(
-            [[pad_id] * (max_len - len(ids)) + ids for ids in prompt_ids_list],
-            dtype=torch.long,
-        ).to(model.device)
-        attention_mask = torch.tensor(
-            [[0] * (max_len - len(ids)) + [1] * len(ids) for ids in prompt_ids_list],
-            dtype=torch.long,
-        ).to(model.device)
-
-        # Llama 3.1 chat template ends turns with <|eot_id|> (128009), not
-        # <|end_of_text|> (128001). Both must be EOS terminators or the model
-        # blows past the natural stopping point and loops.
-        eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
-        eos_ids = list({tokenizer.eos_token_id, eot_id})
-
-        with torch.no_grad():
-            output_ids = model.generate(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                max_new_tokens=MAX_NEW_TOKENS,
-                do_sample=False,
-                pad_token_id=tokenizer.eos_token_id,
-                eos_token_id=eos_ids,
-            )
-
-        for ids in output_ids:
-            new_ids = ids[max_len:]
-            responses.append(tokenizer.decode(new_ids, skip_special_tokens=True))
-
-        _log(
-            f"  [gen] {min(start + GEN_BATCH_SIZE, len(examples))}"
-            f"/{len(examples)}"
-        )
-
-    return responses
-
-
 def batched_loss(
     model: Any,
     tokenizer: Any,
@@ -231,7 +221,7 @@ def batched_loss(
 
 
 def main() -> None:
-    """Parse arguments, run batched inference and loss, print JSON.
+    """Parse arguments, run vLLM generation and loss pass, print JSON.
 
     :returns: None
     """
@@ -247,20 +237,16 @@ def main() -> None:
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
 
-    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
-
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
     examples = list(ds)
-    _log(
-        f"Running inference on {len(examples)} examples "
-        f"(gen_batch={GEN_BATCH_SIZE}, loss_batch={LOSS_BATCH_SIZE})"
-    )
+    _log(f"Loaded {len(examples)} examples")
 
     _log("Generation pass")
-    responses = batched_generate(model, tokenizer, examples)
+    responses = generate_with_vllm(args.checkpoint, examples)
 
     _log("Loss pass")
+    model, tokenizer = load_pytorch_model(args.checkpoint, hf_token)
     eval_loss = batched_loss(model, tokenizer, examples)
 
     completions: list[dict[str, Any]] = [

From e42203bd9489f56242862180553cfd77af7a3ac7 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Mon, 13 Apr 2026 16:55:57 +0100
Subject: [PATCH 20/68] chore: remove vllm

---
 notebooks/results.parquet | Bin 0 -> 994171 bytes
 pyproject.toml            |   1 -
 sweep/infer.py            | 134 +++++++++++++++++++++-----------------
 3 files changed, 75 insertions(+), 60 deletions(-)
 create mode 100644 notebooks/results.parquet

diff --git a/notebooks/results.parquet b/notebooks/results.parquet
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HcmV?d00001

diff --git a/pyproject.toml b/pyproject.toml
index 38a4682..f72bf6b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -20,7 +20,6 @@ dependencies = [
     "sentence-transformers>=5.3.0",
     "transformers",
     "wandb>=0.25.1",
-    "vllm>=0.11.0 ; sys_platform == 'linux'",
     "bitsandbytes>=0.49.1 ; sys_platform == 'linux'",
 ]
 
diff --git a/sweep/infer.py b/sweep/infer.py
index 594cbc8..b1e5006 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,7 +1,7 @@
 """Inference and test-loss script for a single fine-tuned checkpoint.
 
-Uses vLLM with a LoRA adapter for fast generation, then loads the model
-via PyTorch/PEFT to compute token-weighted NLL loss over assistant turns.
+Loads the base model with LoRA adapter, runs batched generation on the Bush
+test split, and computes token-weighted NLL loss over assistant turns only.
 
 Outputs a single JSON object to stdout::
 
@@ -15,7 +15,6 @@
 """
 
 import argparse
-import gc
 import json
 import os
 import sys
@@ -26,8 +25,6 @@
 from dotenv import load_dotenv
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
-from vllm import LLM, SamplingParams
-from vllm.lora.request import LoRARequest
 
 load_dotenv()
 
@@ -35,62 +32,14 @@
 DATASET = "AccelerateScience/bush-dataset"
 MAX_NEW_TOKENS = 1024
 TEMPERATURE = 0.7
+GEN_BATCH_SIZE = 8
 LOSS_BATCH_SIZE = 16
-# Must cover the largest lora_r in the sweep grid (32).
-MAX_LORA_RANK = 32
 
 
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr, flush=True)
 
 
-def generate_with_vllm(
-    checkpoint: str,
-    examples: list[dict[str, Any]],
-) -> list[str]:
-    """Run generation with vLLM using a LoRA adapter.
-
-    vLLM handles batching and padding internally via PagedAttention,
-    so no manual left-padding is required here.
-
-    :param checkpoint: path to the LoRA adapter checkpoint directory.
-    :param examples: list of dataset examples from the test split.
-    :returns: list of generated response strings, one per example.
-    """
-    _log(f"Loading vLLM engine with LoRA from {checkpoint}")
-    llm = LLM(
-        model=BASE_MODEL,
-        enable_lora=True,
-        max_model_len=4096,
-        dtype="bfloat16",
-        tensor_parallel_size=1,
-        max_lora_rank=MAX_LORA_RANK,
-    )
-
-    lora_request = LoRARequest("adapter", 1, checkpoint)
-    sampling_params = SamplingParams(
-        temperature=TEMPERATURE,
-        max_tokens=MAX_NEW_TOKENS,
-    )
-
-    prompts = [
-        [{"role": m["role"], "content": m["content"]} for m in ex["messages"][:-1]]
-        for ex in examples
-    ]
-
-    _log(f"Generating {len(prompts)} responses")
-    outputs = llm.chat(prompts, sampling_params, lora_request=lora_request)
-    responses = [output.outputs[0].text.strip() for output in outputs]
-
-    # Free GPU memory before loading the PyTorch model for the loss pass.
-    del llm
-    gc.collect()
-    torch.cuda.empty_cache()
-    _log("vLLM engine unloaded")
-
-    return responses
-
-
 def _apply_template(
     tokenizer: Any,
     messages: list[dict[str, Any]],
@@ -115,11 +64,11 @@ def _apply_template(
     return list(ids)
 
 
-def load_pytorch_model(
+def load_model_and_tokenizer(
     checkpoint: str,
     hf_token: str,
 ) -> tuple[Any, Any]:
-    """Load the base model with LoRA adapter for the loss pass.
+    """Load the base model with LoRA adapter from a checkpoint directory.
 
     :param checkpoint: path to the epoch checkpoint directory.
     :param hf_token: HuggingFace API token for private model access.
@@ -144,6 +93,72 @@ def load_pytorch_model(
     return model, tokenizer
 
 
+def batched_generate(
+    model: Any,
+    tokenizer: Any,
+    examples: list[dict[str, Any]],
+) -> list[str]:
+    """Run batched generation over all examples.
+
+    Prompts are left-padded so all sequences in a batch end at the same
+    position, which is required for correct batched generation.
+
+    :param model: fine-tuned model (base + LoRA adapter).
+    :param tokenizer: tokenizer matching the base model.
+    :param examples: list of dataset examples from the test split.
+    :returns: list of generated response strings, one per example.
+    """
+    responses: list[str] = []
+    pad_id = tokenizer.pad_token_id
+
+    # Llama 3.1 chat template ends turns with <|eot_id|> (128009), not
+    # <|end_of_text|> (128001). Both must be EOS terminators.
+    eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
+    eos_ids = list({tokenizer.eos_token_id, eot_id})
+
+    for start in range(0, len(examples), GEN_BATCH_SIZE):
+        batch = examples[start : start + GEN_BATCH_SIZE]
+
+        prompt_ids_list = [
+            _apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True)
+            for ex in batch
+        ]
+        max_len = max(len(ids) for ids in prompt_ids_list)
+
+        # Left-pad so all prompts end at the same position
+        input_ids = torch.tensor(
+            [[pad_id] * (max_len - len(ids)) + ids for ids in prompt_ids_list],
+            dtype=torch.long,
+        ).to(model.device)
+        attention_mask = torch.tensor(
+            [[0] * (max_len - len(ids)) + [1] * len(ids) for ids in prompt_ids_list],
+            dtype=torch.long,
+        ).to(model.device)
+
+        with torch.no_grad():
+            output_ids = model.generate(
+                input_ids=input_ids,
+                attention_mask=attention_mask,
+                max_new_tokens=MAX_NEW_TOKENS,
+                do_sample=True,
+                temperature=TEMPERATURE,
+                pad_token_id=tokenizer.eos_token_id,
+                eos_token_id=eos_ids,
+            )
+
+        for ids in output_ids:
+            new_ids = ids[max_len:]
+            responses.append(tokenizer.decode(new_ids, skip_special_tokens=True))
+
+        _log(
+            f"  [gen] {min(start + GEN_BATCH_SIZE, len(examples))}"
+            f"/{len(examples)}"
+        )
+        _log(f"  [sample] {responses[-1][:200]!r}")
+
+    return responses
+
+
 def batched_loss(
     model: Any,
     tokenizer: Any,
@@ -221,7 +236,7 @@ def batched_loss(
 
 
 def main() -> None:
-    """Parse arguments, run vLLM generation and loss pass, print JSON.
+    """Parse arguments, run batched inference and loss, print JSON.
 
     :returns: None
     """
@@ -237,16 +252,17 @@ def main() -> None:
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
 
+    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
+
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
     examples = list(ds)
     _log(f"Loaded {len(examples)} examples")
 
     _log("Generation pass")
-    responses = generate_with_vllm(args.checkpoint, examples)
+    responses = batched_generate(model, tokenizer, examples)
 
     _log("Loss pass")
-    model, tokenizer = load_pytorch_model(args.checkpoint, hf_token)
     eval_loss = batched_loss(model, tokenizer, examples)
 
     completions: list[dict[str, Any]] = [

From 7d0a2806af00ce4888726c4446e8f262d896a9c4 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 08:47:43 +0100
Subject: [PATCH 21/68] chore: gitignore pilot results parquet

---
 .gitignore | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.gitignore b/.gitignore
index b11f7fc..ef1c07f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -55,3 +55,4 @@ htmlcov/
 
 # Notebooks
 **.ipynb
+notebooks/results.parquet
\ No newline at end of file

From 24fe3ac014576dc1f64cf3213728180fed197239 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 09:00:08 +0100
Subject: [PATCH 22/68] chore: add sweep results + logs to gitignore

---
 .gitignore                |   6 +++++-
 notebooks/results.parquet | Bin 994171 -> 0 bytes
 2 files changed, 5 insertions(+), 1 deletion(-)
 delete mode 100644 notebooks/results.parquet

diff --git a/.gitignore b/.gitignore
index ef1c07f..f449218 100644
--- a/.gitignore
+++ b/.gitignore
@@ -55,4 +55,8 @@ htmlcov/
 
 # Notebooks
 **.ipynb
-notebooks/results.parquet
\ No newline at end of file
+
+# Sweep results
+sweep/results.parquet
+sweep/sweep.log
+sweep/state.json
\ No newline at end of file
diff --git a/notebooks/results.parquet b/notebooks/results.parquet
deleted file mode 100644
index 089eacfb02f484310f8394b8903dff6ab51070da..0000000000000000000000000000000000000000
GIT binary patch
literal 0
HcmV?d00001

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From 59bb2ad1e6b13198832f2a362912ea9046f7c787 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 09:49:37 +0100
Subject: [PATCH 23/68] feat: migrate to vllm

---
 pyproject.toml             |   1 -
 sweep/README.md            |  39 +++++++--
 sweep/infer.py             | 165 +++++++++++++++++--------------------
 sweep/infer/pyproject.toml |  17 ++++
 sweep/master_config.yaml   |   6 ++
 sweep/sweep.py             |  40 ++++++---
 6 files changed, 157 insertions(+), 111 deletions(-)
 create mode 100644 sweep/infer/pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
index f72bf6b..3c6adad 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -13,7 +13,6 @@ dependencies = [
     "typer>=0.9.0",
     "matplotlib>=3.10.8",
     "pandas",
-    "peft",
     "pyyaml>=6.0.3",
     "scipy>=1.17.0",
     "seaborn>=0.13.2",
diff --git a/sweep/README.md b/sweep/README.md
index 6653835..c9f6ac9 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -3,9 +3,19 @@
 48 training runs × 3 epoch checkpoints = 144 data points across a grid of
 LoRA rank, learning rate, batch size, and target layer configurations.
 
+## Environments
+
+Two separate uv environments are required to avoid the axolotl/vLLM
+dependency conflict:
+
+| Environment | Location | Purpose |
+|---|---|---|
+| Training | `.venv/` (repo root) | axolotl fine-tuning via `sweep.py` |
+| Inference | `sweep/infer/.venv/` | vLLM generation + PEFT loss via `infer.py` |
+
 ## Setup on the H100
 
-**1. Clone the repo, install dependencies, and activate the venv**
+**1. Clone the repo and install the training environment**
 
 ```bash
 git clone <repo-url> && cd voice
@@ -13,13 +23,20 @@ uv sync
 source .venv/bin/activate
 ```
 
-**2. Set environment variables and log in**
+**2. Install the inference environment**
+
+```bash
+uv sync --project sweep/infer
+```
+
+This creates `sweep/infer/.venv/` with vLLM and PEFT. The sweep orchestrator
+calls this environment automatically for every inference pass.
+
+**3. Set environment variables**
 
 ```bash
 export HF_TOKEN=hf_...
 export WANDB_API_KEY=...
-hf auth login --token $HF_TOKEN
-wandb login $WANDB_API_KEY
 ```
 
 **4. Verify the grid looks correct**
@@ -36,13 +53,17 @@ Start a tmux session so the sweep survives SSH disconnection:
 ```bash
 tmux new -s sweep
 cd sweep
-python sweep.py
-# Override GPU if needed (default is device 3):
-# python sweep.py --device 0
+python sweep.py                    # default: GPUs 2,3
+python sweep.py --gpus 0           # single GPU
+python sweep.py --gpus 0,1,2,3    # all four GPUs
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
 
+Training automatically uses `accelerate launch` for multi-GPU runs and plain
+`python -m axolotl.cli.train` for single-GPU runs.  vLLM inference uses
+`tensor_parallel_size` equal to the number of GPUs in all cases.
+
 Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 
 ## Resuming after failure
@@ -57,7 +78,7 @@ python sweep.py
 ## Outputs
 
 Results are written to `outputs/results.parquet` after each training run
-completes. Schema mirrors `pilot_results.parquet` used in the notebooks:
+completes. Schema:
 
 | Column | Description |
 |---|---|
@@ -65,7 +86,7 @@ completes. Schema mirrors `pilot_results.parquet` used in the notebooks:
 | `epoch` | Epoch number (1, 2, or 3) |
 | `learning_rate` | LoRA learning rate |
 | `lora_r` | LoRA rank |
-| `lora_alpha` | LoRA alpha (= rank) |
+| `lora_alpha` | LoRA alpha (= 2 × rank) |
 | `effective_batch_size` | Effective batch size |
 | `target_layers` | `attention` or `attention_mlp` |
 | `eval_loss` | Test NLL loss at this checkpoint |
diff --git a/sweep/infer.py b/sweep/infer.py
index b1e5006..af8188c 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,7 +1,11 @@
 """Inference and test-loss script for a single fine-tuned checkpoint.
 
-Loads the base model with LoRA adapter, runs batched generation on the Bush
-test split, and computes token-weighted NLL loss over assistant turns only.
+Generation uses vLLM with the LoRA adapter loaded via LoRARequest.  vLLM
+applies the model's built-in chat template and stops automatically on
+<|eot_id|>, so no manual EOS token handling is required.
+
+Loss is computed with PyTorch/PEFT in a separate pass after the vLLM engine
+is torn down to reclaim GPU memory.
 
 Outputs a single JSON object to stdout::
 
@@ -15,6 +19,7 @@
 """
 
 import argparse
+import gc
 import json
 import os
 import sys
@@ -25,6 +30,8 @@
 from dotenv import load_dotenv
 from peft import PeftModel
 from transformers import AutoModelForCausalLM, AutoTokenizer
+from vllm import LLM, SamplingParams
+from vllm.lora.request import LoRARequest
 
 load_dotenv()
 
@@ -32,7 +39,6 @@
 DATASET = "AccelerateScience/bush-dataset"
 MAX_NEW_TOKENS = 1024
 TEMPERATURE = 0.7
-GEN_BATCH_SIZE = 8
 LOSS_BATCH_SIZE = 16
 
 
@@ -64,103 +70,65 @@ def _apply_template(
     return list(ids)
 
 
-def load_model_and_tokenizer(
+def vllm_generate(
     checkpoint: str,
-    hf_token: str,
-) -> tuple[Any, Any]:
-    """Load the base model with LoRA adapter from a checkpoint directory.
-
-    :param checkpoint: path to the epoch checkpoint directory.
-    :param hf_token: HuggingFace API token for private model access.
-    :returns: (model, tokenizer) tuple ready for inference.
-    """
-    _log(f"Loading tokenizer from {BASE_MODEL}")
-    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.eos_token
-
-    _log("Loading base model")
-    base = AutoModelForCausalLM.from_pretrained(
-        BASE_MODEL,
-        torch_dtype=torch.bfloat16,
-        device_map="auto",
-        token=hf_token,
-    )
-
-    _log(f"Loading LoRA adapter from {checkpoint}")
-    model = PeftModel.from_pretrained(base, checkpoint)
-    model.eval()
-    return model, tokenizer
-
-
-def batched_generate(
-    model: Any,
-    tokenizer: Any,
     examples: list[dict[str, Any]],
 ) -> list[str]:
-    """Run batched generation over all examples.
+    """Run batched generation with vLLM and the LoRA adapter.
 
-    Prompts are left-padded so all sequences in a batch end at the same
-    position, which is required for correct batched generation.
+    The vLLM engine is torn down and GPU memory freed before returning so
+    the subsequent PyTorch loss pass can load the model cleanly.
 
-    :param model: fine-tuned model (base + LoRA adapter).
-    :param tokenizer: tokenizer matching the base model.
+    :param checkpoint: path to the LoRA adapter checkpoint directory.
     :param examples: list of dataset examples from the test split.
     :returns: list of generated response strings, one per example.
     """
-    responses: list[str] = []
-    pad_id = tokenizer.pad_token_id
-
-    # Llama 3.1 chat template ends turns with <|eot_id|> (128009), not
-    # <|end_of_text|> (128001). Both must be EOS terminators.
-    eot_id = tokenizer.convert_tokens_to_ids("<|eot_id|>")
-    eos_ids = list({tokenizer.eos_token_id, eot_id})
+    cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
+    n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
+    _log(f"Loading vLLM engine on {n_gpus} GPU(s) (CUDA_VISIBLE_DEVICES={cuda_devices!r})")
+    llm = LLM(
+        model=BASE_MODEL,
+        enable_lora=True,
+        max_lora_rank=32,
+        dtype="bfloat16",
+        max_model_len=2048,
+        tensor_parallel_size=n_gpus,
+    )
 
-    for start in range(0, len(examples), GEN_BATCH_SIZE):
-        batch = examples[start : start + GEN_BATCH_SIZE]
+    lora_request = LoRARequest("adapter", 1, checkpoint)
+    sampling_params = SamplingParams(
+        temperature=TEMPERATURE,
+        max_tokens=MAX_NEW_TOKENS,
+    )
 
-        prompt_ids_list = [
-            _apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True)
-            for ex in batch
-        ]
-        max_len = max(len(ids) for ids in prompt_ids_list)
-
-        # Left-pad so all prompts end at the same position
-        input_ids = torch.tensor(
-            [[pad_id] * (max_len - len(ids)) + ids for ids in prompt_ids_list],
-            dtype=torch.long,
-        ).to(model.device)
-        attention_mask = torch.tensor(
-            [[0] * (max_len - len(ids)) + [1] * len(ids) for ids in prompt_ids_list],
-            dtype=torch.long,
-        ).to(model.device)
+    # Strip the final assistant turn — the model generates it.
+    chat_prompts = [
+        [m for m in ex["messages"] if m["role"] != "assistant"]
+        for ex in examples
+    ]
 
-        with torch.no_grad():
-            output_ids = model.generate(
-                input_ids=input_ids,
-                attention_mask=attention_mask,
-                max_new_tokens=MAX_NEW_TOKENS,
-                do_sample=True,
-                temperature=TEMPERATURE,
-                pad_token_id=tokenizer.eos_token_id,
-                eos_token_id=eos_ids,
-            )
+    _log(f"Generating {len(examples)} completions")
+    outputs = llm.chat(
+        chat_prompts,
+        sampling_params,
+        lora_request=lora_request,
+        use_tqdm=False,
+    )
+    responses = [o.outputs[0].text for o in outputs]
 
-        for ids in output_ids:
-            new_ids = ids[max_len:]
-            responses.append(tokenizer.decode(new_ids, skip_special_tokens=True))
+    for resp in responses[:3]:
+        _log(f"  [sample] {resp!r}")
 
-        _log(
-            f"  [gen] {min(start + GEN_BATCH_SIZE, len(examples))}"
-            f"/{len(examples)}"
-        )
-        _log(f"  [sample] {responses[-1][:200]!r}")
+    del llm
+    gc.collect()
+    torch.cuda.empty_cache()
+    _log("vLLM engine freed")
 
     return responses
 
 
-def batched_loss(
-    model: Any,
+def pytorch_loss(
+    checkpoint: str,
     tokenizer: Any,
     examples: list[dict[str, Any]],
 ) -> float:
@@ -169,11 +137,20 @@ def batched_loss(
     Loss is computed only over assistant tokens; prompt and padding tokens
     are masked with -100.
 
-    :param model: fine-tuned model (base + LoRA adapter).
+    :param checkpoint: path to the LoRA adapter checkpoint directory.
     :param tokenizer: tokenizer matching the base model.
     :param examples: list of dataset examples from the test split.
     :returns: token-weighted average NLL loss across all examples.
     """
+    _log(f"Loading base model + LoRA from {checkpoint}")
+    base = AutoModelForCausalLM.from_pretrained(
+        BASE_MODEL,
+        torch_dtype=torch.bfloat16,
+        device_map="auto",
+    )
+    model = PeftModel.from_pretrained(base, checkpoint)
+    model.eval()
+
     total_loss = 0.0
     total_tokens = 0
 
@@ -185,13 +162,16 @@ def batched_loss(
             for ex in batch
         ]
         prompt_lens = [
-            len(_apply_template(tokenizer, ex["messages"][:-1], add_generation_prompt=True))
+            len(
+                _apply_template(
+                    tokenizer, ex["messages"][:-1], add_generation_prompt=True
+                )
+            )
             for ex in batch
         ]
         max_len = max(len(ids) for ids in full_ids_list)
         pad_id = tokenizer.pad_token_id
 
-        # Right-pad full sequences; labels mask prompt + padding with -100
         input_ids = torch.zeros(len(batch), max_len, dtype=torch.long)
         attention_mask = torch.zeros(len(batch), max_len, dtype=torch.long)
         labels = torch.full((len(batch), max_len), -100, dtype=torch.long)
@@ -236,7 +216,7 @@ def batched_loss(
 
 
 def main() -> None:
-    """Parse arguments, run batched inference and loss, print JSON.
+    """Parse arguments, run generation then loss, print JSON.
 
     :returns: None
     """
@@ -244,7 +224,7 @@ def main() -> None:
     parser.add_argument(
         "--checkpoint",
         required=True,
-        help="Path to epoch checkpoint directory",
+        help="Path to epoch checkpoint directory (LoRA adapter)",
     )
     args = parser.parse_args()
 
@@ -252,7 +232,10 @@ def main() -> None:
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
 
-    model, tokenizer = load_model_and_tokenizer(args.checkpoint, hf_token)
+    _log(f"Loading tokenizer from {BASE_MODEL}")
+    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
 
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
@@ -260,10 +243,10 @@ def main() -> None:
     _log(f"Loaded {len(examples)} examples")
 
     _log("Generation pass")
-    responses = batched_generate(model, tokenizer, examples)
+    responses = vllm_generate(args.checkpoint, examples)
 
     _log("Loss pass")
-    eval_loss = batched_loss(model, tokenizer, examples)
+    eval_loss = pytorch_loss(args.checkpoint, tokenizer, examples)
 
     completions: list[dict[str, Any]] = [
         {
diff --git a/sweep/infer/pyproject.toml b/sweep/infer/pyproject.toml
new file mode 100644
index 0000000..de8ead5
--- /dev/null
+++ b/sweep/infer/pyproject.toml
@@ -0,0 +1,17 @@
+[project]
+name = "voice-infer"
+version = "0.1.0"
+description = "Inference environment for the pilot sweep (vLLM + PEFT)"
+requires-python = ">=3.12,<3.13"
+dependencies = [
+    "vllm>=0.11.0 ; sys_platform == 'linux'",
+    "peft",
+    "transformers",
+    "datasets>=4.5.0",
+    "python-dotenv",
+]
+
+# vLLM only ships GPU wheels for Linux; enforce this so uv does not attempt
+# to resolve a CPU-only environment on the developer's machine.
+[tool.uv]
+required-environments = ["sys_platform == 'linux'"]
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index e4b6c73..1f03b85 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -48,6 +48,12 @@ axolotl:
       roles_to_train:
         - assistant
       train_on_eos: last
+      # Llama 3.1's chat template ends turns with <|eot_id|> (128009), not
+      # <|end_of_text|> (128001).  Without this, axolotl defaults eot_tokens
+      # to eos_token (<|end_of_text|>) which never appears in the sequence, so
+      # train_on_eos: last silently does nothing and the model is never trained
+      # to predict its stop token.
+      eot_tokens: ["<|eot_id|>"]
 
   hf_use_auth_token: true
 
diff --git a/sweep/sweep.py b/sweep/sweep.py
index 2241cbe..ce6b054 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -38,6 +38,11 @@
 MASTER_CONFIG = SWEEP_DIR / "master_config.yaml"
 LOG_FILE = SWEEP_DIR / "sweep.log"
 
+# Python executable for the inference environment (vLLM + PEFT).
+# Create it once on the target machine with:
+#   uv sync --project sweep/infer
+INFER_PYTHON = SWEEP_DIR / "infer" / ".venv" / "bin" / "python"
+
 # Fixed micro batch size; gradient_accumulation_steps derived from
 # the effective_batch_size sweep axis.
 MICRO_BATCH_SIZE: int = 4
@@ -184,14 +189,29 @@ def find_epoch_checkpoints(run_name: str) -> dict[int, Path]:
 def run_training(config_path: Path) -> None:
     """Launch axolotl training for the given config.
 
+    Uses ``accelerate launch`` for multi-GPU runs so that DDP is set up
+    correctly; falls back to a plain ``python -m axolotl.cli.train`` for
+    single-GPU runs.  The number of processes is derived from the
+    ``CUDA_VISIBLE_DEVICES`` environment variable set by ``main()``.
+
     :param config_path: path to the per-run axolotl YAML config.
     :returns: None
     """
-    log.info(f"Training: axolotl train {config_path.name}")
-    subprocess.run(
-        [sys.executable, "-m", "axolotl.cli.train", str(config_path)],
-        check=True,
-    )
+    cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
+    n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
+
+    if n_gpus > 1:
+        cmd = [
+            sys.executable, "-m", "accelerate.commands.launch",
+            f"--num_processes={n_gpus}",
+            "-m", "axolotl.cli.train",
+            str(config_path),
+        ]
+    else:
+        cmd = [sys.executable, "-m", "axolotl.cli.train", str(config_path)]
+
+    log.info(f"Training on {n_gpus} GPU(s): {config_path.name}")
+    subprocess.run(cmd, check=True)
 
 
 # ---------------------------------------------------------------------------
@@ -205,7 +225,7 @@ def run_inference(checkpoint_path: Path) -> dict[str, Any]:
     """
     proc = subprocess.run(
         [
-            sys.executable,
+            str(INFER_PYTHON),
             str(SWEEP_DIR / "infer.py"),
             "--checkpoint",
             str(checkpoint_path),
@@ -313,13 +333,13 @@ def main() -> None:
         help="Print the full grid without training",
     )
     parser.add_argument(
-        "--device",
-        default="3",
-        help="CUDA device index (default: 3)",
+        "--gpus",
+        default="2,3",
+        help="Comma-separated CUDA device indices (default: 2,3)",
     )
     args = parser.parse_args()
 
-    os.environ["CUDA_VISIBLE_DEVICES"] = args.device
+    os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
 
     setup_logging()
 

From 6290698d874d973b802831ef1da980bbfd052cb7 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:02:00 +0100
Subject: [PATCH 24/68] chore: fix run

---
 sweep/infer.py           | 7 +++++--
 sweep/master_config.yaml | 3 +++
 sweep/sweep.py           | 2 ++
 3 files changed, 10 insertions(+), 2 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index af8188c..85eb0eb 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -86,6 +86,9 @@ def vllm_generate(
     cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
     n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
     _log(f"Loading vLLM engine on {n_gpus} GPU(s) (CUDA_VISIBLE_DEVICES={cuda_devices!r})")
+    # vLLM v1's multiproc executor defaults to fork, which breaks CUDA/NCCL
+    # initialisation for tensor-parallel runs.  Spawn is required.
+    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
     llm = LLM(
         model=BASE_MODEL,
         enable_lora=True,
@@ -234,8 +237,8 @@ def main() -> None:
 
     _log(f"Loading tokenizer from {BASE_MODEL}")
     tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
-    if tokenizer.pad_token is None:
-        tokenizer.pad_token = tokenizer.eos_token
+    # Match the pad token axolotl sets during training.
+    tokenizer.pad_token = "<|eot_id|>"
 
     _log(f"Loading test split from {DATASET}")
     ds = load_dataset(DATASET, split="test", token=hf_token)
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 1f03b85..9b9f9b8 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -55,6 +55,9 @@ axolotl:
       # to predict its stop token.
       eot_tokens: ["<|eot_id|>"]
 
+  special_tokens:
+    pad_token: "<|eot_id|>"
+
   hf_use_auth_token: true
 
   # Llama 3.1 Instruct ships with its own Jinja chat template in the
diff --git a/sweep/sweep.py b/sweep/sweep.py
index ce6b054..449b299 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -204,6 +204,8 @@ def run_training(config_path: Path) -> None:
         cmd = [
             sys.executable, "-m", "accelerate.commands.launch",
             f"--num_processes={n_gpus}",
+            "--mixed_precision=no",   # axolotl/HF Trainer handles bf16 itself
+            "--dynamo_backend=no",
             "-m", "axolotl.cli.train",
             str(config_path),
         ]

From 0f2ddd762989e21aa15e29e0060525bc26eb01ad Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:09:37 +0100
Subject: [PATCH 25/68] fix 3

---
 sweep/infer.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 85eb0eb..0ba783e 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -86,9 +86,12 @@ def vllm_generate(
     cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
     n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
     _log(f"Loading vLLM engine on {n_gpus} GPU(s) (CUDA_VISIBLE_DEVICES={cuda_devices!r})")
-    # vLLM v1's multiproc executor defaults to fork, which breaks CUDA/NCCL
-    # initialisation for tensor-parallel runs.  Spawn is required.
-    os.environ.setdefault("VLLM_WORKER_MULTIPROC_METHOD", "spawn")
+    # The vLLM v1 engine is unstable with tensor-parallel + LoRA in this
+    # version.  Force the v0 engine which has well-tested support for both.
+    # Also force spawn (not fork) for the v0 multiproc executor so CUDA/NCCL
+    # initialises cleanly across workers.
+    os.environ["VLLM_USE_V1"] = "0"
+    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
     llm = LLM(
         model=BASE_MODEL,
         enable_lora=True,

From a3357c00e657e8fde86ebd296e3703ae7b2379f6 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:19:07 +0100
Subject: [PATCH 26/68] chore anotehr fix

---
 sweep/infer.py | 16 ++++++++++------
 1 file changed, 10 insertions(+), 6 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 0ba783e..03ff42d 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -25,6 +25,12 @@
 import sys
 from typing import Any
 
+# Must be set before vllm is imported — vllm reads these at import time.
+# v0 engine has stable LoRA + tensor-parallel support; v1 does not.
+# spawn (not fork) avoids CUDA/NCCL re-init issues across worker processes.
+os.environ["VLLM_USE_V1"] = "0"
+os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
+
 import torch
 from datasets import load_dataset
 from dotenv import load_dotenv
@@ -86,12 +92,6 @@ def vllm_generate(
     cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
     n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
     _log(f"Loading vLLM engine on {n_gpus} GPU(s) (CUDA_VISIBLE_DEVICES={cuda_devices!r})")
-    # The vLLM v1 engine is unstable with tensor-parallel + LoRA in this
-    # version.  Force the v0 engine which has well-tested support for both.
-    # Also force spawn (not fork) for the v0 multiproc executor so CUDA/NCCL
-    # initialises cleanly across workers.
-    os.environ["VLLM_USE_V1"] = "0"
-    os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
     llm = LLM(
         model=BASE_MODEL,
         enable_lora=True,
@@ -99,6 +99,10 @@ def vllm_generate(
         dtype="bfloat16",
         max_model_len=2048,
         tensor_parallel_size=n_gpus,
+        # Disable CUDA graph capture and torch.compile/inductor AOT compilation.
+        # The server lacks python3.12-dev headers, which Triton needs to compile
+        # cuda_utils.c; enforce_eager bypasses that compilation entirely.
+        enforce_eager=True,
     )
 
     lora_request = LoRARequest("adapter", 1, checkpoint)

From 11eef81d1d2afc91da4e19018971d06efb0ea2f5 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:23:17 +0100
Subject: [PATCH 27/68] jdkansjn

---
 sweep/infer.py | 14 +++++++-------
 1 file changed, 7 insertions(+), 7 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 03ff42d..f6a1136 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -25,10 +25,14 @@
 import sys
 from typing import Any
 
-# Must be set before vllm is imported — vllm reads these at import time.
-# v0 engine has stable LoRA + tensor-parallel support; v1 does not.
+# Must be set before torch/vllm are imported — read at import/init time.
+# Disable torch.compile globally: vLLM's vocab_parallel_embedding has a
+# @torch.compile'd function (get_masked_input_and_mask) that invokes Triton,
+# which tries to compile cuda_utils.c via gcc and needs python3.12-dev headers
+# that are not installed on this server.  TORCHDYNAMO_DISABLE=1 makes all
+# @torch.compile decorators fall back to eager execution.
+os.environ["TORCHDYNAMO_DISABLE"] = "1"
 # spawn (not fork) avoids CUDA/NCCL re-init issues across worker processes.
-os.environ["VLLM_USE_V1"] = "0"
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
 import torch
@@ -99,10 +103,6 @@ def vllm_generate(
         dtype="bfloat16",
         max_model_len=2048,
         tensor_parallel_size=n_gpus,
-        # Disable CUDA graph capture and torch.compile/inductor AOT compilation.
-        # The server lacks python3.12-dev headers, which Triton needs to compile
-        # cuda_utils.c; enforce_eager bypasses that compilation entirely.
-        enforce_eager=True,
     )
 
     lora_request = LoRARequest("adapter", 1, checkpoint)

From f0692417f3a7a7250fd0b921cb9c262fe65c4152 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:28:26 +0100
Subject: [PATCH 28/68] ijas

---
 sweep/infer.py             | 10 +++-------
 sweep/infer/pyproject.toml |  4 ++++
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index f6a1136..4a83a69 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -25,13 +25,6 @@
 import sys
 from typing import Any
 
-# Must be set before torch/vllm are imported — read at import/init time.
-# Disable torch.compile globally: vLLM's vocab_parallel_embedding has a
-# @torch.compile'd function (get_masked_input_and_mask) that invokes Triton,
-# which tries to compile cuda_utils.c via gcc and needs python3.12-dev headers
-# that are not installed on this server.  TORCHDYNAMO_DISABLE=1 makes all
-# @torch.compile decorators fall back to eager execution.
-os.environ["TORCHDYNAMO_DISABLE"] = "1"
 # spawn (not fork) avoids CUDA/NCCL re-init issues across worker processes.
 os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
 
@@ -103,6 +96,9 @@ def vllm_generate(
         dtype="bfloat16",
         max_model_len=2048,
         tensor_parallel_size=n_gpus,
+        # Skip vLLM's AOT compilation pipeline — reduces startup overhead for
+        # the sweep's 144 sequential inference runs.
+        enforce_eager=True,
     )
 
     lora_request = LoRARequest("adapter", 1, checkpoint)
diff --git a/sweep/infer/pyproject.toml b/sweep/infer/pyproject.toml
index de8ead5..952ce4d 100644
--- a/sweep/infer/pyproject.toml
+++ b/sweep/infer/pyproject.toml
@@ -15,3 +15,7 @@ dependencies = [
 # to resolve a CPU-only environment on the developer's machine.
 [tool.uv]
 required-environments = ["sys_platform == 'linux'"]
+# Use uv's own managed Python (not system Python) — uv bundles the full Python
+# distribution including dev headers, which Triton needs to compile cuda_utils.c.
+# System Python 3.12 on this host lacks python3.12-dev headers.
+python-preference = "managed"

From 40ab15673c1293ec68dc042e345c2087e9d2ec25 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:36:12 +0100
Subject: [PATCH 29/68] chore: downgrade vllm

---
 sweep/infer.py             | 6 ------
 sweep/infer/pyproject.toml | 2 +-
 2 files changed, 1 insertion(+), 7 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index 4a83a69..7c7688d 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -25,9 +25,6 @@
 import sys
 from typing import Any
 
-# spawn (not fork) avoids CUDA/NCCL re-init issues across worker processes.
-os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
-
 import torch
 from datasets import load_dataset
 from dotenv import load_dotenv
@@ -96,9 +93,6 @@ def vllm_generate(
         dtype="bfloat16",
         max_model_len=2048,
         tensor_parallel_size=n_gpus,
-        # Skip vLLM's AOT compilation pipeline — reduces startup overhead for
-        # the sweep's 144 sequential inference runs.
-        enforce_eager=True,
     )
 
     lora_request = LoRARequest("adapter", 1, checkpoint)
diff --git a/sweep/infer/pyproject.toml b/sweep/infer/pyproject.toml
index 952ce4d..9bf926b 100644
--- a/sweep/infer/pyproject.toml
+++ b/sweep/infer/pyproject.toml
@@ -4,7 +4,7 @@ version = "0.1.0"
 description = "Inference environment for the pilot sweep (vLLM + PEFT)"
 requires-python = ">=3.12,<3.13"
 dependencies = [
-    "vllm>=0.11.0 ; sys_platform == 'linux'",
+    "vllm==0.11.0 ; sys_platform == 'linux'",
     "peft",
     "transformers",
     "datasets>=4.5.0",

From 75fdb01515f17dae58c2349678591d460e363564 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:45:08 +0100
Subject: [PATCH 30/68] fix transformers version

---
 sweep/infer/pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sweep/infer/pyproject.toml b/sweep/infer/pyproject.toml
index 9bf926b..b05b2e0 100644
--- a/sweep/infer/pyproject.toml
+++ b/sweep/infer/pyproject.toml
@@ -6,7 +6,7 @@ requires-python = ">=3.12,<3.13"
 dependencies = [
     "vllm==0.11.0 ; sys_platform == 'linux'",
     "peft",
-    "transformers",
+    "transformers==4.57.1",
     "datasets>=4.5.0",
     "python-dotenv",
 ]

From b0b878f35827d36ce2c82c70ee136aa36c5579e8 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 10:49:45 +0100
Subject: [PATCH 31/68] chore

---
 sweep/infer.py | 15 +++++++++++++++
 1 file changed, 15 insertions(+)

diff --git a/sweep/infer.py b/sweep/infer.py
index 7c7688d..2b525ef 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -25,6 +25,17 @@
 import sys
 from typing import Any
 
+# Must be set before torch/triton are imported.
+# vLLM 0.11.0 with enforce_eager=True disables vLLM's own compilation wrapper
+# (compilation level → NONE), but PyTorch still has @torch.compile on
+# get_masked_input_and_mask in vocab_parallel_embedding.py.  That function
+# tries to run inductor → triton, which compiles cuda_utils.c via gcc and
+# needs python3.12-dev headers not installed on this server.
+# TORCHDYNAMO_DISABLE=1 makes all @torch.compile decorators fall back to
+# eager execution.  With enforce_eager=True, vLLM's own aot_compile path is
+# also disabled, so TORCHDYNAMO_DISABLE doesn't break anything.
+os.environ["TORCHDYNAMO_DISABLE"] = "1"
+
 import torch
 from datasets import load_dataset
 from dotenv import load_dotenv
@@ -93,6 +104,10 @@ def vllm_generate(
         dtype="bfloat16",
         max_model_len=2048,
         tensor_parallel_size=n_gpus,
+        # Disable vLLM's torch.compile wrapper (compilation level → NONE).
+        # Combined with TORCHDYNAMO_DISABLE above this avoids all Triton
+        # compilation, which requires python3.12-dev headers not on this server.
+        enforce_eager=True,
     )
 
     lora_request = LoRARequest("adapter", 1, checkpoint)

From 042dd16a9e028c844c44577fe1aab6c19fc711c5 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 14:20:15 +0100
Subject: [PATCH 32/68] migrate to online vllm

---
 pyproject.toml             |   2 +
 sweep/README.md            |  59 ++++---
 sweep/infer.py             | 310 ++++++++++---------------------------
 sweep/infer/pyproject.toml |  21 ---
 sweep/master_config.yaml   |  16 +-
 sweep/sweep.py             | 121 +++++++++++----
 6 files changed, 226 insertions(+), 303 deletions(-)
 delete mode 100644 sweep/infer/pyproject.toml

diff --git a/pyproject.toml b/pyproject.toml
index 3c6adad..b68b2bc 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,8 @@ dependencies = [
     "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@v0.16.1 ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
+    "openai",
+    "requests",
     "huggingface-hub>=1.4.1",
     "typer>=0.9.0",
     "matplotlib>=3.10.8",
diff --git a/sweep/README.md b/sweep/README.md
index c9f6ac9..b75a420 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -3,19 +3,20 @@
 48 training runs × 3 epoch checkpoints = 144 data points across a grid of
 LoRA rank, learning rate, batch size, and target layer configurations.
 
-## Environments
+Validation loss is computed by axolotl after each epoch (stored in
+`trainer_state.json`). Completions are generated via a persistent vLLM
+Docker server on a dedicated GPU.
 
-Two separate uv environments are required to avoid the axolotl/vLLM
-dependency conflict:
+## GPU layout
 
-| Environment | Location | Purpose |
-|---|---|---|
-| Training | `.venv/` (repo root) | axolotl fine-tuning via `sweep.py` |
-| Inference | `sweep/infer/.venv/` | vLLM generation + PEFT loss via `infer.py` |
+| GPU | Role |
+|---|---|
+| `2` | axolotl training (`sweep.py`) |
+| `3` | vLLM Docker server (persistent, started once before sweep) |
 
 ## Setup on the H100
 
-**1. Clone the repo and install the training environment**
+**1. Clone the repo and install the environment**
 
 ```bash
 git clone <repo-url> && cd voice
@@ -23,14 +24,31 @@ uv sync
 source .venv/bin/activate
 ```
 
-**2. Install the inference environment**
+**2. Start the vLLM Docker server on GPU 3 (one-time)**
 
 ```bash
-uv sync --project sweep/infer
+docker run -d --name vllm-sweep \
+  --runtime nvidia \
+  -e NVIDIA_VISIBLE_DEVICES=3 \
+  --gpus device=3 \
+  -p 127.0.0.1:8000:8000 \
+  -v "$PWD/sweep/checkpoints:/checkpoints:ro" \
+  vllm/vllm-openai:v0.9.0 \
+  --model meta-llama/Llama-3.1-8B-Instruct \
+  --enable-lora \
+  --max-lora-rank 32 \
+  --dtype bfloat16 \
+  --max-model-len 2048 \
+  --port 8000
 ```
 
-This creates `sweep/infer/.venv/` with vLLM and PEFT. The sweep orchestrator
-calls this environment automatically for every inference pass.
+The container mounts `sweep/checkpoints` at `/checkpoints` (read-only).
+Wait for the server to be healthy before starting the sweep:
+
+```bash
+docker logs -f vllm-sweep   # wait for "Application startup complete"
+curl http://localhost:8000/health
+```
 
 **3. Set environment variables**
 
@@ -53,16 +71,17 @@ Start a tmux session so the sweep survives SSH disconnection:
 ```bash
 tmux new -s sweep
 cd sweep
-python sweep.py                    # default: GPUs 2,3
-python sweep.py --gpus 0           # single GPU
-python sweep.py --gpus 0,1,2,3    # all four GPUs
+python sweep.py          # GPU 2 for training, localhost:8000 for vLLM
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
 
-Training automatically uses `accelerate launch` for multi-GPU runs and plain
-`python -m axolotl.cli.train` for single-GPU runs.  vLLM inference uses
-`tensor_parallel_size` equal to the number of GPUs in all cases.
+If the vLLM server is on a different port or host, use `--vllm-url` and
+`--container-checkpoints-dir` to override:
+
+```bash
+python sweep.py --vllm-url http://localhost:8001 --container-checkpoints-dir /data/checkpoints
+```
 
 Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 
@@ -89,5 +108,5 @@ completes. Schema:
 | `lora_alpha` | LoRA alpha (= 2 × rank) |
 | `effective_batch_size` | Effective batch size |
 | `target_layers` | `attention` or `attention_mlp` |
-| `eval_loss` | Test NLL loss at this checkpoint |
-| `completions` | JSON list of generated responses on the test split |
+| `eval_loss` | Validation NLL loss (from axolotl trainer_state.json) |
+| `completions` | JSON list of generated responses on the validation split |
diff --git a/sweep/infer.py b/sweep/infer.py
index 2b525ef..c5a32cf 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -1,245 +1,77 @@
-"""Inference and test-loss script for a single fine-tuned checkpoint.
+"""Inference script for a single fine-tuned checkpoint.
 
-Generation uses vLLM with the LoRA adapter loaded via LoRARequest.  vLLM
-applies the model's built-in chat template and stops automatically on
-<|eot_id|>, so no manual EOS token handling is required.
+Sends completions requests to a persistent vLLM Docker server with the
+LoRA adapter loaded dynamically via the /v1/load_lora_adapter endpoint.
 
-Loss is computed with PyTorch/PEFT in a separate pass after the vLLM engine
-is torn down to reclaim GPU memory.
+Eval loss is read directly from axolotl's trainer_state.json (written
+during training with eval_strategy: epoch) — no loss recomputation here.
 
-Outputs a single JSON object to stdout::
+Outputs a JSON list of completion dicts to stdout::
 
-    {"eval_loss": float, "completions": [{messages, generated_response}, ...]}
+    [{"messages": [...], "generated_response": "..."}, ...]
 
 All progress logs go to stderr so stdout stays clean for sweep.py to parse.
 
 Usage::
 
-    python infer.py --checkpoint /path/to/checkpoint-300
+    python infer.py \\
+        --adapter-path /checkpoints/run_000_lr1e-4_r4_bs4_attention/checkpoint-300 \\
+        --vllm-url http://localhost:8000
 """
 
 import argparse
-import gc
 import json
 import os
 import sys
+from pathlib import Path
 from typing import Any
 
-# Must be set before torch/triton are imported.
-# vLLM 0.11.0 with enforce_eager=True disables vLLM's own compilation wrapper
-# (compilation level → NONE), but PyTorch still has @torch.compile on
-# get_masked_input_and_mask in vocab_parallel_embedding.py.  That function
-# tries to run inductor → triton, which compiles cuda_utils.c via gcc and
-# needs python3.12-dev headers not installed on this server.
-# TORCHDYNAMO_DISABLE=1 makes all @torch.compile decorators fall back to
-# eager execution.  With enforce_eager=True, vLLM's own aot_compile path is
-# also disabled, so TORCHDYNAMO_DISABLE doesn't break anything.
-os.environ["TORCHDYNAMO_DISABLE"] = "1"
-
-import torch
+import requests
 from datasets import load_dataset
 from dotenv import load_dotenv
-from peft import PeftModel
-from transformers import AutoModelForCausalLM, AutoTokenizer
-from vllm import LLM, SamplingParams
-from vllm.lora.request import LoRARequest
+from openai import OpenAI
 
 load_dotenv()
 
-BASE_MODEL = "meta-llama/Llama-3.1-8B-Instruct"
 DATASET = "AccelerateScience/bush-dataset"
 MAX_NEW_TOKENS = 1024
 TEMPERATURE = 0.7
-LOSS_BATCH_SIZE = 16
 
 
 def _log(msg: str) -> None:
     print(msg, file=sys.stderr, flush=True)
 
 
-def _apply_template(
-    tokenizer: Any,
-    messages: list[dict[str, Any]],
-    add_generation_prompt: bool,
-) -> list[int]:
-    """Apply chat template and return a flat list of token IDs.
+def _adapter_name(adapter_path: str) -> str:
+    """Derive a unique adapter name from the checkpoint path."""
+    return Path(adapter_path).name
 
-    :param tokenizer: tokenizer to apply the template with.
-    :param messages: list of message dicts with ``role`` and ``content`` keys.
-    :param add_generation_prompt: whether to append the generation prompt.
-    :returns: list of integer token IDs.
-    """
-    ids = tokenizer.apply_chat_template(
-        messages,
-        tokenize=True,
-        add_generation_prompt=add_generation_prompt,
-    )
-    if hasattr(ids, "input_ids"):
-        ids = ids.input_ids
-        if hasattr(ids, "tolist"):
-            ids = ids[0].tolist()
-    return list(ids)
-
-
-def vllm_generate(
-    checkpoint: str,
-    examples: list[dict[str, Any]],
-) -> list[str]:
-    """Run batched generation with vLLM and the LoRA adapter.
 
-    The vLLM engine is torn down and GPU memory freed before returning so
-    the subsequent PyTorch loss pass can load the model cleanly.
+def _load_validation_examples(hf_token: str) -> list[dict[str, Any]]:
+    """Load the dataset's native validation split.
 
-    :param checkpoint: path to the LoRA adapter checkpoint directory.
-    :param examples: list of dataset examples from the test split.
-    :returns: list of generated response strings, one per example.
+    :param hf_token: HuggingFace API token.
+    :returns: list of dataset examples.
     """
-    cuda_devices = os.environ.get("CUDA_VISIBLE_DEVICES", "0")
-    n_gpus = len([d for d in cuda_devices.split(",") if d.strip()])
-    _log(f"Loading vLLM engine on {n_gpus} GPU(s) (CUDA_VISIBLE_DEVICES={cuda_devices!r})")
-    llm = LLM(
-        model=BASE_MODEL,
-        enable_lora=True,
-        max_lora_rank=32,
-        dtype="bfloat16",
-        max_model_len=2048,
-        tensor_parallel_size=n_gpus,
-        # Disable vLLM's torch.compile wrapper (compilation level → NONE).
-        # Combined with TORCHDYNAMO_DISABLE above this avoids all Triton
-        # compilation, which requires python3.12-dev headers not on this server.
-        enforce_eager=True,
-    )
-
-    lora_request = LoRARequest("adapter", 1, checkpoint)
-    sampling_params = SamplingParams(
-        temperature=TEMPERATURE,
-        max_tokens=MAX_NEW_TOKENS,
-    )
-
-    # Strip the final assistant turn — the model generates it.
-    chat_prompts = [
-        [m for m in ex["messages"] if m["role"] != "assistant"]
-        for ex in examples
-    ]
-
-    _log(f"Generating {len(examples)} completions")
-    outputs = llm.chat(
-        chat_prompts,
-        sampling_params,
-        lora_request=lora_request,
-        use_tqdm=False,
-    )
-    responses = [o.outputs[0].text for o in outputs]
-
-    for resp in responses[:3]:
-        _log(f"  [sample] {resp!r}")
-
-    del llm
-    gc.collect()
-    torch.cuda.empty_cache()
-    _log("vLLM engine freed")
-
-    return responses
-
-
-def pytorch_loss(
-    checkpoint: str,
-    tokenizer: Any,
-    examples: list[dict[str, Any]],
-) -> float:
-    """Compute token-weighted NLL loss over assistant turns in batches.
-
-    Loss is computed only over assistant tokens; prompt and padding tokens
-    are masked with -100.
-
-    :param checkpoint: path to the LoRA adapter checkpoint directory.
-    :param tokenizer: tokenizer matching the base model.
-    :param examples: list of dataset examples from the test split.
-    :returns: token-weighted average NLL loss across all examples.
-    """
-    _log(f"Loading base model + LoRA from {checkpoint}")
-    base = AutoModelForCausalLM.from_pretrained(
-        BASE_MODEL,
-        torch_dtype=torch.bfloat16,
-        device_map="auto",
-    )
-    model = PeftModel.from_pretrained(base, checkpoint)
-    model.eval()
-
-    total_loss = 0.0
-    total_tokens = 0
-
-    for start in range(0, len(examples), LOSS_BATCH_SIZE):
-        batch = examples[start : start + LOSS_BATCH_SIZE]
-
-        full_ids_list = [
-            _apply_template(tokenizer, ex["messages"], add_generation_prompt=False)[:2048]
-            for ex in batch
-        ]
-        prompt_lens = [
-            len(
-                _apply_template(
-                    tokenizer, ex["messages"][:-1], add_generation_prompt=True
-                )
-            )
-            for ex in batch
-        ]
-        max_len = max(len(ids) for ids in full_ids_list)
-        pad_id = tokenizer.pad_token_id
-
-        input_ids = torch.zeros(len(batch), max_len, dtype=torch.long)
-        attention_mask = torch.zeros(len(batch), max_len, dtype=torch.long)
-        labels = torch.full((len(batch), max_len), -100, dtype=torch.long)
-
-        for i, (full_ids, p_len) in enumerate(zip(full_ids_list, prompt_lens)):
-            seq_len = len(full_ids)
-            input_ids[i, :seq_len] = torch.tensor(full_ids, dtype=torch.long)
-            input_ids[i, seq_len:] = pad_id
-            attention_mask[i, :seq_len] = 1
-            labels[i, p_len:seq_len] = torch.tensor(
-                full_ids[p_len:], dtype=torch.long
-            )
-
-        input_ids = input_ids.to(model.device)
-        attention_mask = attention_mask.to(model.device)
-        labels = labels.to(model.device)
-
-        with torch.no_grad():
-            logits = model(
-                input_ids=input_ids, attention_mask=attention_mask
-            ).logits
-
-        shift_logits = logits[..., :-1, :].contiguous()
-        shift_labels = labels[..., 1:].contiguous()
-        valid = shift_labels != -100
-        n_tokens = int(valid.sum().item())
-
-        if n_tokens > 0:
-            loss_val = torch.nn.functional.cross_entropy(
-                shift_logits.view(-1, shift_logits.size(-1))[valid.view(-1)],
-                shift_labels.view(-1)[valid.view(-1)],
-            ).item()
-            total_loss += loss_val * n_tokens
-            total_tokens += n_tokens
-
-        _log(
-            f"  [loss] {min(start + LOSS_BATCH_SIZE, len(examples))}"
-            f"/{len(examples)}"
-        )
-
-    return total_loss / total_tokens if total_tokens > 0 else float("nan")
+    ds = load_dataset(DATASET, split="validation", token=hf_token)
+    return list(ds)
 
 
 def main() -> None:
-    """Parse arguments, run generation then loss, print JSON.
+    """Load adapter, generate completions, output JSON list.
 
     :returns: None
     """
     parser = argparse.ArgumentParser()
     parser.add_argument(
-        "--checkpoint",
+        "--adapter-path",
         required=True,
-        help="Path to epoch checkpoint directory (LoRA adapter)",
+        help="Path to the LoRA adapter inside the Docker container",
+    )
+    parser.add_argument(
+        "--vllm-url",
+        default="http://localhost:8000",
+        help="Base URL of the vLLM OpenAI-compatible server",
     )
     args = parser.parse_args()
 
@@ -247,35 +79,59 @@ def main() -> None:
         os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
     )
 
-    _log(f"Loading tokenizer from {BASE_MODEL}")
-    tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, token=hf_token)
-    # Match the pad token axolotl sets during training.
-    tokenizer.pad_token = "<|eot_id|>"
-
-    _log(f"Loading test split from {DATASET}")
-    ds = load_dataset(DATASET, split="test", token=hf_token)
-    examples = list(ds)
-    _log(f"Loaded {len(examples)} examples")
-
-    _log("Generation pass")
-    responses = vllm_generate(args.checkpoint, examples)
-
-    _log("Loss pass")
-    eval_loss = pytorch_loss(args.checkpoint, tokenizer, examples)
-
-    completions: list[dict[str, Any]] = [
-        {
-            "messages": [
-                {"role": m["role"], "content": m["content"]}
-                for m in ex["messages"][:-1]
-            ],
-            "generated_response": response,
-        }
-        for ex, response in zip(examples, responses)
-    ]
-
-    _log(f"eval_loss={eval_loss:.4f}  n_completions={len(completions)}")
-    print(json.dumps({"eval_loss": eval_loss, "completions": completions}))
+    adapter_name = _adapter_name(args.adapter_path)
+    base_url = args.vllm_url.rstrip("/")
+
+    _log(f"Loading validation split from {DATASET}")
+    examples = _load_validation_examples(hf_token)
+    _log(f"Loaded {len(examples)} validation examples")
+
+    _log(f"Loading LoRA adapter: {adapter_name} from {args.adapter_path}")
+    resp = requests.post(
+        f"{base_url}/v1/load_lora_adapter",
+        json={"lora_name": adapter_name, "lora_path": args.adapter_path},
+        timeout=60,
+    )
+    resp.raise_for_status()
+    _log("Adapter loaded")
+
+    client = OpenAI(api_key="unused", base_url=f"{base_url}/v1")
+
+    completions: list[dict[str, Any]] = []
+    for i, ex in enumerate(examples):
+        # Strip the final assistant turn — the model generates it.
+        prompt_messages = [m for m in ex["messages"] if m["role"] != "assistant"]
+        response = client.chat.completions.create(
+            model=adapter_name,
+            messages=prompt_messages,
+            max_tokens=MAX_NEW_TOKENS,
+            temperature=TEMPERATURE,
+        )
+        generated = response.choices[0].message.content or ""
+        completions.append(
+            {
+                "messages": [
+                    {"role": m["role"], "content": m["content"]}
+                    for m in prompt_messages
+                ],
+                "generated_response": generated,
+            }
+        )
+        if i < 3:
+            _log(f"  [sample] {generated!r}")
+
+    _log(f"Generated {len(completions)} completions")
+
+    _log(f"Unloading LoRA adapter: {adapter_name}")
+    resp = requests.post(
+        f"{base_url}/v1/unload_lora_adapter",
+        json={"lora_name": adapter_name},
+        timeout=30,
+    )
+    resp.raise_for_status()
+    _log("Adapter unloaded")
+
+    print(json.dumps(completions))
 
 
 if __name__ == "__main__":
diff --git a/sweep/infer/pyproject.toml b/sweep/infer/pyproject.toml
deleted file mode 100644
index b05b2e0..0000000
--- a/sweep/infer/pyproject.toml
+++ /dev/null
@@ -1,21 +0,0 @@
-[project]
-name = "voice-infer"
-version = "0.1.0"
-description = "Inference environment for the pilot sweep (vLLM + PEFT)"
-requires-python = ">=3.12,<3.13"
-dependencies = [
-    "vllm==0.11.0 ; sys_platform == 'linux'",
-    "peft",
-    "transformers==4.57.1",
-    "datasets>=4.5.0",
-    "python-dotenv",
-]
-
-# vLLM only ships GPU wheels for Linux; enforce this so uv does not attempt
-# to resolve a CPU-only environment on the developer's machine.
-[tool.uv]
-required-environments = ["sys_platform == 'linux'"]
-# Use uv's own managed Python (not system Python) — uv bundles the full Python
-# distribution including dev headers, which Triton needs to compile cuda_utils.c.
-# System Python 3.12 on this host lacks python3.12-dev headers.
-python-preference = "managed"
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 9b9f9b8..18265c1 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -16,8 +16,8 @@
 # Grid: 4 ranks × 3 LRs × 2 batch sizes × 2 target layers = 48 training
 # runs → 144 data points.
 #
-# No validation during training. Test loss is computed post-training from
-# each epoch checkpoint and used for the NLL-style correlation analysis.
+# Validation loss is computed after each epoch via axolotl and stored in
+# trainer_state.json. sweep.py reads it from there — no post-training loss pass.
 # =============================================================================
 
 # --- Sweep grid -------------------------------------------------------------
@@ -65,12 +65,12 @@ axolotl:
   chat_template: tokenizer_default
   val_set_size: 0.0
 
-  # test_datasets is used only for the post-training loss pass (axolotl
-  # evaluate), not during training. Gives the single eval_loss per run
-  # used for the NLL-style correlation.
+  # Validation dataset — uses the dataset's native validation split.
+  # Axolotl uses this for per-epoch eval (eval_strategy: epoch), which
+  # writes eval_loss to trainer_state.json after each epoch.
   test_datasets:
     - path: AccelerateScience/bush-dataset
-      split: test
+      split: validation
       type: chat_template
       field_messages: messages
 
@@ -104,8 +104,8 @@ axolotl:
   warmup_ratio: 0.03
   weight_decay: 0.0
 
-  # No evaluation during training — test loss computed post-training only.
-  eval_strategy: "no"
+  # Evaluate on the validation split after each epoch.
+  eval_strategy: "epoch"
 
   # Keep all epoch checkpoints (up to 3) so sweep.py can run inference at
   # each epoch without re-training. 48 training runs × 3 epochs = 144 rows.
diff --git a/sweep/sweep.py b/sweep/sweep.py
index 449b299..73a9e97 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -2,16 +2,21 @@
 
 Reads master_config.yaml, expands the hyperparameter grid, and for each run:
   1. Writes a per-run axolotl config YAML
-  2. Trains with axolotl (always 3 epochs, saving a checkpoint each epoch)
-  3. For each epoch checkpoint: runs infer.py to get completions + test loss
+  2. Trains with axolotl (always 3 epochs, saving a checkpoint each epoch
+     and computing validation loss after each epoch via axolotl)
+  3. For each epoch checkpoint: runs infer.py to get completions via the
+     persistent vLLM Docker server; reads eval_loss from trainer_state.json
   4. Appends 3 rows to outputs/results.parquet
   5. Updates state.json for resumability
 
+The vLLM server must be running before sweep.py is started.  See README.md
+for the one-time Docker startup command.
+
 Usage (inside tmux on the H100)::
 
     python sweep.py
     python sweep.py --dry-run   # print grid without training
-    python sweep.py --device 0  # override GPU (default: 3)
+    python sweep.py --gpus 2    # override training GPU (default: 2)
 """
 
 import argparse
@@ -38,11 +43,6 @@
 MASTER_CONFIG = SWEEP_DIR / "master_config.yaml"
 LOG_FILE = SWEEP_DIR / "sweep.log"
 
-# Python executable for the inference environment (vLLM + PEFT).
-# Create it once on the target machine with:
-#   uv sync --project sweep/infer
-INFER_PYTHON = SWEEP_DIR / "infer" / ".venv" / "bin" / "python"
-
 # Fixed micro batch size; gradient_accumulation_steps derived from
 # the effective_batch_size sweep axis.
 MICRO_BATCH_SIZE: int = 4
@@ -183,6 +183,28 @@ def find_epoch_checkpoints(run_name: str) -> dict[int, Path]:
     return epoch_map
 
 
+# ---------------------------------------------------------------------------
+# Eval loss (from axolotl's trainer_state.json)
+# ---------------------------------------------------------------------------
+def get_eval_loss(checkpoint_path: Path, epoch: int) -> float:
+    """Read the validation loss for an epoch from trainer_state.json.
+
+    Axolotl writes eval_loss to log_history after each epoch when
+    eval_strategy is "epoch".  We scan backwards to find the entry
+    whose epoch field matches.
+
+    :param checkpoint_path: path to the epoch checkpoint directory.
+    :param epoch: the epoch number to look up.
+    :returns: eval_loss float, or nan if not found.
+    """
+    state_file = checkpoint_path / "trainer_state.json"
+    trainer_state = json.loads(state_file.read_text())
+    for entry in reversed(trainer_state.get("log_history", [])):
+        if round(entry.get("epoch", 0)) == epoch and "eval_loss" in entry:
+            return float(entry["eval_loss"])
+    return float("nan")
+
+
 # ---------------------------------------------------------------------------
 # Training
 # ---------------------------------------------------------------------------
@@ -217,20 +239,29 @@ def run_training(config_path: Path) -> None:
 
 
 # ---------------------------------------------------------------------------
-# Inference + loss
+# Inference (via persistent vLLM Docker server)
 # ---------------------------------------------------------------------------
-def run_inference(checkpoint_path: Path) -> dict[str, Any]:
-    """Run infer.py for a checkpoint and return eval_loss + completions.
+def run_inference(
+    checkpoint_path: Path,
+    container_checkpoint_path: str,
+    vllm_url: str,
+) -> list[dict[str, Any]]:
+    """Run infer.py for a checkpoint and return the list of completions.
 
-    :param checkpoint_path: path to the epoch checkpoint directory.
-    :returns: dict with ``eval_loss`` (float) and ``completions`` (list).
+    :param checkpoint_path: host-side path to the checkpoint (for logging).
+    :param container_checkpoint_path: path inside the Docker container.
+    :param vllm_url: base URL of the persistent vLLM server.
+    :returns: list of completion dicts with ``messages`` and
+        ``generated_response`` keys.
     """
     proc = subprocess.run(
         [
-            str(INFER_PYTHON),
+            sys.executable,
             str(SWEEP_DIR / "infer.py"),
-            "--checkpoint",
-            str(checkpoint_path),
+            "--adapter-path",
+            container_checkpoint_path,
+            "--vllm-url",
+            vllm_url,
         ],
         capture_output=True,
         text=True,
@@ -242,7 +273,7 @@ def run_inference(checkpoint_path: Path) -> dict[str, Any]:
         raise RuntimeError(
             f"infer.py exited with code {proc.returncode}"
         )
-    return cast(dict[str, Any], json.loads(proc.stdout))
+    return cast(list[dict[str, Any]], json.loads(proc.stdout))
 
 
 # ---------------------------------------------------------------------------
@@ -267,17 +298,37 @@ def append_results(rows: list[dict[str, Any]]) -> None:
 # ---------------------------------------------------------------------------
 # Per-run helpers (private — extracted to keep main() complexity low)
 # ---------------------------------------------------------------------------
+def _container_checkpoint_path(
+    checkpoint_path: Path,
+    checkpoints_dir: Path,
+    container_checkpoints_dir: str,
+) -> str:
+    """Translate a host checkpoint path to its container-side equivalent.
+
+    :param checkpoint_path: absolute host path to the checkpoint.
+    :param checkpoints_dir: host-side checkpoints root directory.
+    :param container_checkpoints_dir: container-side checkpoints root.
+    :returns: absolute path string as seen inside the Docker container.
+    """
+    relative = checkpoint_path.relative_to(checkpoints_dir)
+    return str(Path(container_checkpoints_dir) / relative)
+
+
 def _collect_rows(
     run: dict[str, Any],
     epoch_map: dict[int, Path],
+    vllm_url: str,
+    container_checkpoints_dir: str,
 ) -> list[dict[str, Any]]:
     rows: list[dict[str, Any]] = []
     for epoch in sorted(epoch_map):
         ckpt_path = epoch_map[epoch]
+        container_path = _container_checkpoint_path(
+            ckpt_path, CHECKPOINTS_DIR, container_checkpoints_dir
+        )
         log.info(f"  [INFER] epoch {epoch} — {ckpt_path.name}")
-        result = run_inference(ckpt_path)
-        loss_val = result["eval_loss"]
-        n_comp = len(result["completions"])
+        completions = run_inference(ckpt_path, container_path, vllm_url)
+        eval_loss = get_eval_loss(ckpt_path, epoch)
         rows.append(
             {
                 "run_name": run["run_name"],
@@ -287,11 +338,13 @@ def _collect_rows(
                 "lora_alpha": run["lora_alpha"],
                 "effective_batch_size": run["effective_batch_size"],
                 "target_layers": run["target_layers_name"],
-                "eval_loss": loss_val,
-                "completions": json.dumps(result["completions"]),
+                "eval_loss": eval_loss,
+                "completions": json.dumps(completions),
             }
         )
-        log.info(f"    eval_loss={loss_val:.4f}  n_completions={n_comp}")
+        log.info(
+            f"    eval_loss={eval_loss:.4f}  n_completions={len(completions)}"
+        )
     return rows
 
 
@@ -299,6 +352,8 @@ def _execute_run(
     run: dict[str, Any],
     axolotl_base: dict[str, Any],
     state: dict[str, Any],
+    vllm_url: str,
+    container_checkpoints_dir: str,
 ) -> None:
     name = run["run_name"]
     try:
@@ -308,7 +363,7 @@ def _execute_run(
         if not epoch_map:
             raise RuntimeError("No epoch checkpoints found after training")
         log.info(f"Found checkpoints for epochs: {sorted(epoch_map)}")
-        rows = _collect_rows(run, epoch_map)
+        rows = _collect_rows(run, epoch_map, vllm_url, container_checkpoints_dir)
         append_results(rows)
         state["completed"].append(name)
         state["failed"].pop(name, None)
@@ -336,8 +391,18 @@ def main() -> None:
     )
     parser.add_argument(
         "--gpus",
-        default="2,3",
-        help="Comma-separated CUDA device indices (default: 2,3)",
+        default="2",
+        help="Comma-separated CUDA device indices for training (default: 2)",
+    )
+    parser.add_argument(
+        "--vllm-url",
+        default="http://localhost:8000",
+        help="Base URL of the persistent vLLM Docker server (default: http://localhost:8000)",
+    )
+    parser.add_argument(
+        "--container-checkpoints-dir",
+        default="/checkpoints",
+        help="Checkpoints directory as mounted inside the vLLM container (default: /checkpoints)",
     )
     args = parser.parse_args()
 
@@ -379,7 +444,9 @@ def main() -> None:
         log.info(f"\n{'=' * 60}")
         log.info(f"[START] {name}")
         log.info(f"{'=' * 60}")
-        _execute_run(run, axolotl_base, state)
+        _execute_run(
+            run, axolotl_base, state, args.vllm_url, args.container_checkpoints_dir
+        )
 
     n_done = len(state["completed"])
     log.info(f"\nSweep complete: {n_done}/{n_runs} runs succeeded.")

From 5e24704c27f1cd955c99f4b0d3fcdf9637b2aea9 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 15:10:25 +0100
Subject: [PATCH 33/68] update readme

---
 sweep/README.md | 15 ++++++++++++++-
 1 file changed, 14 insertions(+), 1 deletion(-)

diff --git a/sweep/README.md b/sweep/README.md
index b75a420..ca4d11d 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -24,7 +24,7 @@ uv sync
 source .venv/bin/activate
 ```
 
-**2. Start the vLLM Docker server on GPU 3 (one-time)**
+**2. Start the vLLM Docker server on GPU 3 (one time, from project root)**
 
 ```bash
 docker run -d --name vllm-sweep \
@@ -32,6 +32,9 @@ docker run -d --name vllm-sweep \
   -e NVIDIA_VISIBLE_DEVICES=3 \
   --gpus device=3 \
   -p 127.0.0.1:8000:8000 \
+  -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN \
+  -e VLLM_ALLOW_RUNTIME_LORA_UPDATING=true \
+  -v ~/.cache/huggingface:/root/.cache/huggingface \
   -v "$PWD/sweep/checkpoints:/checkpoints:ro" \
   vllm/vllm-openai:v0.9.0 \
   --model meta-llama/Llama-3.1-8B-Instruct \
@@ -42,6 +45,10 @@ docker run -d --name vllm-sweep \
   --port 8000
 ```
 
+`VLLM_ALLOW_RUNTIME_LORA_UPDATING=true` enables the `/v1/load_lora_adapter`
+and `/v1/unload_lora_adapter` endpoints, which are disabled by default in
+vLLM 0.9.0. Without it, `infer.py` will get a 404.
+
 The container mounts `sweep/checkpoints` at `/checkpoints` (read-only).
 Wait for the server to be healthy before starting the sweep:
 
@@ -50,6 +57,12 @@ docker logs -f vllm-sweep   # wait for "Application startup complete"
 curl http://localhost:8000/health
 ```
 
+If you need to restart the container (e.g. after a crash), remove it first:
+
+```bash
+docker stop vllm-sweep && docker rm vllm-sweep
+```
+
 **3. Set environment variables**
 
 ```bash

From 733a9eaafe918595e5a62219dc51066db7c17d7c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 15:28:46 +0100
Subject: [PATCH 34/68] 1

---
 sweep/infer.py | 70 +++++++++++++++++++++++++++++++-------------------
 1 file changed, 44 insertions(+), 26 deletions(-)

diff --git a/sweep/infer.py b/sweep/infer.py
index c5a32cf..63453ee 100644
--- a/sweep/infer.py
+++ b/sweep/infer.py
@@ -3,6 +3,9 @@
 Sends completions requests to a persistent vLLM Docker server with the
 LoRA adapter loaded dynamically via the /v1/load_lora_adapter endpoint.
 
+All validation examples are dispatched concurrently so vLLM can batch them
+server-side and fully utilise the GPU.
+
 Eval loss is read directly from axolotl's trainer_state.json (written
 during training with eval_strategy: epoch) — no loss recomputation here.
 
@@ -20,6 +23,7 @@
 """
 
 import argparse
+import asyncio
 import json
 import os
 import sys
@@ -29,7 +33,7 @@
 import requests
 from datasets import load_dataset
 from dotenv import load_dotenv
-from openai import OpenAI
+from openai import AsyncOpenAI
 
 load_dotenv()
 
@@ -57,8 +61,42 @@ def _load_validation_examples(hf_token: str) -> list[dict[str, Any]]:
     return list(ds)
 
 
+async def _generate_all(
+    client: AsyncOpenAI,
+    adapter_name: str,
+    examples: list[dict[str, Any]],
+) -> list[dict[str, Any]]:
+    """Dispatch all examples concurrently and collect completions in order.
+
+    :param client: async OpenAI client pointed at the vLLM server.
+    :param adapter_name: registered LoRA adapter name to use as the model.
+    :param examples: validation examples to generate completions for.
+    :returns: list of completion dicts preserving input order.
+    """
+    async def _one(ex: dict[str, Any]) -> dict[str, Any]:
+        prompt_messages = [
+            m for m in ex["messages"] if m["role"] != "assistant"
+        ]
+        response = await client.chat.completions.create(
+            model=adapter_name,
+            messages=prompt_messages,
+            max_tokens=MAX_NEW_TOKENS,
+            temperature=TEMPERATURE,
+        )
+        generated = response.choices[0].message.content or ""
+        return {
+            "messages": [
+                {"role": m["role"], "content": m["content"]}
+                for m in prompt_messages
+            ],
+            "generated_response": generated,
+        }
+
+    return list(await asyncio.gather(*[_one(ex) for ex in examples]))
+
+
 def main() -> None:
-    """Load adapter, generate completions, output JSON list.
+    """Load adapter, generate completions concurrently, output JSON list.
 
     :returns: None
     """
@@ -95,31 +133,11 @@ def main() -> None:
     resp.raise_for_status()
     _log("Adapter loaded")
 
-    client = OpenAI(api_key="unused", base_url=f"{base_url}/v1")
-
-    completions: list[dict[str, Any]] = []
-    for i, ex in enumerate(examples):
-        # Strip the final assistant turn — the model generates it.
-        prompt_messages = [m for m in ex["messages"] if m["role"] != "assistant"]
-        response = client.chat.completions.create(
-            model=adapter_name,
-            messages=prompt_messages,
-            max_tokens=MAX_NEW_TOKENS,
-            temperature=TEMPERATURE,
-        )
-        generated = response.choices[0].message.content or ""
-        completions.append(
-            {
-                "messages": [
-                    {"role": m["role"], "content": m["content"]}
-                    for m in prompt_messages
-                ],
-                "generated_response": generated,
-            }
-        )
-        if i < 3:
-            _log(f"  [sample] {generated!r}")
+    client = AsyncOpenAI(api_key="unused", base_url=f"{base_url}/v1")
+    completions = asyncio.run(_generate_all(client, adapter_name, examples))
 
+    for comp in completions[:3]:
+        _log(f"  [sample] {comp['generated_response']!r}")
     _log(f"Generated {len(completions)} completions")
 
     _log(f"Unloading LoRA adapter: {adapter_name}")

From 54213f60b553a6deb7a5c137a31d9e106e5d95cb Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 19:27:07 +0100
Subject: [PATCH 35/68] refactor(sweep): replace vLLM inference with in-process
 TrainerCallback

Remove infer.py and all vLLM/Docker machinery. Add EvalCompletionsCallback
which generates validation completions at epoch end using the live in-memory
model (T=1.0, no truncation), eliminating any train/inference distribution
mismatch. sweep.py reads completions written to disk by the callback instead
of orchestrating a separate inference subprocess. Output format is unchanged.
---
 pyproject.toml           |   4 +-
 sweep/callbacks.py       | 186 +++++++++++++++++++++++++++++++++++
 sweep/infer.py           | 156 ------------------------------
 sweep/master_config.yaml |  13 ++-
 sweep/sweep.py           | 202 ++++++++++-----------------------------
 5 files changed, 248 insertions(+), 313 deletions(-)
 create mode 100644 sweep/callbacks.py
 delete mode 100644 sweep/infer.py

diff --git a/pyproject.toml b/pyproject.toml
index b68b2bc..f1c4555 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,9 +9,7 @@ dependencies = [
     "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@v0.16.1 ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
-    "openai",
-    "requests",
-    "huggingface-hub>=1.4.1",
+"huggingface-hub>=1.4.1",
     "typer>=0.9.0",
     "matplotlib>=3.10.8",
     "pandas",
diff --git a/sweep/callbacks.py b/sweep/callbacks.py
new file mode 100644
index 0000000..3f1469d
--- /dev/null
+++ b/sweep/callbacks.py
@@ -0,0 +1,186 @@
+"""Axolotl TrainerCallback that generates validation completions at each epoch end.
+
+Registered via the axolotl ``callbacks`` YAML key — instantiated with no
+arguments. All configuration is sourced from environment variables or the
+TrainingArguments passed at runtime.
+
+Validation examples are loaded once at train-begin and cached. At each epoch
+end the in-memory model generates completions in left-padded batches, writes
+them to ``{output_dir}/completions_epoch_{N}.json``, and uploads an artifact
+to W&B named ``{run_name}-epoch{N}-Results``.
+
+The output format is identical to the previous vLLM inference pipeline::
+
+    [{"messages": [...], "generated_response": "..."}, ...]
+"""
+
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any
+
+import torch
+import wandb
+from datasets import load_dataset
+from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
+
+log = logging.getLogger(__name__)
+
+DATASET: str = "AccelerateScience/bush-dataset"
+INFERENCE_BATCH_SIZE: int = 8
+
+# Sampling parameters agreed for all experiments:
+# T=1.0 with no truncation samples from the exact learned distribution,
+# eliminating any gap between the training and inference distributions.
+GENERATION_CONFIG: dict[str, Any] = {
+    "do_sample": True,
+    "temperature": 1.0,
+    "top_p": 1.0,
+    "top_k": 0,
+    "repetition_penalty": 1.0,
+    "max_new_tokens": 1024,
+}
+
+
+class EvalCompletionsCallback(TrainerCallback):
+    """Generate and log validation completions at each epoch end.
+
+    Designed to be zero-argument so it can be registered directly in the
+    axolotl YAML as ``sweep.callbacks.EvalCompletionsCallback``.
+    """
+
+    def __init__(self) -> None:
+        """Initialise with an empty example cache."""
+        self._examples: list[dict[str, Any]] = []
+
+    def on_train_begin(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        **kwargs: Any,
+    ) -> None:
+        """Load and cache the validation split once before training starts.
+
+        :param args: HF TrainingArguments.
+        :param state: current TrainerState.
+        :param control: TrainerControl object.
+        :returns: None
+        """
+        hf_token = os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN")
+        ds = load_dataset(DATASET, split="validation", token=hf_token)
+        # Strip assistant turns — only system + user messages form the prompt.
+        self._examples = [
+            {"messages": [m for m in ex["messages"] if m["role"] != "assistant"]}
+            for ex in ds
+        ]
+        log.info(
+            "EvalCompletionsCallback: loaded %d validation examples",
+            len(self._examples),
+        )
+
+    def on_epoch_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        model: Any = None,
+        tokenizer: Any = None,
+        **kwargs: Any,
+    ) -> None:
+        """Generate completions for all validation examples and persist results.
+
+        Runs only on the main process (rank 0) in distributed training.
+
+        :param args: HF TrainingArguments.
+        :param state: TrainerState — ``state.epoch`` gives the current epoch.
+        :param control: TrainerControl object.
+        :param model: the live model (already on the correct device).
+        :param tokenizer: the tokenizer associated with the model.
+        :returns: None
+        """
+        if args.process_index != 0:
+            return
+
+        epoch = round(state.epoch)
+        log.info(
+            "EvalCompletionsCallback: generating completions for epoch %d", epoch
+        )
+
+        model.eval()
+        completions: list[dict[str, Any]] = []
+        for i in range(0, len(self._examples), INFERENCE_BATCH_SIZE):
+            batch = self._examples[i : i + INFERENCE_BATCH_SIZE]
+            completions.extend(_generate_batch(batch, model, tokenizer))
+        model.train()
+
+        output_dir = Path(args.output_dir)
+        out_path = output_dir / f"completions_epoch_{epoch}.json"
+        out_path.write_text(json.dumps(completions))
+        log.info(
+            "EvalCompletionsCallback: wrote %d completions → %s",
+            len(completions),
+            out_path,
+        )
+
+        if wandb.run is not None:
+            artifact = wandb.Artifact(
+                f"{wandb.run.name}-epoch{epoch}-Results",
+                type="completions",
+            )
+            artifact.add_file(str(out_path))
+            wandb.log_artifact(artifact)
+
+
+def _generate_batch(
+    batch: list[dict[str, Any]],
+    model: Any,
+    tokenizer: Any,
+) -> list[dict[str, Any]]:
+    """Generate completions for one batch using left-padded inputs.
+
+    :param batch: list of examples, each with a ``messages`` list
+        (system + user only — no assistant turn).
+    :param model: the current model.
+    :param tokenizer: the current tokenizer.
+    :returns: list of completion dicts with ``messages`` and
+        ``generated_response`` keys.
+    """
+    encoded = [
+        tokenizer.apply_chat_template(
+            ex["messages"],
+            return_tensors="pt",
+            add_generation_prompt=True,
+        ).squeeze(0)  # (seq_len,)
+        for ex in batch
+    ]
+
+    # Left-pad all sequences to the length of the longest in the batch.
+    max_len = max(t.shape[0] for t in encoded)
+    pad_id = tokenizer.pad_token_id
+    input_ids = torch.full(
+        (len(batch), max_len), pad_id, dtype=torch.long, device=model.device
+    )
+    attention_mask = torch.zeros(
+        (len(batch), max_len), dtype=torch.long, device=model.device
+    )
+    for j, t in enumerate(encoded):
+        start = max_len - t.shape[0]
+        input_ids[j, start:] = t.to(model.device)
+        attention_mask[j, start:] = 1
+
+    with torch.no_grad():
+        out = model.generate(
+            input_ids,
+            attention_mask=attention_mask,
+            **GENERATION_CONFIG,
+        )
+
+    results = []
+    for j, ex in enumerate(batch):
+        # out includes the (padded) prompt tokens; skip past them.
+        new_tokens = out[j, max_len:]
+        generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
+        results.append({"messages": ex["messages"], "generated_response": generated})
+    return results
diff --git a/sweep/infer.py b/sweep/infer.py
deleted file mode 100644
index 63453ee..0000000
--- a/sweep/infer.py
+++ /dev/null
@@ -1,156 +0,0 @@
-"""Inference script for a single fine-tuned checkpoint.
-
-Sends completions requests to a persistent vLLM Docker server with the
-LoRA adapter loaded dynamically via the /v1/load_lora_adapter endpoint.
-
-All validation examples are dispatched concurrently so vLLM can batch them
-server-side and fully utilise the GPU.
-
-Eval loss is read directly from axolotl's trainer_state.json (written
-during training with eval_strategy: epoch) — no loss recomputation here.
-
-Outputs a JSON list of completion dicts to stdout::
-
-    [{"messages": [...], "generated_response": "..."}, ...]
-
-All progress logs go to stderr so stdout stays clean for sweep.py to parse.
-
-Usage::
-
-    python infer.py \\
-        --adapter-path /checkpoints/run_000_lr1e-4_r4_bs4_attention/checkpoint-300 \\
-        --vllm-url http://localhost:8000
-"""
-
-import argparse
-import asyncio
-import json
-import os
-import sys
-from pathlib import Path
-from typing import Any
-
-import requests
-from datasets import load_dataset
-from dotenv import load_dotenv
-from openai import AsyncOpenAI
-
-load_dotenv()
-
-DATASET = "AccelerateScience/bush-dataset"
-MAX_NEW_TOKENS = 1024
-TEMPERATURE = 0.7
-
-
-def _log(msg: str) -> None:
-    print(msg, file=sys.stderr, flush=True)
-
-
-def _adapter_name(adapter_path: str) -> str:
-    """Derive a unique adapter name from the checkpoint path."""
-    return Path(adapter_path).name
-
-
-def _load_validation_examples(hf_token: str) -> list[dict[str, Any]]:
-    """Load the dataset's native validation split.
-
-    :param hf_token: HuggingFace API token.
-    :returns: list of dataset examples.
-    """
-    ds = load_dataset(DATASET, split="validation", token=hf_token)
-    return list(ds)
-
-
-async def _generate_all(
-    client: AsyncOpenAI,
-    adapter_name: str,
-    examples: list[dict[str, Any]],
-) -> list[dict[str, Any]]:
-    """Dispatch all examples concurrently and collect completions in order.
-
-    :param client: async OpenAI client pointed at the vLLM server.
-    :param adapter_name: registered LoRA adapter name to use as the model.
-    :param examples: validation examples to generate completions for.
-    :returns: list of completion dicts preserving input order.
-    """
-    async def _one(ex: dict[str, Any]) -> dict[str, Any]:
-        prompt_messages = [
-            m for m in ex["messages"] if m["role"] != "assistant"
-        ]
-        response = await client.chat.completions.create(
-            model=adapter_name,
-            messages=prompt_messages,
-            max_tokens=MAX_NEW_TOKENS,
-            temperature=TEMPERATURE,
-        )
-        generated = response.choices[0].message.content or ""
-        return {
-            "messages": [
-                {"role": m["role"], "content": m["content"]}
-                for m in prompt_messages
-            ],
-            "generated_response": generated,
-        }
-
-    return list(await asyncio.gather(*[_one(ex) for ex in examples]))
-
-
-def main() -> None:
-    """Load adapter, generate completions concurrently, output JSON list.
-
-    :returns: None
-    """
-    parser = argparse.ArgumentParser()
-    parser.add_argument(
-        "--adapter-path",
-        required=True,
-        help="Path to the LoRA adapter inside the Docker container",
-    )
-    parser.add_argument(
-        "--vllm-url",
-        default="http://localhost:8000",
-        help="Base URL of the vLLM OpenAI-compatible server",
-    )
-    args = parser.parse_args()
-
-    hf_token = (
-        os.environ.get("HF_TOKEN") or os.environ.get("HUGGINGFACE_TOKEN") or ""
-    )
-
-    adapter_name = _adapter_name(args.adapter_path)
-    base_url = args.vllm_url.rstrip("/")
-
-    _log(f"Loading validation split from {DATASET}")
-    examples = _load_validation_examples(hf_token)
-    _log(f"Loaded {len(examples)} validation examples")
-
-    _log(f"Loading LoRA adapter: {adapter_name} from {args.adapter_path}")
-    resp = requests.post(
-        f"{base_url}/v1/load_lora_adapter",
-        json={"lora_name": adapter_name, "lora_path": args.adapter_path},
-        timeout=60,
-    )
-    resp.raise_for_status()
-    _log("Adapter loaded")
-
-    client = AsyncOpenAI(api_key="unused", base_url=f"{base_url}/v1")
-    completions = asyncio.run(_generate_all(client, adapter_name, examples))
-
-    for comp in completions[:3]:
-        _log(f"  [sample] {comp['generated_response']!r}")
-    _log(f"Generated {len(completions)} completions")
-
-    _log(f"Unloading LoRA adapter: {adapter_name}")
-    resp = requests.post(
-        f"{base_url}/v1/unload_lora_adapter",
-        json={"lora_name": adapter_name},
-        timeout=30,
-    )
-    resp.raise_for_status()
-    _log("Adapter unloaded")
-
-    print(json.dumps(completions))
-
-
-if __name__ == "__main__":
-    main()
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 18265c1..5f3e9c0 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -10,9 +10,10 @@
 # micro_batch_size, gradient_accumulation_steps, output_dir, wandb_name)
 # on top of the axolotl section below.
 #
-# Epoch checkpoints are reused: each run trains to epoch 3 and saves a
-# checkpoint after each epoch. sweep.py runs inference + eval at each
-# checkpoint, yielding 3 data points per training run without re-training.
+# EvalCompletionsCallback runs inside the training process at each epoch end,
+# generating completions on the validation split using the live in-memory model.
+# This eliminates any train/inference distribution mismatch. Completions are
+# written to {output_dir}/completions_epoch_{N}.json and uploaded to W&B.
 # Grid: 4 ranks × 3 LRs × 2 batch sizes × 2 target layers = 48 training
 # runs → 144 data points.
 #
@@ -112,6 +113,12 @@ axolotl:
   save_strategy: epoch
   save_total_limit: 3
 
+  # Callback — generates validation completions at each epoch end using the
+  # live in-memory model. Requires sweep/ to be on PYTHONPATH (run from repo
+  # root, or set PYTHONPATH=. before launching axolotl).
+  callbacks:
+    - sweep.callbacks.EvalCompletionsCallback
+
   # W&B — wandb_name injected per run by sweep.py.
   use_wandb: true
   wandb_project: VOICE
diff --git a/sweep/sweep.py b/sweep/sweep.py
index 73a9e97..720d908 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -2,16 +2,13 @@
 
 Reads master_config.yaml, expands the hyperparameter grid, and for each run:
   1. Writes a per-run axolotl config YAML
-  2. Trains with axolotl (always 3 epochs, saving a checkpoint each epoch
-     and computing validation loss after each epoch via axolotl)
-  3. For each epoch checkpoint: runs infer.py to get completions via the
-     persistent vLLM Docker server; reads eval_loss from trainer_state.json
+  2. Trains with axolotl (always 3 epochs, saving a checkpoint and computing
+     validation loss after each epoch via axolotl)
+  3. Reads completions written by EvalCompletionsCallback (one JSON file per
+     epoch, produced during training) and eval_loss from trainer_state.json
   4. Appends 3 rows to outputs/results.parquet
   5. Updates state.json for resumability
 
-The vLLM server must be running before sweep.py is started.  See README.md
-for the one-time Docker startup command.
-
 Usage (inside tmux on the H100)::
 
     python sweep.py
@@ -162,47 +159,26 @@ def write_run_config(
     return config_path
 
 
-# ---------------------------------------------------------------------------
-# Checkpoint discovery
-# ---------------------------------------------------------------------------
-def find_epoch_checkpoints(run_name: str) -> dict[int, Path]:
-    """Map epoch numbers to checkpoint paths via trainer_state.json.
-
-    :param run_name: name of the run, used to locate the checkpoint dir.
-    :returns: dict mapping epoch int to the corresponding checkpoint Path.
-    """
-    checkpoint_dir = CHECKPOINTS_DIR / run_name
-    epoch_map: dict[int, Path] = {}
-    for ckpt in sorted(checkpoint_dir.glob("checkpoint-*")):
-        state_file = ckpt / "trainer_state.json"
-        if not state_file.exists():
-            continue
-        trainer_state = json.loads(state_file.read_text())
-        epoch = round(trainer_state["epoch"])
-        epoch_map[epoch] = ckpt
-    return epoch_map
-
-
 # ---------------------------------------------------------------------------
 # Eval loss (from axolotl's trainer_state.json)
 # ---------------------------------------------------------------------------
-def get_eval_loss(checkpoint_path: Path, epoch: int) -> float:
-    """Read the validation loss for an epoch from trainer_state.json.
+def _get_all_eval_losses(output_dir: Path) -> dict[int, float]:
+    """Read per-epoch validation losses from trainer_state.json.
 
-    Axolotl writes eval_loss to log_history after each epoch when
-    eval_strategy is "epoch".  We scan backwards to find the entry
-    whose epoch field matches.
+    HF Trainer writes this file to the run output directory after training.
 
-    :param checkpoint_path: path to the epoch checkpoint directory.
-    :param epoch: the epoch number to look up.
-    :returns: eval_loss float, or nan if not found.
+    :param output_dir: root output directory for the run.
+    :returns: dict mapping epoch int to eval_loss float.
     """
-    state_file = checkpoint_path / "trainer_state.json"
+    state_file = output_dir / "trainer_state.json"
+    if not state_file.exists():
+        return {}
     trainer_state = json.loads(state_file.read_text())
-    for entry in reversed(trainer_state.get("log_history", [])):
-        if round(entry.get("epoch", 0)) == epoch and "eval_loss" in entry:
-            return float(entry["eval_loss"])
-    return float("nan")
+    losses: dict[int, float] = {}
+    for entry in trainer_state.get("log_history", []):
+        if "eval_loss" in entry:
+            losses[round(entry["epoch"])] = float(entry["eval_loss"])
+    return losses
 
 
 # ---------------------------------------------------------------------------
@@ -226,7 +202,7 @@ def run_training(config_path: Path) -> None:
         cmd = [
             sys.executable, "-m", "accelerate.commands.launch",
             f"--num_processes={n_gpus}",
-            "--mixed_precision=no",   # axolotl/HF Trainer handles bf16 itself
+            "--mixed_precision=no",
             "--dynamo_backend=no",
             "-m", "axolotl.cli.train",
             str(config_path),
@@ -238,44 +214,6 @@ def run_training(config_path: Path) -> None:
     subprocess.run(cmd, check=True)
 
 
-# ---------------------------------------------------------------------------
-# Inference (via persistent vLLM Docker server)
-# ---------------------------------------------------------------------------
-def run_inference(
-    checkpoint_path: Path,
-    container_checkpoint_path: str,
-    vllm_url: str,
-) -> list[dict[str, Any]]:
-    """Run infer.py for a checkpoint and return the list of completions.
-
-    :param checkpoint_path: host-side path to the checkpoint (for logging).
-    :param container_checkpoint_path: path inside the Docker container.
-    :param vllm_url: base URL of the persistent vLLM server.
-    :returns: list of completion dicts with ``messages`` and
-        ``generated_response`` keys.
-    """
-    proc = subprocess.run(
-        [
-            sys.executable,
-            str(SWEEP_DIR / "infer.py"),
-            "--adapter-path",
-            container_checkpoint_path,
-            "--vllm-url",
-            vllm_url,
-        ],
-        capture_output=True,
-        text=True,
-    )
-    if proc.stderr:
-        for line in proc.stderr.strip().splitlines():
-            log.info(f"  [infer] {line}")
-    if proc.returncode != 0:
-        raise RuntimeError(
-            f"infer.py exited with code {proc.returncode}"
-        )
-    return cast(list[dict[str, Any]], json.loads(proc.stdout))
-
-
 # ---------------------------------------------------------------------------
 # Output
 # ---------------------------------------------------------------------------
@@ -296,74 +234,48 @@ def append_results(rows: list[dict[str, Any]]) -> None:
 
 
 # ---------------------------------------------------------------------------
-# Per-run helpers (private — extracted to keep main() complexity low)
+# Per-run execution
 # ---------------------------------------------------------------------------
-def _container_checkpoint_path(
-    checkpoint_path: Path,
-    checkpoints_dir: Path,
-    container_checkpoints_dir: str,
-) -> str:
-    """Translate a host checkpoint path to its container-side equivalent.
-
-    :param checkpoint_path: absolute host path to the checkpoint.
-    :param checkpoints_dir: host-side checkpoints root directory.
-    :param container_checkpoints_dir: container-side checkpoints root.
-    :returns: absolute path string as seen inside the Docker container.
-    """
-    relative = checkpoint_path.relative_to(checkpoints_dir)
-    return str(Path(container_checkpoints_dir) / relative)
-
-
-def _collect_rows(
-    run: dict[str, Any],
-    epoch_map: dict[int, Path],
-    vllm_url: str,
-    container_checkpoints_dir: str,
-) -> list[dict[str, Any]]:
-    rows: list[dict[str, Any]] = []
-    for epoch in sorted(epoch_map):
-        ckpt_path = epoch_map[epoch]
-        container_path = _container_checkpoint_path(
-            ckpt_path, CHECKPOINTS_DIR, container_checkpoints_dir
-        )
-        log.info(f"  [INFER] epoch {epoch} — {ckpt_path.name}")
-        completions = run_inference(ckpt_path, container_path, vllm_url)
-        eval_loss = get_eval_loss(ckpt_path, epoch)
-        rows.append(
-            {
-                "run_name": run["run_name"],
-                "epoch": epoch,
-                "learning_rate": run["learning_rate"],
-                "lora_r": run["lora_r"],
-                "lora_alpha": run["lora_alpha"],
-                "effective_batch_size": run["effective_batch_size"],
-                "target_layers": run["target_layers_name"],
-                "eval_loss": eval_loss,
-                "completions": json.dumps(completions),
-            }
-        )
-        log.info(
-            f"    eval_loss={eval_loss:.4f}  n_completions={len(completions)}"
-        )
-    return rows
-
-
 def _execute_run(
     run: dict[str, Any],
     axolotl_base: dict[str, Any],
     state: dict[str, Any],
-    vllm_url: str,
-    container_checkpoints_dir: str,
 ) -> None:
     name = run["run_name"]
+    output_dir = CHECKPOINTS_DIR / name
     try:
         config_path = write_run_config(run, axolotl_base)
         run_training(config_path)
-        epoch_map = find_epoch_checkpoints(name)
-        if not epoch_map:
-            raise RuntimeError("No epoch checkpoints found after training")
-        log.info(f"Found checkpoints for epochs: {sorted(epoch_map)}")
-        rows = _collect_rows(run, epoch_map, vllm_url, container_checkpoints_dir)
+
+        eval_losses = _get_all_eval_losses(output_dir)
+        rows: list[dict[str, Any]] = []
+        for epoch in range(1, 4):
+            completions_path = output_dir / f"completions_epoch_{epoch}.json"
+            if not completions_path.exists():
+                log.warning(f"  Missing completions for epoch {epoch}, skipping")
+                continue
+            completions = json.loads(completions_path.read_text())
+            rows.append(
+                {
+                    "run_name": name,
+                    "epoch": epoch,
+                    "learning_rate": run["learning_rate"],
+                    "lora_r": run["lora_r"],
+                    "lora_alpha": run["lora_alpha"],
+                    "effective_batch_size": run["effective_batch_size"],
+                    "target_layers": run["target_layers_name"],
+                    "eval_loss": eval_losses.get(epoch, float("nan")),
+                    "completions": json.dumps(completions),
+                }
+            )
+            log.info(
+                f"  epoch {epoch}  eval_loss={eval_losses.get(epoch, float('nan')):.4f}"
+                f"  n_completions={len(completions)}"
+            )
+
+        if not rows:
+            raise RuntimeError("No completions written by EvalCompletionsCallback")
+
         append_results(rows)
         state["completed"].append(name)
         state["failed"].pop(name, None)
@@ -394,16 +306,6 @@ def main() -> None:
         default="2",
         help="Comma-separated CUDA device indices for training (default: 2)",
     )
-    parser.add_argument(
-        "--vllm-url",
-        default="http://localhost:8000",
-        help="Base URL of the persistent vLLM Docker server (default: http://localhost:8000)",
-    )
-    parser.add_argument(
-        "--container-checkpoints-dir",
-        default="/checkpoints",
-        help="Checkpoints directory as mounted inside the vLLM container (default: /checkpoints)",
-    )
     args = parser.parse_args()
 
     os.environ["CUDA_VISIBLE_DEVICES"] = args.gpus
@@ -444,9 +346,7 @@ def main() -> None:
         log.info(f"\n{'=' * 60}")
         log.info(f"[START] {name}")
         log.info(f"{'=' * 60}")
-        _execute_run(
-            run, axolotl_base, state, args.vllm_url, args.container_checkpoints_dir
-        )
+        _execute_run(run, axolotl_base, state)
 
     n_done = len(state["completed"])
     log.info(f"\nSweep complete: {n_done}/{n_runs} runs succeeded.")

From 727a54cfbf206553db3d4ec2138843a6a17a29df Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 19:29:13 +0100
Subject: [PATCH 36/68] fix(sweep): log sample completions at each epoch end

---
 sweep/callbacks.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 3f1469d..6f9ca12 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -118,6 +118,8 @@ def on_epoch_end(
         output_dir = Path(args.output_dir)
         out_path = output_dir / f"completions_epoch_{epoch}.json"
         out_path.write_text(json.dumps(completions))
+        for comp in completions[:3]:
+            log.info("  [sample] %r", comp["generated_response"])
         log.info(
             "EvalCompletionsCallback: wrote %d completions → %s",
             len(completions),

From c1efff53ea5a7807f6994e16234a23b0128c14ea Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 19:30:12 +0100
Subject: [PATCH 37/68] docs(sweep): update README to remove vLLM setup,
 reflect callback approach

---
 sweep/README.md | 76 ++++++++++---------------------------------------
 1 file changed, 15 insertions(+), 61 deletions(-)

diff --git a/sweep/README.md b/sweep/README.md
index ca4d11d..d9a4711 100644
--- a/sweep/README.md
+++ b/sweep/README.md
@@ -4,15 +4,9 @@
 LoRA rank, learning rate, batch size, and target layer configurations.
 
 Validation loss is computed by axolotl after each epoch (stored in
-`trainer_state.json`). Completions are generated via a persistent vLLM
-Docker server on a dedicated GPU.
-
-## GPU layout
-
-| GPU | Role |
-|---|---|
-| `2` | axolotl training (`sweep.py`) |
-| `3` | vLLM Docker server (persistent, started once before sweep) |
+`trainer_state.json`). Completions are generated by `EvalCompletionsCallback`
+at the end of each epoch using the live in-memory model — no separate inference
+process or server required.
 
 ## Setup on the H100
 
@@ -24,57 +18,17 @@ uv sync
 source .venv/bin/activate
 ```
 
-**2. Start the vLLM Docker server on GPU 3 (one time, from project root)**
-
-```bash
-docker run -d --name vllm-sweep \
-  --runtime nvidia \
-  -e NVIDIA_VISIBLE_DEVICES=3 \
-  --gpus device=3 \
-  -p 127.0.0.1:8000:8000 \
-  -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN \
-  -e VLLM_ALLOW_RUNTIME_LORA_UPDATING=true \
-  -v ~/.cache/huggingface:/root/.cache/huggingface \
-  -v "$PWD/sweep/checkpoints:/checkpoints:ro" \
-  vllm/vllm-openai:v0.9.0 \
-  --model meta-llama/Llama-3.1-8B-Instruct \
-  --enable-lora \
-  --max-lora-rank 32 \
-  --dtype bfloat16 \
-  --max-model-len 2048 \
-  --port 8000
-```
-
-`VLLM_ALLOW_RUNTIME_LORA_UPDATING=true` enables the `/v1/load_lora_adapter`
-and `/v1/unload_lora_adapter` endpoints, which are disabled by default in
-vLLM 0.9.0. Without it, `infer.py` will get a 404.
-
-The container mounts `sweep/checkpoints` at `/checkpoints` (read-only).
-Wait for the server to be healthy before starting the sweep:
-
-```bash
-docker logs -f vllm-sweep   # wait for "Application startup complete"
-curl http://localhost:8000/health
-```
-
-If you need to restart the container (e.g. after a crash), remove it first:
-
-```bash
-docker stop vllm-sweep && docker rm vllm-sweep
-```
-
-**3. Set environment variables**
+**2. Set environment variables**
 
 ```bash
 export HF_TOKEN=hf_...
 export WANDB_API_KEY=...
 ```
 
-**4. Verify the grid looks correct**
+**3. Verify the grid looks correct**
 
 ```bash
-cd sweep
-python sweep.py --dry-run
+python sweep/sweep.py --dry-run
 ```
 
 ## Running the sweep
@@ -83,20 +37,20 @@ Start a tmux session so the sweep survives SSH disconnection:
 
 ```bash
 tmux new -s sweep
-cd sweep
-python sweep.py          # GPU 2 for training, localhost:8000 for vLLM
+python sweep/sweep.py          # defaults to GPU 2
 # Ctrl+B, D  →  detach (safe to close laptop)
 # tmux attach -t sweep  →  reconnect later
 ```
 
-If the vLLM server is on a different port or host, use `--vllm-url` and
-`--container-checkpoints-dir` to override:
+To use a different GPU:
 
 ```bash
-python sweep.py --vllm-url http://localhost:8001 --container-checkpoints-dir /data/checkpoints
+python sweep/sweep.py --gpus 0
 ```
 
-Progress is logged to `sweep.log` and to the W&B project `VOICE`.
+Progress is logged to `sweep/sweep.log` and to the W&B project `VOICE`.
+Completions are uploaded to W&B as artifacts named `{run_name}-epoch{N}-Results`
+and written locally to `sweep/checkpoints/{run_name}/completions_epoch_{N}.json`.
 
 ## Resuming after failure
 
@@ -104,12 +58,12 @@ Progress is logged to `sweep.log` and to the W&B project `VOICE`.
 it will skip completed runs and retry failed ones:
 
 ```bash
-python sweep.py
+python sweep/sweep.py
 ```
 
 ## Outputs
 
-Results are written to `outputs/results.parquet` after each training run
+Results are written to `sweep/outputs/results.parquet` after each training run
 completes. Schema:
 
 | Column | Description |
@@ -118,7 +72,7 @@ completes. Schema:
 | `epoch` | Epoch number (1, 2, or 3) |
 | `learning_rate` | LoRA learning rate |
 | `lora_r` | LoRA rank |
-| `lora_alpha` | LoRA alpha (= 2 × rank) |
+| `lora_alpha` | LoRA alpha (= rank) |
 | `effective_batch_size` | Effective batch size |
 | `target_layers` | `attention` or `attention_mlp` |
 | `eval_loss` | Validation NLL loss (from axolotl trainer_state.json) |

From 11fde9024cdfee334dfc9aef8981f10ceae5ddc8 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 20:07:57 +0100
Subject: [PATCH 38/68] fix(sweep): add repo root to PYTHONPATH so axolotl can
 import EvalCompletionsCallback

---
 sweep/sweep.py | 11 ++++++++++-
 1 file changed, 10 insertions(+), 1 deletion(-)

diff --git a/sweep/sweep.py b/sweep/sweep.py
index 720d908..cc39686 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -210,8 +210,17 @@ def run_training(config_path: Path) -> None:
     else:
         cmd = [sys.executable, "-m", "axolotl.cli.train", str(config_path)]
 
+    # Ensure the repo root is on PYTHONPATH so axolotl can import
+    # sweep.callbacks.EvalCompletionsCallback when loading the callback list
+    # from the per-run YAML config.
+    env = os.environ.copy()
+    repo_root = str(SWEEP_DIR.parent)
+    env["PYTHONPATH"] = (
+        repo_root + ":" + env["PYTHONPATH"] if "PYTHONPATH" in env else repo_root
+    )
+
     log.info(f"Training on {n_gpus} GPU(s): {config_path.name}")
-    subprocess.run(cmd, check=True)
+    subprocess.run(cmd, check=True, env=env)
 
 
 # ---------------------------------------------------------------------------

From 2218301a5dc0b48b752e76b7ca6c2f09937c638b Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 20:23:11 +0100
Subject: [PATCH 39/68] build: upgrade axolotl pin from v0.16.1 to main for
 callbacks YAML support

---
 pyproject.toml | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/pyproject.toml b/pyproject.toml
index f1c4555..c8d8ff7 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12,<3.13"
 dependencies = [
     "accelerate",
-    "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@v0.16.1 ; sys_platform == 'linux'",
+    "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@main ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
 "huggingface-hub>=1.4.1",

From 1fd9cbff53905a4ede7fa302822027f30c265b7c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 14 Apr 2026 20:40:53 +0100
Subject: [PATCH 40/68] fix(sweep): switch to axolotl plugins API for callback
 registration

Replace non-existent callbacks YAML key with the plugins system.
EvalCompletionsPlugin wraps EvalCompletionsCallback via add_callbacks_pre_trainer.
Also switch axolotl dependency from git source to PyPI package.
---
 pyproject.toml           |  2 +-
 sweep/callbacks.py       | 22 ++++++++++++++++++++++
 sweep/master_config.yaml | 10 +++++-----
 3 files changed, 28 insertions(+), 6 deletions(-)

diff --git a/pyproject.toml b/pyproject.toml
index c8d8ff7..24f6c48 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -6,7 +6,7 @@ readme = "README.md"
 requires-python = ">=3.12,<3.13"
 dependencies = [
     "accelerate",
-    "axolotl @ git+https://github.com/axolotl-ai-cloud/axolotl.git@main ; sys_platform == 'linux'",
+    "axolotl>=0.16.1 ; sys_platform == 'linux'",
     "datasets>=4.5.0",
     "dotenv>=0.9.9",
 "huggingface-hub>=1.4.1",
diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 6f9ca12..9469e0a 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -22,6 +22,7 @@
 
 import torch
 import wandb
+from axolotl.integrations.base import BasePlugin
 from datasets import load_dataset
 from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
 
@@ -186,3 +187,24 @@ def _generate_batch(
         generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
         results.append({"messages": ex["messages"], "generated_response": generated})
     return results
+
+
+class EvalCompletionsPlugin(BasePlugin):
+    """Axolotl plugin that registers EvalCompletionsCallback before training.
+
+    Register via the axolotl YAML config::
+
+        plugins:
+          - sweep.callbacks.EvalCompletionsPlugin
+    """
+
+    def add_callbacks_pre_trainer(
+        self, cfg: Any, model: Any
+    ) -> list[TrainerCallback]:
+        """Return the completions callback to be added to the Trainer.
+
+        :param cfg: axolotl config dict.
+        :param model: the loaded model.
+        :returns: list containing the EvalCompletionsCallback instance.
+        """
+        return [EvalCompletionsCallback()]
diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 5f3e9c0..724484f 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -113,11 +113,11 @@ axolotl:
   save_strategy: epoch
   save_total_limit: 3
 
-  # Callback — generates validation completions at each epoch end using the
-  # live in-memory model. Requires sweep/ to be on PYTHONPATH (run from repo
-  # root, or set PYTHONPATH=. before launching axolotl).
-  callbacks:
-    - sweep.callbacks.EvalCompletionsCallback
+  # Plugin — generates validation completions at each epoch end using the
+  # live in-memory model. Requires sweep/ to be on PYTHONPATH (handled by
+  # sweep.py before launching axolotl).
+  plugins:
+    - sweep.callbacks.EvalCompletionsPlugin
 
   # W&B — wandb_name injected per run by sweep.py.
   use_wandb: true

From e3b3d24d9609e12917880f44bf841239e11ce3d6 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 08:30:13 +0100
Subject: [PATCH 41/68] fix: axolotl from source

---
 pyproject.toml | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/pyproject.toml b/pyproject.toml
index 24f6c48..7d37f4b 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -25,6 +25,9 @@ dependencies = [
 [tool.uv]
 required-environments = ["sys_platform == 'linux'"]
 
+[tool.uv.sources]
+axolotl = { git = "https://github.com/axolotl-ai-cloud/axolotl.git", branch = "main" }
+
 [build-system]
 requires = ["setuptools>=68", "wheel"]
 build-backend = "setuptools.build_meta"

From 5c28180e6c7cb4a46e6b37f285ff5c51a9eb7e0c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 08:36:40 +0100
Subject: [PATCH 42/68] fix: tokenizer = None err

---
 sweep/callbacks.py | 34 ++++++++++++++++++++++++++++------
 1 file changed, 28 insertions(+), 6 deletions(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 9469e0a..06a046e 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -51,9 +51,15 @@ class EvalCompletionsCallback(TrainerCallback):
     axolotl YAML as ``sweep.callbacks.EvalCompletionsCallback``.
     """
 
-    def __init__(self) -> None:
-        """Initialise with an empty example cache."""
+    def __init__(self, trainer: Any = None) -> None:
+        """Initialise with an empty example cache.
+
+        :param trainer: the HF Trainer instance, stored so we can resolve
+            the tokenizer via ``trainer.processing_class`` at epoch end
+            (newer transformers deprecates passing tokenizer through kwargs).
+        """
         self._examples: list[dict[str, Any]] = []
+        self._trainer = trainer
 
     def on_train_begin(
         self,
@@ -109,6 +115,19 @@ def on_epoch_end(
             "EvalCompletionsCallback: generating completions for epoch %d", epoch
         )
 
+        # Newer transformers passes tokenizer via processing_class; fall back
+        # to the stored trainer reference when the kwarg arrives as None.
+        if tokenizer is None and self._trainer is not None:
+            tokenizer = getattr(
+                self._trainer, "processing_class", None
+            ) or getattr(self._trainer, "tokenizer", None)
+        if tokenizer is None:
+            log.error("EvalCompletionsCallback: tokenizer is None, skipping epoch %d", epoch)
+            return
+
+        if model is None and self._trainer is not None:
+            model = self._trainer.model
+
         model.eval()
         completions: list[dict[str, Any]] = []
         for i in range(0, len(self._examples), INFERENCE_BATCH_SIZE):
@@ -198,13 +217,16 @@ class EvalCompletionsPlugin(BasePlugin):
           - sweep.callbacks.EvalCompletionsPlugin
     """
 
-    def add_callbacks_pre_trainer(
-        self, cfg: Any, model: Any
+    def add_callbacks_post_trainer(
+        self, cfg: Any, trainer: Any
     ) -> list[TrainerCallback]:
         """Return the completions callback to be added to the Trainer.
 
+        Called after the Trainer is built, so we can pass the trainer
+        instance into the callback and resolve ``processing_class`` later.
+
         :param cfg: axolotl config dict.
-        :param model: the loaded model.
+        :param trainer: the built HF Trainer instance.
         :returns: list containing the EvalCompletionsCallback instance.
         """
-        return [EvalCompletionsCallback()]
+        return [EvalCompletionsCallback(trainer=trainer)]

From 2ac9558fcea4dab7bca6d94fcf3cfd5cff059092 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 08:45:01 +0100
Subject: [PATCH 43/68] fix:

---
 sweep/callbacks.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 06a046e..7795860 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -174,7 +174,7 @@ def _generate_batch(
             ex["messages"],
             return_tensors="pt",
             add_generation_prompt=True,
-        ).squeeze(0)  # (seq_len,)
+        )["input_ids"].squeeze(0)  # (seq_len,)
         for ex in batch
     ]
 

From 5cedd8a1c2dec44473eca11ad579c78caba6e0c8 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 09:06:10 +0100
Subject: [PATCH 44/68] chore: remove padding

---
 sweep/callbacks.py | 76 +++++++++++++++-------------------------------
 1 file changed, 25 insertions(+), 51 deletions(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 7795860..ce21605 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -29,7 +29,6 @@
 log = logging.getLogger(__name__)
 
 DATASET: str = "AccelerateScience/bush-dataset"
-INFERENCE_BATCH_SIZE: int = 8
 
 # Sampling parameters agreed for all experiments:
 # T=1.0 with no truncation samples from the exact learned distribution,
@@ -128,18 +127,19 @@ def on_epoch_end(
         if model is None and self._trainer is not None:
             model = self._trainer.model
 
+        output_dir = Path(args.output_dir)
+        out_path = output_dir / f"completions_epoch_{epoch}.json"
+        out_path.write_text("[]")
+
         model.eval()
         completions: list[dict[str, Any]] = []
-        for i in range(0, len(self._examples), INFERENCE_BATCH_SIZE):
-            batch = self._examples[i : i + INFERENCE_BATCH_SIZE]
-            completions.extend(_generate_batch(batch, model, tokenizer))
+        for example in self._examples:
+            result = _generate_one(example, model, tokenizer)
+            completions.append(result)
+            out_path.write_text(json.dumps(completions, indent=2))
         model.train()
 
-        output_dir = Path(args.output_dir)
-        out_path = output_dir / f"completions_epoch_{epoch}.json"
-        out_path.write_text(json.dumps(completions))
-        for comp in completions[:3]:
-            log.info("  [sample] %r", comp["generated_response"])
+        log.info("  [sample] %r", completions[0]["generated_response"])
         log.info(
             "EvalCompletionsCallback: wrote %d completions → %s",
             len(completions),
@@ -155,57 +155,31 @@ def on_epoch_end(
             wandb.log_artifact(artifact)
 
 
-def _generate_batch(
-    batch: list[dict[str, Any]],
+def _generate_one(
+    example: dict[str, Any],
     model: Any,
     tokenizer: Any,
-) -> list[dict[str, Any]]:
-    """Generate completions for one batch using left-padded inputs.
+) -> dict[str, Any]:
+    """Generate a completion for a single example.
 
-    :param batch: list of examples, each with a ``messages`` list
-        (system + user only — no assistant turn).
+    :param example: dict with a ``messages`` list (system + user only).
     :param model: the current model.
     :param tokenizer: the current tokenizer.
-    :returns: list of completion dicts with ``messages`` and
-        ``generated_response`` keys.
+    :returns: completion dict with ``messages`` and ``generated_response`` keys.
     """
-    encoded = [
-        tokenizer.apply_chat_template(
-            ex["messages"],
-            return_tensors="pt",
-            add_generation_prompt=True,
-        )["input_ids"].squeeze(0)  # (seq_len,)
-        for ex in batch
-    ]
-
-    # Left-pad all sequences to the length of the longest in the batch.
-    max_len = max(t.shape[0] for t in encoded)
-    pad_id = tokenizer.pad_token_id
-    input_ids = torch.full(
-        (len(batch), max_len), pad_id, dtype=torch.long, device=model.device
-    )
-    attention_mask = torch.zeros(
-        (len(batch), max_len), dtype=torch.long, device=model.device
-    )
-    for j, t in enumerate(encoded):
-        start = max_len - t.shape[0]
-        input_ids[j, start:] = t.to(model.device)
-        attention_mask[j, start:] = 1
+    input_ids = tokenizer.apply_chat_template(
+        example["messages"],
+        return_tensors="pt",
+        add_generation_prompt=True,
+    )["input_ids"].to(model.device)  # (1, seq_len)
+    prompt_len = input_ids.shape[1]
 
     with torch.no_grad():
-        out = model.generate(
-            input_ids,
-            attention_mask=attention_mask,
-            **GENERATION_CONFIG,
-        )
+        out = model.generate(input_ids, **GENERATION_CONFIG)
 
-    results = []
-    for j, ex in enumerate(batch):
-        # out includes the (padded) prompt tokens; skip past them.
-        new_tokens = out[j, max_len:]
-        generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
-        results.append({"messages": ex["messages"], "generated_response": generated})
-    return results
+    new_tokens = out[0, prompt_len:]
+    generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
+    return {"messages": example["messages"], "generated_response": generated}
 
 
 class EvalCompletionsPlugin(BasePlugin):

From 027794a00f83f9d479b9226000d1c1d289f45ddf Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 09:19:16 +0100
Subject: [PATCH 45/68] chore: temperature -> 0.7

---
 sweep/callbacks.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index ce21605..f9c3319 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -31,11 +31,11 @@
 DATASET: str = "AccelerateScience/bush-dataset"
 
 # Sampling parameters agreed for all experiments:
-# T=1.0 with no truncation samples from the exact learned distribution,
+# T=0.7 with no truncation samples from the exact learned distribution,
 # eliminating any gap between the training and inference distributions.
 GENERATION_CONFIG: dict[str, Any] = {
     "do_sample": True,
-    "temperature": 1.0,
+    "temperature": 0.7,
     "top_p": 1.0,
     "top_k": 0,
     "repetition_penalty": 1.0,

From eda3fe2cf35f6a0d551952fea5e12fbad276b20c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 09:29:55 +0100
Subject: [PATCH 46/68] add batching

---
 sweep/callbacks.py | 161 +++++++++++++++++++++++++++++++--------------
 1 file changed, 112 insertions(+), 49 deletions(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index f9c3319..8a03a93 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -1,13 +1,14 @@
 """Axolotl TrainerCallback that generates validation completions at each epoch end.
 
-Registered via the axolotl ``callbacks`` YAML key — instantiated with no
-arguments. All configuration is sourced from environment variables or the
-TrainingArguments passed at runtime.
+Registered via the axolotl ``plugins`` YAML key as
+``sweep.callbacks.EvalCompletionsPlugin``.
 
-Validation examples are loaded once at train-begin and cached. At each epoch
-end the in-memory model generates completions in left-padded batches, writes
-them to ``{output_dir}/completions_epoch_{N}.json``, and uploads an artifact
-to W&B named ``{run_name}-epoch{N}-Results``.
+All GPUs participate in generation: the 100-example validation set is sharded
+across processes (rank 0 gets indices 0, 2, 4, …; rank 1 gets 1, 3, 5, …).
+Each shard is generated in left-padded batches of ``INFERENCE_BATCH_SIZE``.
+Rank 0 streams its own partial results to disk after every batch so the file
+is readable mid-run. After all ranks finish, results are gathered and rank 0
+writes the final ordered JSON and uploads a W&B artifact.
 
 The output format is identical to the previous vLLM inference pipeline::
 
@@ -21,6 +22,7 @@
 from typing import Any
 
 import torch
+import torch.distributed
 import wandb
 from axolotl.integrations.base import BasePlugin
 from datasets import load_dataset
@@ -30,6 +32,10 @@
 
 DATASET: str = "AccelerateScience/bush-dataset"
 
+# 32 examples per batch fits comfortably in 80 GB VRAM.
+# With 2 GPUs each handling ~50 examples this means ≤ 2 forward passes per GPU.
+INFERENCE_BATCH_SIZE: int = 32
+
 # Sampling parameters agreed for all experiments:
 # T=0.7 with no truncation samples from the exact learned distribution,
 # eliminating any gap between the training and inference distributions.
@@ -44,18 +50,14 @@
 
 
 class EvalCompletionsCallback(TrainerCallback):
-    """Generate and log validation completions at each epoch end.
-
-    Designed to be zero-argument so it can be registered directly in the
-    axolotl YAML as ``sweep.callbacks.EvalCompletionsCallback``.
-    """
+    """Generate and log validation completions at each epoch end."""
 
     def __init__(self, trainer: Any = None) -> None:
         """Initialise with an empty example cache.
 
         :param trainer: the HF Trainer instance, stored so we can resolve
-            the tokenizer via ``trainer.processing_class`` at epoch end
-            (newer transformers deprecates passing tokenizer through kwargs).
+            the tokenizer via ``trainer.processing_class`` and unwrap the
+            model via ``trainer.accelerator`` at epoch end.
         """
         self._examples: list[dict[str, Any]] = []
         self._trainer = trainer
@@ -97,48 +99,98 @@ def on_epoch_end(
     ) -> None:
         """Generate completions for all validation examples and persist results.
 
-        Runs only on the main process (rank 0) in distributed training.
+        All ranks generate their shard in parallel; rank 0 gathers and writes.
 
         :param args: HF TrainingArguments.
         :param state: TrainerState — ``state.epoch`` gives the current epoch.
         :param control: TrainerControl object.
-        :param model: the live model (already on the correct device).
-        :param tokenizer: the tokenizer associated with the model.
+        :param model: the live model (DDP/FSDP wrapped; unwrapped internally).
+        :param tokenizer: the tokenizer, or None in newer transformers (resolved
+            via ``processing_class`` kwarg or the stored trainer reference).
         :returns: None
         """
-        if args.process_index != 0:
-            return
-
         epoch = round(state.epoch)
         log.info(
             "EvalCompletionsCallback: generating completions for epoch %d", epoch
         )
 
-        # Newer transformers passes tokenizer via processing_class; fall back
-        # to the stored trainer reference when the kwarg arrives as None.
+        # Newer transformers passes the tokenizer as processing_class, not tokenizer.
+        if tokenizer is None:
+            tokenizer = kwargs.get("processing_class")
         if tokenizer is None and self._trainer is not None:
             tokenizer = getattr(
                 self._trainer, "processing_class", None
             ) or getattr(self._trainer, "tokenizer", None)
         if tokenizer is None:
-            log.error("EvalCompletionsCallback: tokenizer is None, skipping epoch %d", epoch)
+            log.error(
+                "EvalCompletionsCallback: tokenizer is None, skipping epoch %d", epoch
+            )
             return
 
+        # Unwrap DDP/FSDP wrapper so .generate() is available.
         if model is None and self._trainer is not None:
             model = self._trainer.model
+        if self._trainer is not None and hasattr(self._trainer, "accelerator"):
+            model = self._trainer.accelerator.unwrap_model(model)
+
+        # Shard the validation set across all ranks so every GPU contributes.
+        local_indices = list(
+            range(args.process_index, len(self._examples), args.world_size)
+        )
+        local_examples = [self._examples[i] for i in local_indices]
 
         output_dir = Path(args.output_dir)
         out_path = output_dir / f"completions_epoch_{epoch}.json"
-        out_path.write_text("[]")
 
+        orig_padding_side = tokenizer.padding_side
+        tokenizer.padding_side = "left"
         model.eval()
-        completions: list[dict[str, Any]] = []
-        for example in self._examples:
-            result = _generate_one(example, model, tokenizer)
-            completions.append(result)
-            out_path.write_text(json.dumps(completions, indent=2))
+
+        local_completions: list[dict[str, Any]] = []
+        rank0_by_idx: dict[int, dict[str, Any]] = {}
+
+        for i in range(0, len(local_examples), INFERENCE_BATCH_SIZE):
+            batch_examples = local_examples[i : i + INFERENCE_BATCH_SIZE]
+            batch_indices = local_indices[i : i + INFERENCE_BATCH_SIZE]
+            results = _generate_batch(batch_examples, model, tokenizer)
+            local_completions.extend(results)
+
+            # Rank 0 streams partial results after every batch.
+            if args.process_index == 0:
+                for orig_idx, result in zip(batch_indices, results):
+                    rank0_by_idx[orig_idx] = result
+                out_path.write_text(
+                    json.dumps(
+                        [rank0_by_idx[j] for j in sorted(rank0_by_idx)], indent=2
+                    )
+                )
+
         model.train()
+        tokenizer.padding_side = orig_padding_side
+
+        # Gather every rank's shard onto rank 0.
+        if (
+            args.world_size > 1
+            and torch.distributed.is_available()
+            and torch.distributed.is_initialized()
+        ):
+            all_shards: list[Any] = [None] * args.world_size
+            torch.distributed.all_gather_object(
+                all_shards, (local_indices, local_completions)
+            )
+        else:
+            all_shards = [(local_indices, local_completions)]
+
+        if args.process_index != 0:
+            return
+
+        # Reassemble in original dataset order.
+        completions: list[dict[str, Any]] = [{}] * len(self._examples)
+        for proc_indices, proc_completions in all_shards:
+            for orig_idx, comp in zip(proc_indices, proc_completions):
+                completions[orig_idx] = comp
 
+        out_path.write_text(json.dumps(completions, indent=2))
         log.info("  [sample] %r", completions[0]["generated_response"])
         log.info(
             "EvalCompletionsCallback: wrote %d completions → %s",
@@ -155,31 +207,42 @@ def on_epoch_end(
             wandb.log_artifact(artifact)
 
 
-def _generate_one(
-    example: dict[str, Any],
+def _generate_batch(
+    batch: list[dict[str, Any]],
     model: Any,
     tokenizer: Any,
-) -> dict[str, Any]:
-    """Generate a completion for a single example.
-
-    :param example: dict with a ``messages`` list (system + user only).
-    :param model: the current model.
-    :param tokenizer: the current tokenizer.
-    :returns: completion dict with ``messages`` and ``generated_response`` keys.
+) -> list[dict[str, Any]]:
+    """Generate completions for a batch using left-padded inputs.
+
+    :param batch: list of examples, each with a ``messages`` list
+        (system + user only — no assistant turn).
+    :param model: unwrapped model (already on the correct device).
+    :param tokenizer: tokenizer with ``padding_side`` set to ``"left"``.
+    :returns: list of completion dicts with ``messages`` and
+        ``generated_response`` keys.
     """
-    input_ids = tokenizer.apply_chat_template(
-        example["messages"],
-        return_tensors="pt",
-        add_generation_prompt=True,
-    )["input_ids"].to(model.device)  # (1, seq_len)
-    prompt_len = input_ids.shape[1]
+    prompts = [
+        tokenizer.apply_chat_template(
+            ex["messages"], tokenize=False, add_generation_prompt=True
+        )
+        for ex in batch
+    ]
+    inputs = tokenizer(
+        prompts, return_tensors="pt", padding=True, truncation=False
+    ).to(model.device)
+    padded_len = inputs["input_ids"].shape[1]
 
     with torch.no_grad():
-        out = model.generate(input_ids, **GENERATION_CONFIG)
-
-    new_tokens = out[0, prompt_len:]
-    generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
-    return {"messages": example["messages"], "generated_response": generated}
+        outputs = model.generate(**inputs, **GENERATION_CONFIG)
+
+    results = []
+    for j, ex in enumerate(batch):
+        # outputs[j] = [left-pad tokens] + [prompt tokens] + [generated tokens]
+        # All inputs share the same padded_len so slicing is uniform.
+        new_tokens = outputs[j, padded_len:]
+        generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
+        results.append({"messages": ex["messages"], "generated_response": generated})
+    return results
 
 
 class EvalCompletionsPlugin(BasePlugin):

From 4f4dd719625e8ec5b60cf135aa5734d8a6033aed Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 09:38:27 +0100
Subject: [PATCH 47/68] chore: update default devices

---
 sweep/sweep.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/sweep/sweep.py b/sweep/sweep.py
index cc39686..4fd0ea1 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -13,7 +13,7 @@
 
     python sweep.py
     python sweep.py --dry-run   # print grid without training
-    python sweep.py --gpus 2    # override training GPU (default: 2)
+    python sweep.py --gpus 2    # override training GPU (default: 2,3)
 """
 
 import argparse
@@ -312,8 +312,8 @@ def main() -> None:
     )
     parser.add_argument(
         "--gpus",
-        default="2",
-        help="Comma-separated CUDA device indices for training (default: 2)",
+        default="2,3",
+        help="Comma-separated CUDA device indices for training (default: 2,3)",
     )
     args = parser.parse_args()
 

From 84686026ba170bbcf5b77ad82c59d85e8e72264b Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 09:41:43 +0100
Subject: [PATCH 48/68] chore: update wandb project

---
 sweep/master_config.yaml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/sweep/master_config.yaml b/sweep/master_config.yaml
index 724484f..afacee6 100644
--- a/sweep/master_config.yaml
+++ b/sweep/master_config.yaml
@@ -122,3 +122,4 @@ axolotl:
   # W&B — wandb_name injected per run by sweep.py.
   use_wandb: true
   wandb_project: VOICE
+  wandb_entity: accelerate-science

From 4c091e306692adf46d180e33ad83e287a133779d Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 15 Apr 2026 10:04:36 +0100
Subject: [PATCH 49/68] fix eval loss logging

---
 sweep/callbacks.py | 36 ++++++++++++++++++++++++++++++++++++
 sweep/sweep.py     | 12 ++++++++++--
 2 files changed, 46 insertions(+), 2 deletions(-)

diff --git a/sweep/callbacks.py b/sweep/callbacks.py
index 8a03a93..b200450 100644
--- a/sweep/callbacks.py
+++ b/sweep/callbacks.py
@@ -88,6 +88,42 @@ def on_train_begin(
             len(self._examples),
         )
 
+    def on_evaluate(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        control: TrainerControl,
+        metrics: dict[str, Any] | None = None,
+        **kwargs: Any,
+    ) -> None:
+        """Persist eval_loss for this epoch to eval_losses.json.
+
+        Called by HF Trainer immediately after the evaluation loop, with
+        ``metrics`` containing the freshly computed ``eval_loss``.  Writing
+        our own file removes the dependency on trainer_state.json parsing.
+
+        :param args: HF TrainingArguments.
+        :param state: current TrainerState.
+        :param control: TrainerControl object.
+        :param metrics: evaluation metrics dict — ``eval_loss`` is always
+            present when an evaluation loop completes with losses.
+        :returns: None
+        """
+        if args.process_index != 0 or not metrics or "eval_loss" not in metrics:
+            return
+        epoch = round(state.epoch)
+        losses_path = Path(args.output_dir) / "eval_losses.json"
+        losses: dict[str, float] = (
+            json.loads(losses_path.read_text()) if losses_path.exists() else {}
+        )
+        losses[str(epoch)] = float(metrics["eval_loss"])
+        losses_path.write_text(json.dumps(losses, indent=2))
+        log.info(
+            "EvalCompletionsCallback: epoch %d eval_loss=%.4f",
+            epoch,
+            metrics["eval_loss"],
+        )
+
     def on_epoch_end(
         self,
         args: TrainingArguments,
diff --git a/sweep/sweep.py b/sweep/sweep.py
index 4fd0ea1..99e9d8a 100644
--- a/sweep/sweep.py
+++ b/sweep/sweep.py
@@ -163,13 +163,21 @@ def write_run_config(
 # Eval loss (from axolotl's trainer_state.json)
 # ---------------------------------------------------------------------------
 def _get_all_eval_losses(output_dir: Path) -> dict[int, float]:
-    """Read per-epoch validation losses from trainer_state.json.
+    """Read per-epoch validation losses written by EvalCompletionsCallback.
 
-    HF Trainer writes this file to the run output directory after training.
+    Prefers eval_losses.json (written directly by the callback via on_evaluate,
+    which receives the metrics dict immediately after each evaluation loop).
+    Falls back to trainer_state.json for runs that predate the callback change.
 
     :param output_dir: root output directory for the run.
     :returns: dict mapping epoch int to eval_loss float.
     """
+    losses_file = output_dir / "eval_losses.json"
+    if losses_file.exists():
+        raw = json.loads(losses_file.read_text())
+        return {int(k): float(v) for k, v in raw.items()}
+
+    # Fallback: parse trainer_state.json written by HF Trainer after training.
     state_file = output_dir / "trainer_state.json"
     if not state_file.exists():
         return {}

From 20401130f8c9ecfbc41a882f69176a6cf82d5d5c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Thu, 21 May 2026 16:09:09 +0100
Subject: [PATCH 50/68] feat(docs): add docs for data, evals and stylometry

---
 README.md                | 13 ++++++-
 cspell/library-words.txt |  2 +
 cspell/project-words.txt |  1 +
 docs/00_data.md          | 22 +++++++++++
 docs/01_stylometry.md    | 52 ++++++++++++++++++++++++++
 docs/02_evals.md         | 80 ++++++++++++++++++++++++++++++++++++++++
 6 files changed, 169 insertions(+), 1 deletion(-)
 create mode 100644 docs/00_data.md
 create mode 100644 docs/01_stylometry.md
 create mode 100644 docs/02_evals.md

diff --git a/README.md b/README.md
index 9c1ae2b..f6a441d 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,14 @@
-# VOICE - Fine-tuning for Stylistic Fidelity
+# 🗣️ VOICE
+
+...
 
 ---
+
+## Documentation
+
+```
+docs/
+├── 00_data.md         — Included datasets
+├── 01_evals.md        — Evaluation suite: scoring and interpretation
+└── 02_stylometry.md   — Stylometric metrics: definition and catalogue
+```
diff --git a/cspell/library-words.txt b/cspell/library-words.txt
index dc8d8fa..3e2f1e1 100644
--- a/cspell/library-words.txt
+++ b/cspell/library-words.txt
@@ -9,3 +9,5 @@ ylabel
 frameon
 allclose
 linalg
+mathcal
+mathbb
diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index 43b5325..68a0486 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -12,3 +12,4 @@ bonf
 bonferroni
 aeiou
 stylometrically
+evals
diff --git a/docs/00_data.md b/docs/00_data.md
new file mode 100644
index 0000000..e504061
--- /dev/null
+++ b/docs/00_data.md
@@ -0,0 +1,22 @@
+# Datasets
+
+---
+
+Two example datasets are included, each containing press conference Q&A transcripts split into train, validation and test sets:
+
+| President | HuggingFace |
+|---|---|
+| Barack Obama | [`AccelerateScience/bo-press-conference-qa`](https://huggingface.co/datasets/AccelerateScience/bo-press-conference-qa) |
+| George W. Bush | [`AccelerateScience/gwb-press-conference-qa`](https://huggingface.co/datasets/AccelerateScience/gwb-press-conference-qa) |
+
+Each example is a single JSONL record in chat format:
+
+```json
+{
+  "messages": [
+    {"role": "system",    "content": "You are president Barack Obama ..."},
+    {"role": "user",      "content": "<press conference question>"},
+    {"role": "assistant", "content": "<president's answer>"}
+  ]
+}
+```
diff --git a/docs/01_stylometry.md b/docs/01_stylometry.md
new file mode 100644
index 0000000..ee053e4
--- /dev/null
+++ b/docs/01_stylometry.md
@@ -0,0 +1,52 @@
+# Stylometric Metrics
+
+---
+
+A **stylometric metric** is any function
+
+$$f : \mathcal{T} \rightarrow \mathbb{R}$$
+
+that maps a text string to a real scalar capturing some surface property of writing style. VOICE treats each metric as a distribution over a corpus: given a set of texts, $f$ is applied to each one to produce a sample from the author's stylometric distribution for that feature.
+
+## Implemented Metrics
+
+### Word Length Distribution
+Moments of the per-word character length distribution.
+
+| Metric | Description |
+|---|---|
+| `avg_word_length` | Mean word length |
+| `std_word_length` | Standard deviation of word length |
+| `skew_word_length` | Skewness of word length |
+| `kurtosis_word_length` | Kurtosis of word length |
+
+### Vocabulary Richness
+Type–token and word statistics measuring lexical diversity.
+
+| Metric | Description |
+|---|---|
+| `type_token_ratio` | Unique tokens / total tokens |
+| `moving_avg_type_token_ratio` | TTR averaged over a sliding window (MATTR) |
+| `hapax_legomena_ratio` | Fraction of words appearing exactly once |
+| `dis_legomena_ratio` | Fraction of words appearing exactly twice |
+| `tri_legomena_ratio` | Fraction of words appearing exactly three times |
+
+### Function Words
+| Metric | Description |
+|---|---|
+| `function_word_ratio` | Proportion of tokens drawn from a closed function-word list |
+
+### Character N-gram Diversity
+Type–token ratio and MATTR computed over character $n$-grams for $n \in \{3, 4, 5\}$.
+
+| Metric | Description |
+|---|---|
+| `char_{n}gram_type_token_ratio` | Character $n$-gram TTR |
+| `char_{n}gram_moving_avg_type_token_ratio` | Character $n$-gram MATTR |
+
+### Text Length
+| Metric | Description |
+|---|---|
+| `num_words` | Total word count |
+
+> **Note:** `num_words` may be deprecated in a future release. It does not tend to be used as a signature for authorship attribution in the broader stylometry literature.
diff --git a/docs/02_evals.md b/docs/02_evals.md
new file mode 100644
index 0000000..4b55fce
--- /dev/null
+++ b/docs/02_evals.md
@@ -0,0 +1,80 @@
+# Evaluation Suite
+
+---
+
+## How Scoring Works
+
+For a set of model completions $\mathcal{C} = \{c_1, \dots, c_n\}$ and a set of reference answers $\mathcal{D} = \{d_1, \dots, d_n\}$ drawn from the same underlying question corpus, VOICE computes an **alignment score** in $[0, 1]$ measuring how stylometrically similar the completions are to the reference author. Both $\mathcal{C}$ and $\mathcal{D}$ are responses to the same set of prompts, so any observed distributional difference reflects style rather than content.
+
+### Step 1: Wasserstein Distance
+
+For each metric $f$, apply it to every text to get empirical distributions:
+
+$$X = \{f(c_i)\}_{i=1}^n \quad \text{and} \quad Y = \{f(d_i)\}_{i=1}^n$$
+
+and compute their Wasserstein-1 distance $W(X, Y)$. A smaller distance means the two distributions are closer in shape.
+
+### Step 2: Calibration
+
+To interpret $W(X, Y)$ we need to know what distance is *expected even between two samples of the same size from the same author*. VOICE estimates this from the **training split** of $\mathcal{D}$ by bootstrap resampling: repeatedly drawing two non-overlapping subsets of size $n$ and computing their mutual Wasserstein distance. This yields a reference distribution $\mathcal{W}_0$ of self-distances that characterises natural within-author variation under the same sample size as the completions being evaluated.
+
+### Step 3: Tail Value
+
+The observed distance is located within $\mathcal{W}_0$ via its empirical CDF:
+
+$$p = \hat{F}_{\mathcal{W}_0}(W(X, Y)), \qquad \tau = 1 - p$$
+
+$\tau$ is the **tail value**: the fraction of self-distances that *exceed* the observed distance. A high $\tau$ means the model's completions are no further from the reference than typical same-author samples, i.e. they are stylistically consistent with the reference author.
+
+### Step 4: Group and Overall Score
+
+Metrics are organised into groups (see [Stylometric Metrics](01_stylometry.md)). Each group captures a distinct linguistic object — word length, vocabulary richness, and so on — and the metrics within it represent different ways of characterising that same object. Because they study the same underlying property, within-group metrics are highly correlated; averaging within groups before aggregating across them prevents any single linguistic dimension from dominating the score simply by having more metrics defined for it.
+
+Within each group the tail values are averaged:
+
+$$\bar{\tau}_g = \frac{1}{|g|} \sum_{f \in g} \tau_f$$
+
+The overall **alignment score** is then the mean over groups, weighting each group equally:
+
+$$\text{score} = \frac{1}{G} \sum_{g=1}^{G} \bar{\tau}_g \in [0, 1]$$
+
+A score of **1** would mean the completions are stylistically indistinguishable from the reference even relative to same-author variation; in practice this is unrealistic but the directionality is still informative.
+
+---
+
+## This Is Not a Hypothesis Test
+
+The tail value $\tau$ resembles a p-value but is not one and should not be interpreted as such.
+
+A classical hypothesis test asks whether there is sufficient evidence to reject a null hypothesis. That framing entails a binary decision and is sensitive to sample size: with enough data, even negligible stylistic differences will cross any fixed significance threshold.
+
+VOICE is not concerned with whether two distributions are statistically indistinguishable. It asks how similar they are, on a continuous scale, relative to the natural within-author variation present in the training corpus. The calibration distribution $\mathcal{W}_0$ serves as a normalisation baseline, not a null to be rejected. There is no significance threshold and no p-value, only a score.
+
+---
+
+## Example
+
+Suppose a model achieves an alignment score of **0.2**. A natural reading is:
+
+> On average across stylometric groups, the model's completions sit at the 80th percentile of same-author self-distances, meaning the completions are stylistically closer to the reference than 20% of within-author sample pairs drawn from the training corpus.
+
+---
+
+## Diagnostics
+
+The API exposes intermediate results for deeper inspection:
+
+```python
+results = make_comparison(completions, true_ds)
+
+# Per-metric tail values
+results.metric_tails   # dict[str, float]
+
+# Per-group average tail values
+results.group_tails    # dict[MetricGroup, float]
+
+# Overall score
+results.score          # float
+```
+
+Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score.

From 6fe6287b96d772a69f1f94f335b12bde218f070f Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 09:51:16 +0100
Subject: [PATCH 51/68] chore: update gitignore

---
 .gitignore               | 6 +++++-
 cspell/library-words.txt | 3 +++
 2 files changed, 8 insertions(+), 1 deletion(-)

diff --git a/.gitignore b/.gitignore
index c849113..d0d1f59 100644
--- a/.gitignore
+++ b/.gitignore
@@ -65,4 +65,8 @@ notes.md
 # Sweep results
 sweep/results.parquet
 sweep/sweep.log
-sweep/state.json
\ No newline at end of file
+sweep/state.json
+
+# Fine-tuning runs and local configs
+runs/
+configs/
diff --git a/cspell/library-words.txt b/cspell/library-words.txt
index ed50a9b..46d0eca 100644
--- a/cspell/library-words.txt
+++ b/cspell/library-words.txt
@@ -17,3 +17,6 @@ checkpointing
 proc
 peft
 pyyaml
+libcuda
+PYTHONPATH
+venv

From 81d76d114f9490a17a5a744f96e94f53faf1875a Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 09:56:30 +0100
Subject: [PATCH 52/68] chore: add configs and run dirs

---
 .gitignore              | 6 +++++-
 configs/single/.gitkeep | 0
 configs/sweep/.gitkeep  | 0
 runs/.gitkeep           | 0
 4 files changed, 5 insertions(+), 1 deletion(-)
 create mode 100644 configs/single/.gitkeep
 create mode 100644 configs/sweep/.gitkeep
 create mode 100644 runs/.gitkeep

diff --git a/.gitignore b/.gitignore
index d0d1f59..e0d9f1d 100644
--- a/.gitignore
+++ b/.gitignore
@@ -69,4 +69,8 @@ sweep/state.json
 
 # Fine-tuning runs and local configs
 runs/
-configs/
+!runs/.gitkeep
+configs/single
+configs/sweep
+!configs/single/.gitkeep
+!configs/sweep/.gitkeep
diff --git a/configs/single/.gitkeep b/configs/single/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/configs/sweep/.gitkeep b/configs/sweep/.gitkeep
new file mode 100644
index 0000000..e69de29
diff --git a/runs/.gitkeep b/runs/.gitkeep
new file mode 100644
index 0000000..e69de29

From f5893dafbf92230147ee7da52f8561d70a5be62c Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 10:08:53 +0100
Subject: [PATCH 53/68] feat(data): add local dataset loading to VoiceDataset

---
 src/voice/__init__.py                    |  3 +-
 src/voice/datasets/__init__.py           | 14 ++++--
 src/voice/datasets/dataset.py            | 42 +++++++++++++++-
 src/voice/datasets/get_dataset.py        | 47 +++++++++++++++--
 tests/voice/datasets/test_dataset.py     | 36 ++++++++++++-
 tests/voice/datasets/test_get_dataset.py | 64 +++++++++++++++++++++++-
 6 files changed, 194 insertions(+), 12 deletions(-)

diff --git a/src/voice/__init__.py b/src/voice/__init__.py
index a3a9cc5..3f54ae8 100644
--- a/src/voice/__init__.py
+++ b/src/voice/__init__.py
@@ -9,11 +9,12 @@
     make_comparison,
     stylometric_distribution,
 )
-from voice.datasets import DatasetSpec, get_dataset
+from voice.datasets import DatasetSpec, LocalDatasetSpec, get_dataset
 from voice.stylometry import get_groups, get_metrics
 
 __all__: list[str] = [
     "DatasetSpec",
+    "LocalDatasetSpec",
     "get_metrics",
     "get_groups",
     "get_dataset",
diff --git a/src/voice/datasets/__init__.py b/src/voice/datasets/__init__.py
index 696e12e..75b3d0f 100644
--- a/src/voice/datasets/__init__.py
+++ b/src/voice/datasets/__init__.py
@@ -4,12 +4,18 @@
 This package contains user-facing abstractions for dataset management,
 in particular we expose:
 
-- `DatasetSpec` - Lightweight, declarative specification of a dataset.
-- `VoiceDataset` - Split-aware wrapper around a Hugging Face dataset.
+- `DatasetSpec` - Lightweight, declarative specification of a HF dataset.
+- `LocalDatasetSpec` - Specification for a locally saved dataset.
+- `VoiceDataset` - Split-aware wrapper around a dataset.
 - `get_dataset` - Entrypoint for materialising a dataset from a specification.
 """
 
-from voice.datasets.dataset import DatasetSpec, VoiceDataset
+from voice.datasets.dataset import DatasetSpec, LocalDatasetSpec, VoiceDataset
 from voice.datasets.get_dataset import get_dataset
 
-__all__: list[str] = ["DatasetSpec", "VoiceDataset", "get_dataset"]
+__all__: list[str] = [
+    "DatasetSpec",
+    "LocalDatasetSpec",
+    "VoiceDataset",
+    "get_dataset",
+]
diff --git a/src/voice/datasets/dataset.py b/src/voice/datasets/dataset.py
index 9e7d821..54ef88c 100644
--- a/src/voice/datasets/dataset.py
+++ b/src/voice/datasets/dataset.py
@@ -5,6 +5,7 @@
 
 - `DatasetSpec` - A lightweight declarative specification of a dataset
   hosted on Hugging Face.
+- `LocalDatasetSpec` - A specification for a locally saved dataset.
 - `_PinnedDatasetSpec` - An internal revision-pinned specification
   resolved to a specific git commit SHA.
 - `Example` - A representation of a single question-answer example
@@ -18,6 +19,7 @@
 import re
 from collections.abc import Callable, Iterator, Mapping, Sequence
 from dataclasses import dataclass
+from pathlib import Path
 from typing import Any, NamedTuple, overload
 
 from datasets import Dataset
@@ -29,7 +31,7 @@
 # -----------------------------------------------------------------------------
 
 
-_SHA_RE = re.compile(r"[0-9a-f]{7,40}$")
+_SHA_RE = re.compile(r"[0-9a-f]{7,40}$|local$")
 
 
 @dataclass(frozen=True, slots=True)
@@ -99,6 +101,44 @@ def _pin(
         return _PinnedDatasetSpec.from_spec(self, resolve_revision)
 
 
+@dataclass(frozen=True, slots=True)
+class LocalDatasetSpec:
+    """
+    Lightweight specification of a locally saved dataset.
+
+    Expects one ``.jsonl`` file per split at ``{path}/{split}.jsonl``
+    (e.g. ``path/train.jsonl``, ``path/validation.jsonl``).
+
+    .. attribute :: path
+
+        Directory containing the per-split ``.jsonl`` files.
+
+    .. attribute :: splits
+
+        Dataset splits to include (options: 'train','validation','test')
+    """
+
+    path: Path
+    splits: tuple[Split, ...] = (Split.TRAIN,)
+
+    def __post_init__(self) -> None:
+        """
+        Validate and normalise the specification.
+
+        :return: None
+        :raises ValueError: If `splits` is empty
+        """
+        if not self.splits:
+            raise ValueError("`splits` must be non-empty")
+        splits = (
+            (self.splits,)
+            if isinstance(self.splits, (str, Split))
+            else self.splits
+        )
+        norm = tuple(Split.parse(s) for s in splits)
+        object.__setattr__(self, "splits", norm)
+
+
 @dataclass(frozen=True, slots=True)
 class _PinnedDatasetSpec:
     """
diff --git a/src/voice/datasets/get_dataset.py b/src/voice/datasets/get_dataset.py
index fe5fd83..8591622 100644
--- a/src/voice/datasets/get_dataset.py
+++ b/src/voice/datasets/get_dataset.py
@@ -23,6 +23,7 @@
 from voice.datasets._schema import Split
 from voice.datasets.dataset import (
     DatasetSpec,
+    LocalDatasetSpec,
     VoiceDataset,
     _PinnedDatasetSpec,
 )
@@ -133,21 +134,59 @@ def _validate_canonical(ds: Dataset) -> None:
 # -----------------------------------------------------------------------------
 
 
-def get_dataset(spec: DatasetSpec) -> VoiceDataset:
+def _get_local_dataset(spec: LocalDatasetSpec) -> VoiceDataset:
     """
-    Materialise a Hugging Face dataset as a VoiceDataset.
+    Materialise a locally saved dataset as a VoiceDataset.
 
-    Steps:
+    Expects one ``.jsonl`` file per split at ``{path}/{split}.jsonl``
+    (e.g. ``path/train.jsonl``, ``path/validation.jsonl``).
+
+    :param spec: Local dataset specification
+    :return: Split-aware VoiceDataset
+    :raises RuntimeError: If a split cannot be loaded
+    """
+    pinned = _PinnedDatasetSpec(
+        repo_id=str(spec.path),
+        revision="local",
+        splits=spec.splits,
+    )
+    datasets: dict[Split, Dataset] = {}
+    for split in spec.splits:
+        jsonl_path = spec.path / f"{split.value}.jsonl"
+        try:
+            ds = load_dataset(
+                "json",
+                data_files={split.value: str(jsonl_path)},
+                split=split.value,
+            )
+        except Exception as e:
+            raise RuntimeError(
+                f"Failed to load split '{split.value}' from {jsonl_path}"
+            ) from e
+        ds = _canonicalise_dataset(ds)
+        _validate_canonical(ds)
+        datasets[split] = ds
+    return VoiceDataset(datasets=datasets, spec=pinned)
+
+
+def get_dataset(spec: DatasetSpec | LocalDatasetSpec) -> VoiceDataset:
+    """
+    Materialise a dataset as a VoiceDataset.
+
+    Accepts either a Hugging Face spec or a local spec. For HF specs:
         1. Resolve DatasetSpec to _PinnedDatasetSpec.
         2. Load requested splits from Hugging Face.
         3. Canonicalise each split into (system, question, answer).
         4. Validate canonical structure.
         5. Construct VoiceDataset.
 
-    :param spec: Dataset specification
+    :param spec: Dataset specification (HF or local)
     :return: Split-aware VoiceDataset
     :raises RuntimeError: If a split cannot be loaded
     """
+    if isinstance(spec, LocalDatasetSpec):
+        return _get_local_dataset(spec)
+
     # Resolve dataset revision
     pinned: _PinnedDatasetSpec = spec._pin(_resolve_revision)
 
diff --git a/tests/voice/datasets/test_dataset.py b/tests/voice/datasets/test_dataset.py
index db0e983..9eb430e 100644
--- a/tests/voice/datasets/test_dataset.py
+++ b/tests/voice/datasets/test_dataset.py
@@ -3,6 +3,7 @@
 
 Scope:
 - DatasetSpec validation/normalisation
+- LocalDatasetSpec validation/normalisation
 - Revision pinning invariants with _PinnedDatasetSpec
 - VoiceDataset split access
 """
@@ -10,11 +11,12 @@
 from __future__ import annotations
 
 import dataclasses
+from pathlib import Path
 
 import pytest
 from datasets import Dataset
 
-from voice import DatasetSpec
+from voice import DatasetSpec, LocalDatasetSpec
 from voice.datasets import VoiceDataset
 from voice.datasets._schema import Split
 from voice.datasets.dataset import (
@@ -84,6 +86,37 @@ def test_dataset_spec_normalises_splits_from_iterable():
     assert spec.splits == (Split.TRAIN, Split.TEST)
 
 
+# -----------------------------------------------------------------------------
+# LocalDatasetSpec validation & normalisation
+# -----------------------------------------------------------------------------
+
+
+def test_local_dataset_spec_rejects_empty_splits():
+    with pytest.raises(ValueError, match=r"`splits` must be non-empty"):
+        LocalDatasetSpec(path=Path("/data/bush"), splits=())
+
+
+def test_local_dataset_spec_normalises_splits_from_str():
+    spec = LocalDatasetSpec(path=Path("/data/bush"), splits="train")  # type: ignore[arg-type]
+    assert spec.splits == (Split.TRAIN,)
+
+
+def test_local_dataset_spec_normalises_splits_from_iterable():
+    spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train", "test"))
+    assert spec.splits == (Split.TRAIN, Split.TEST)
+
+
+def test_local_dataset_spec_is_frozen():
+    spec = LocalDatasetSpec(path=Path("/data/bush"))
+    with pytest.raises(dataclasses.FrozenInstanceError):
+        setattr(spec, "path", Path("/other"))  # noqa: B010
+
+
+def test_local_dataset_spec_default_split_is_train():
+    spec = LocalDatasetSpec(path=Path("/data/bush"))
+    assert spec.splits == (Split.TRAIN,)
+
+
 # -----------------------------------------------------------------------------
 # Pinned spec invariants & pinning behaviour
 # -----------------------------------------------------------------------------
@@ -94,6 +127,7 @@ def test_dataset_spec_normalises_splits_from_iterable():
     [
         "a1b2c3d",
         "0123456789abcdef0123456789abcdef01234567",  # pragma: allowlist secret
+        "local",
     ],
 )
 def test_pinned_dataset_spec_accepts_sha_like_revisions(sha: str):
diff --git a/tests/voice/datasets/test_get_dataset.py b/tests/voice/datasets/test_get_dataset.py
index 2858738..52bf98a 100644
--- a/tests/voice/datasets/test_get_dataset.py
+++ b/tests/voice/datasets/test_get_dataset.py
@@ -6,11 +6,13 @@
 - Schema canonicalisation for supported dataset formats
 - Canonical schema validation
 - Public materialisation pipeline (get_dataset) with dependency patching
+- Local dataset loading via _get_local_dataset
 """
 
 from __future__ import annotations
 
 import importlib
+from pathlib import Path
 from types import SimpleNamespace
 
 import pytest
@@ -18,7 +20,7 @@
 from datasets import Dataset
 from huggingface_hub.utils import HfHubHTTPError
 
-from voice import DatasetSpec
+from voice import DatasetSpec, LocalDatasetSpec
 from voice.datasets._schema import Split
 from voice.datasets.dataset import _PinnedDatasetSpec
 
@@ -190,6 +192,66 @@ def fake_load_dataset(repo_id: str, *, split: str, revision: str):
     assert ex0.spec == vd.spec
 
 
+# -----------------------------------------------------------------------------
+# Local dataset loading
+# -----------------------------------------------------------------------------
+
+
+def test_get_local_dataset_loads_splits_and_returns_voice_dataset(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    calls: list[tuple[str, str]] = []
+
+    def fake_load_dataset(*args: object, **kwargs: object) -> Dataset:
+        calls.append((str(args[0]), str(kwargs.get("split"))))
+        return _canonical_hf_ds(3)
+
+    monkeypatch.setattr(gd, "load_dataset", fake_load_dataset)
+
+    spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train", "test"))
+    vd = gd._get_local_dataset(spec)
+
+    assert vd.spec.repo_id == "/data/bush"
+    assert vd.spec.revision == "local"
+    assert set(vd.splits) == {Split.TRAIN, Split.TEST}
+    assert calls == [("json", "train"), ("json", "test")]
+
+    ex = vd.train[0]
+    assert ex.split == Split.TRAIN
+    assert ex.spec.revision == "local"
+
+
+def test_get_local_dataset_wraps_load_failures_in_runtime_error(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    def fake_load_dataset(*args: object, **kwargs: object) -> Dataset:
+        raise RuntimeError("not found")
+
+    monkeypatch.setattr(gd, "load_dataset", fake_load_dataset)
+
+    spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train",))
+    with pytest.raises(RuntimeError, match=r"Failed to load split 'train'"):
+        gd._get_local_dataset(spec)
+
+
+def test_get_dataset_routes_to_local_for_local_spec(
+    monkeypatch: pytest.MonkeyPatch,
+):
+    sentinel = object()
+
+    def fake_get_local(spec: LocalDatasetSpec) -> object:
+        return sentinel
+
+    monkeypatch.setattr(gd, "_get_local_dataset", fake_get_local)
+
+    spec = LocalDatasetSpec(path=Path("/data/bush"), splits=("train",))
+    result = gd.get_dataset(spec)
+    assert result is sentinel
+
+
+# -----------------------------------------------------------------------------
+
+
 def test_get_dataset_wraps_load_failures_in_runtime_error(
     monkeypatch: pytest.MonkeyPatch,
 ):

From a8b7d596bf9176bebb3120ee4d6a2ac411191316 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 11:14:41 +0100
Subject: [PATCH 54/68] feat(fine-tuning): add fine-tuning module for single
 runs and grid search

---
 cspell/project-words.txt            |   2 +
 src/voice/finetune/__init__.py      |   9 ++
 src/voice/finetune/_grid.py         | 102 +++++++++++++++
 src/voice/finetune/_schema.py       | 193 ++++++++++++++++++++++++++++
 src/voice/finetune/_state.py        |  69 ++++++++++
 tests/voice/finetune/test_grid.py   | 168 ++++++++++++++++++++++++
 tests/voice/finetune/test_schema.py | 163 +++++++++++++++++++++++
 tests/voice/finetune/test_state.py  |  86 +++++++++++++
 8 files changed, 792 insertions(+)
 create mode 100644 src/voice/finetune/__init__.py
 create mode 100644 src/voice/finetune/_grid.py
 create mode 100644 src/voice/finetune/_schema.py
 create mode 100644 src/voice/finetune/_state.py
 create mode 100644 tests/voice/finetune/test_grid.py
 create mode 100644 tests/voice/finetune/test_schema.py
 create mode 100644 tests/voice/finetune/test_state.py

diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index 64ecef8..b36da10 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -14,3 +14,5 @@ aeiou
 stylometrically
 lora
 resumability
+resumable
+hyperparams
diff --git a/src/voice/finetune/__init__.py b/src/voice/finetune/__init__.py
new file mode 100644
index 0000000..3547da2
--- /dev/null
+++ b/src/voice/finetune/__init__.py
@@ -0,0 +1,9 @@
+"""
+Fine-tuning orchestration package for VOICE.
+
+Provides utilities for running single fine-tuning jobs and hyperparameter
+sweeps, including:
+- CLI entrypoints
+- Local experiment tracking
+- Post-hoc evaluation on stylometric evaluation suite
+"""
diff --git a/src/voice/finetune/_grid.py b/src/voice/finetune/_grid.py
new file mode 100644
index 0000000..f158be5
--- /dev/null
+++ b/src/voice/finetune/_grid.py
@@ -0,0 +1,102 @@
+"""
+Hyperparameter grid expansion and run naming utilities.
+
+This module is private by convention and not part of the public API.
+"""
+
+from __future__ import annotations
+
+import itertools
+import re
+import uuid
+from typing import Any
+
+_REQUIRED_AXES: frozenset[str] = frozenset(
+    {
+        "learning_rate",
+        "lora_r",
+        "micro_batch_size",
+        "gradient_accumulation_steps",
+        "target_layers",
+    }
+)
+
+# -----------------------------------------------------------------------------
+# Run naming
+# -----------------------------------------------------------------------------
+
+
+def _short_name(repo_or_path: str) -> str:
+    """
+    Extract and normalise the trailing segment of a repo id or path.
+
+    :param repo_or_path: A HF repo id (``namespace/name``) or file path.
+    :return: Lowercase, hyphen-separated identifier.
+    """
+    name = repo_or_path.rsplit("/", 1)[-1]
+    return re.sub(r"[^a-z0-9]+", "-", name.lower()).strip("-")
+
+
+def new_uid() -> str:
+    """
+    Generate a short unique identifier (8 hex characters).
+
+    :return: Random 8-character hex string.
+    """
+    return uuid.uuid4().hex[:8]
+
+
+def generate_run_name(model: str, uid: str) -> str:
+    """
+    Generate a short, human-readable run name.
+
+    Format: ``{model}_{uid}``  e.g. ``llama-3-1-8b-instruct_a3f2b9c1``
+
+    :param model: Base model identifier (HF repo id or local path).
+    :param uid: Short unique identifier, e.g. from :func:`new_uid`.
+    :return: Run name string.
+    """
+    return f"{_short_name(model)}_{uid}"
+
+
+# -----------------------------------------------------------------------------
+# Grid expansion
+# -----------------------------------------------------------------------------
+
+
+def expand_grid(sweep_cfg: dict[str, Any]) -> list[dict[str, Any]]:
+    """
+    Expand the sweep section into a flat list of per-run hyperparameter dicts.
+
+    :param sweep_cfg: The ``sweep`` section of a sweep config YAML.
+    :return: List of hyperparameter dicts, one per combination.
+    :raises ValueError: If required sweep axes are missing.
+    """
+    missing = _REQUIRED_AXES - sweep_cfg.keys()
+    if missing:
+        raise ValueError(
+            f"Sweep config missing required axes: {sorted(missing)}"
+        )
+
+    runs: list[dict[str, Any]] = []
+    combos = itertools.product(
+        sweep_cfg["learning_rate"],
+        sweep_cfg["lora_r"],
+        sweep_cfg["micro_batch_size"],
+        sweep_cfg["gradient_accumulation_steps"],
+        sweep_cfg["target_layers"],
+    )
+    for idx, (lr, rank, mbs, gas, tl) in enumerate(combos):
+        runs.append(
+            {
+                "run_idx": idx,
+                "learning_rate": lr,
+                "lora_r": rank,
+                "lora_alpha": rank,
+                "micro_batch_size": mbs,
+                "gradient_accumulation_steps": gas,
+                "target_layers_name": tl["name"],
+                "lora_target_modules": tl["modules"],
+            }
+        )
+    return runs
diff --git a/src/voice/finetune/_schema.py b/src/voice/finetune/_schema.py
new file mode 100644
index 0000000..fbe9ef5
--- /dev/null
+++ b/src/voice/finetune/_schema.py
@@ -0,0 +1,193 @@
+"""
+Dataclass schemas for fine-tuning configuration and run metadata.
+
+This module is private by convention and not part of the public API.
+
+This module defines:
+
+- `SweepConfig` - Parsed representation of a sweep config YAML.
+- `SingleRunConfig` - Parsed representation of a single-run config YAML.
+- `RunMetadata` - Mutable metadata written to each run directory.
+"""
+
+from __future__ import annotations
+
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal
+
+import yaml
+
+# -----------------------------------------------------------------------------
+# Config schemas
+# -----------------------------------------------------------------------------
+
+
+@dataclass(frozen=True, slots=True)
+class SweepConfig:
+    """
+    Parsed sweep configuration.
+
+    .. attribute :: sweep
+
+        Hyperparameter grid axes (each value is a list of candidates).
+
+    .. attribute :: axolotl
+
+        Shared axolotl settings merged into every per-run config.
+    """
+
+    sweep: dict[str, Any]
+    axolotl: dict[str, Any]
+
+    @classmethod
+    def from_yaml(cls, path: Path) -> SweepConfig:
+        """
+        Load a sweep config from a YAML file.
+
+        :param path: Path to the YAML config file.
+        :return: Parsed SweepConfig.
+        :raises ValueError: If required top-level keys are missing.
+        """
+        raw: dict[str, Any] = yaml.safe_load(path.read_text())
+        missing = {"sweep", "axolotl"} - raw.keys()
+        if missing:
+            raise ValueError(
+                f"Sweep config missing required keys: {sorted(missing)}"
+            )
+        return cls(sweep=raw["sweep"], axolotl=raw["axolotl"])
+
+
+@dataclass(frozen=True, slots=True)
+class SingleRunConfig:
+    """
+    Parsed single-run configuration.
+
+    .. attribute :: axolotl
+
+        Full axolotl training settings for the run.
+    """
+
+    axolotl: dict[str, Any]
+
+    @classmethod
+    def from_yaml(cls, path: Path) -> SingleRunConfig:
+        """
+        Load a single-run config from a YAML file.
+
+        :param path: Path to the YAML config file.
+        :return: Parsed SingleRunConfig.
+        :raises ValueError: If the ``axolotl`` key is missing.
+        """
+        raw: dict[str, Any] = yaml.safe_load(path.read_text())
+        if "axolotl" not in raw:
+            raise ValueError(
+                "Single-run config missing required key: 'axolotl'"
+            )
+        return cls(axolotl=raw["axolotl"])
+
+
+# -----------------------------------------------------------------------------
+# Run metadata
+# -----------------------------------------------------------------------------
+
+
+@dataclass(slots=True)
+class RunMetadata:
+    """
+    Mutable metadata for a single fine-tuning run.
+
+    Written to ``runs/{run_name}/metadata.json`` and updated throughout
+    the run lifecycle.
+
+    .. attribute :: run_name
+
+        Unique identifier for the run.
+
+    .. attribute :: model
+
+        Base model identifier (HF repo_id or local path).
+
+    .. attribute :: dataset
+
+        Dataset identifier (HF repo_id or local path).
+
+    .. attribute :: hyperparams
+
+        Concrete hyperparameter values for this run.
+
+    .. attribute :: status
+
+        Lifecycle state: ``pending``, ``running``, ``done``, or ``failed``.
+
+    .. attribute :: started_at
+
+        ISO-format timestamp when training began, or ``None``.
+
+    .. attribute :: finished_at
+
+        ISO-format timestamp when the run completed, or ``None``.
+
+    .. attribute :: eval_loss
+
+        Mapping of epoch number (as string) to validation loss.
+
+    .. attribute :: alignment_val
+
+        Mapping of epoch number (as string) to validation alignment score.
+
+    .. attribute :: alignment_test
+
+        Mapping of epoch number (as string) to test alignment score.
+    """
+
+    run_name: str
+    model: str
+    dataset: str
+    hyperparams: dict[str, Any]
+    status: Literal["pending", "running", "done", "failed"] = "pending"
+    started_at: str | None = None
+    finished_at: str | None = None
+    eval_loss: dict[str, float] = field(default_factory=dict)
+    alignment_val: dict[str, float] = field(default_factory=dict)
+    alignment_test: dict[str, float] = field(default_factory=dict)
+
+    def to_dict(self) -> dict[str, Any]:
+        """
+        Serialise metadata to a plain dict for JSON output.
+
+        :return: Dict representation of the metadata.
+        """
+        return {
+            "run_name": self.run_name,
+            "model": self.model,
+            "dataset": self.dataset,
+            "hyperparams": self.hyperparams,
+            "status": self.status,
+            "started_at": self.started_at,
+            "finished_at": self.finished_at,
+            "eval_loss": self.eval_loss,
+            "alignment_val": self.alignment_val,
+            "alignment_test": self.alignment_test,
+        }
+
+    @classmethod
+    def from_dict(cls, d: dict[str, Any]) -> RunMetadata:
+        """
+        Reconstruct metadata from a plain dict.
+
+        :param d: Dict representation of the metadata.
+        :return: Reconstructed RunMetadata.
+        """
+        return cls(
+            run_name=d["run_name"],
+            model=d["model"],
+            dataset=d["dataset"],
+            hyperparams=d["hyperparams"],
+            status=d["status"],
+            started_at=d.get("started_at"),
+            finished_at=d.get("finished_at"),
+            eval_loss=d.get("eval_loss", {}),
+            alignment_val=d.get("alignment_val", {}),
+            alignment_test=d.get("alignment_test", {}),
+        )
diff --git a/src/voice/finetune/_state.py b/src/voice/finetune/_state.py
new file mode 100644
index 0000000..1d68114
--- /dev/null
+++ b/src/voice/finetune/_state.py
@@ -0,0 +1,69 @@
+"""
+Sweep state persistence for grid searches with resume support.
+
+This module is private by convention and not part of the public API.
+
+State is written to a JSON file with two keys:
+
+- ``completed``: list of run names that finished successfully.
+- ``failed``: mapping of run name to error string for failed runs.
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+from typing import Any, cast
+
+
+def load_state(state_file: Path) -> dict[str, Any]:
+    """
+    Load sweep state from a JSON file, or return a fresh state dict.
+
+    :param state_file: Path to the state JSON file.
+    :return: State dict with ``completed`` and ``failed`` keys.
+    """
+    if state_file.exists():
+        return cast(dict[str, Any], json.loads(state_file.read_text()))
+    return {"completed": [], "failed": {}}
+
+
+def save_state(state: dict[str, Any], state_file: Path) -> None:
+    """
+    Persist sweep state to a JSON file.
+
+    :param state: State dict to serialise.
+    :param state_file: Destination path.
+    :return: None
+    """
+    state_file.write_text(json.dumps(state, indent=2))
+
+
+def mark_completed(state: dict[str, Any], name: str, state_file: Path) -> None:
+    """
+    Record a run as completed and persist state.
+
+    :param state: Current sweep state dict.
+    :param name: Run name to mark as completed.
+    :param state_file: Destination path for persisting state.
+    :return: None
+    """
+    state["completed"].append(name)
+    state["failed"].pop(name, None)
+    save_state(state, state_file)
+
+
+def mark_failed(
+    state: dict[str, Any], name: str, error: str, state_file: Path
+) -> None:
+    """
+    Record a run as failed and persist state.
+
+    :param state: Current sweep state dict.
+    :param name: Run name to mark as failed.
+    :param error: Error string describing the failure.
+    :param state_file: Destination path for persisting state.
+    :return: None
+    """
+    state["failed"][name] = error
+    save_state(state, state_file)
diff --git a/tests/voice/finetune/test_grid.py b/tests/voice/finetune/test_grid.py
new file mode 100644
index 0000000..46c4b93
--- /dev/null
+++ b/tests/voice/finetune/test_grid.py
@@ -0,0 +1,168 @@
+"""
+Tests for voice.finetune._grid.
+
+Scope:
+- expand_grid count and structure
+- Hyperparameter pass-through (micro_batch_size, gradient_accumulation_steps)
+- Missing axis validation
+- generate_run_name format and uniqueness
+- _short_name normalisation
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from voice.finetune._grid import (
+    _short_name,
+    expand_grid,
+    generate_run_name,
+    new_uid,
+)
+
+# -----------------------------------------------------------------------------
+# Helpers
+# -----------------------------------------------------------------------------
+
+
+def _minimal_sweep(
+    *,
+    lrs: list | None = None,
+    ranks: list | None = None,
+    micro_batch_sizes: list | None = None,
+    accumulation_steps: list | None = None,
+    layers: list | None = None,
+) -> dict:
+    return {
+        "learning_rate": lrs or [1e-4, 5e-4],
+        "lora_r": ranks or [4, 8],
+        "micro_batch_size": micro_batch_sizes or [4, 8],
+        "gradient_accumulation_steps": accumulation_steps or [1, 4],
+        "target_layers": layers
+        or [
+            {"name": "attention", "modules": ["q_proj"]},
+            {"name": "attention_mlp", "modules": ["q_proj", "gate_proj"]},
+        ],
+    }
+
+
+# -----------------------------------------------------------------------------
+# expand_grid
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "n_lr, n_r, n_mbs, n_gas, n_tl",
+    [
+        (1, 1, 1, 1, 1),
+        (3, 4, 2, 2, 2),
+        (2, 2, 2, 2, 2),
+    ],
+)
+def test_expand_grid_count(
+    n_lr: int, n_r: int, n_mbs: int, n_gas: int, n_tl: int
+):
+    cfg = _minimal_sweep(
+        lrs=list(range(n_lr)),
+        ranks=list(range(1, n_r + 1)),
+        micro_batch_sizes=list(range(1, n_mbs + 1)),
+        accumulation_steps=list(range(1, n_gas + 1)),
+        layers=[{"name": f"l{i}", "modules": ["q"]} for i in range(n_tl)],
+    )
+    runs = expand_grid(cfg)
+    assert len(runs) == n_lr * n_r * n_mbs * n_gas * n_tl
+
+
+def test_expand_grid_indices_are_sequential():
+    runs = expand_grid(_minimal_sweep())
+    assert [r["run_idx"] for r in runs] == list(range(len(runs)))
+
+
+def test_expand_grid_micro_batch_size_passed_through():
+    cfg = _minimal_sweep(micro_batch_sizes=[2, 4, 8])
+    runs = expand_grid(cfg)
+    observed = {r["micro_batch_size"] for r in runs}
+    assert observed == {2, 4, 8}
+
+
+def test_expand_grid_gradient_accumulation_steps_passed_through():
+    cfg = _minimal_sweep(accumulation_steps=[1, 2, 8])
+    runs = expand_grid(cfg)
+    observed = {r["gradient_accumulation_steps"] for r in runs}
+    assert observed == {1, 2, 8}
+
+
+def test_expand_grid_lora_alpha_equals_lora_r():
+    runs = expand_grid(_minimal_sweep())
+    for run in runs:
+        assert run["lora_alpha"] == run["lora_r"]
+
+
+def test_expand_grid_no_effective_batch_size_field():
+    runs = expand_grid(_minimal_sweep())
+    for run in runs:
+        assert "effective_batch_size" not in run
+
+
+@pytest.mark.parametrize(
+    "missing_key",
+    [
+        "learning_rate",
+        "lora_r",
+        "micro_batch_size",
+        "gradient_accumulation_steps",
+        "target_layers",
+    ],
+)
+def test_expand_grid_raises_on_missing_axis(missing_key: str):
+    cfg = _minimal_sweep()
+    del cfg[missing_key]
+    with pytest.raises(ValueError, match=r"missing required axes"):
+        expand_grid(cfg)
+
+
+# -----------------------------------------------------------------------------
+# generate_run_name / new_uid
+# -----------------------------------------------------------------------------
+
+
+def test_generate_run_name_contains_model_and_uid():
+    name = generate_run_name(
+        model="meta-llama/Llama-3.1-8B-Instruct", uid="a3f2b9c1"
+    )
+    assert "llama" in name
+    assert "a3f2b9c1" in name
+
+
+def test_generate_run_name_format():
+    name = generate_run_name(model="ns/my-model", uid="deadbeef")
+    assert name == "my-model_deadbeef"
+
+
+def test_new_uid_is_8_hex_chars():
+    uid = new_uid()
+    assert len(uid) == 8
+    assert all(c in "0123456789abcdef" for c in uid)
+
+
+def test_new_uid_is_unique():
+    uids = {new_uid() for _ in range(100)}
+    assert len(uids) == 100
+
+
+# -----------------------------------------------------------------------------
+# _short_name
+# -----------------------------------------------------------------------------
+
+
+@pytest.mark.parametrize(
+    "inp, expected",
+    [
+        ("meta-llama/Llama-3.1-8B-Instruct", "llama-3-1-8b-instruct"),
+        ("AccelerateScience/bush-dataset", "bush-dataset"),
+        ("/data/local/bush", "bush"),
+        ("simple", "simple"),
+    ],
+)
+def test_short_name(inp: str, expected: str):
+    assert _short_name(inp) == expected
diff --git a/tests/voice/finetune/test_schema.py b/tests/voice/finetune/test_schema.py
new file mode 100644
index 0000000..84f6854
--- /dev/null
+++ b/tests/voice/finetune/test_schema.py
@@ -0,0 +1,163 @@
+"""
+Tests for voice.finetune._schema.
+
+Scope:
+- SweepConfig.from_yaml validation and parsing
+- SingleRunConfig.from_yaml validation and parsing
+- RunMetadata to_dict / from_dict round-trip
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+import pytest
+
+from voice.finetune._schema import RunMetadata, SingleRunConfig, SweepConfig
+
+# -----------------------------------------------------------------------------
+# Helpers
+# -----------------------------------------------------------------------------
+
+
+def _write_yaml(path: Path, content: str) -> Path:
+    path.write_text(content)
+    return path
+
+
+# -----------------------------------------------------------------------------
+# SweepConfig
+# -----------------------------------------------------------------------------
+
+
+def test_sweep_config_from_yaml_reads_sections(tmp_path: Path):
+    p = _write_yaml(
+        tmp_path / "sweep.yaml",
+        "sweep:\n  learning_rate: [1.0e-4]\naxolotl:\n  base_model: x\n",
+    )
+    cfg = SweepConfig.from_yaml(p)
+    assert cfg.sweep == {"learning_rate": [1e-4]}
+    assert cfg.axolotl == {"base_model": "x"}
+
+
+def test_sweep_config_from_yaml_raises_on_missing_sweep_key(tmp_path: Path):
+    p = _write_yaml(tmp_path / "cfg.yaml", "axolotl:\n  base_model: x\n")
+    with pytest.raises(ValueError, match=r"missing required keys"):
+        SweepConfig.from_yaml(p)
+
+
+def test_sweep_config_from_yaml_raises_on_missing_axolotl_key(tmp_path: Path):
+    p = _write_yaml(
+        tmp_path / "cfg.yaml", "sweep:\n  learning_rate: [1.0e-4]\n"
+    )
+    with pytest.raises(ValueError, match=r"missing required keys"):
+        SweepConfig.from_yaml(p)
+
+
+def test_sweep_config_is_frozen(tmp_path: Path):
+    p = _write_yaml(
+        tmp_path / "cfg.yaml",
+        "sweep:\n  lr: [1e-4]\naxolotl:\n  base_model: x\n",
+    )
+    cfg = SweepConfig.from_yaml(p)
+    import dataclasses
+
+    with pytest.raises(dataclasses.FrozenInstanceError):
+        setattr(cfg, "sweep", {})  # noqa: B010
+
+
+# -----------------------------------------------------------------------------
+# SingleRunConfig
+# -----------------------------------------------------------------------------
+
+
+def test_single_run_config_from_yaml_reads_axolotl_section(tmp_path: Path):
+    p = _write_yaml(
+        tmp_path / "run.yaml", "axolotl:\n  base_model: my-model\n"
+    )
+    cfg = SingleRunConfig.from_yaml(p)
+    assert cfg.axolotl == {"base_model": "my-model"}
+
+
+def test_single_run_config_from_yaml_raises_on_missing_axolotl_key(
+    tmp_path: Path,
+):
+    p = _write_yaml(tmp_path / "run.yaml", "model: x\n")
+    with pytest.raises(ValueError, match=r"missing required key: 'axolotl'"):
+        SingleRunConfig.from_yaml(p)
+
+
+# -----------------------------------------------------------------------------
+# RunMetadata
+# -----------------------------------------------------------------------------
+
+
+def test_run_metadata_defaults():
+    meta = RunMetadata(
+        run_name="r",
+        model="m",
+        dataset="d",
+        hyperparams={"lr": 1e-4},
+    )
+    assert meta.status == "pending"
+    assert meta.started_at is None
+    assert meta.finished_at is None
+    assert meta.eval_loss == {}
+    assert meta.alignment_val == {}
+    assert meta.alignment_test == {}
+
+
+def test_run_metadata_to_dict_from_dict_round_trips():
+    meta = RunMetadata(
+        run_name="run-001",
+        model="ns/model",
+        dataset="ns/ds",
+        hyperparams={"lr": 1e-4, "lora_r": 8},
+        status="done",
+        started_at="2026-05-22T14:30:00",
+        finished_at="2026-05-22T16:00:00",
+        eval_loss={"1": 0.45, "2": 0.40},
+        alignment_val={"1": 0.82, "2": 0.85},
+        alignment_test={"1": 0.80, "2": 0.83},
+    )
+    d = meta.to_dict()
+    restored = RunMetadata.from_dict(d)
+
+    assert restored.run_name == meta.run_name
+    assert restored.model == meta.model
+    assert restored.dataset == meta.dataset
+    assert restored.hyperparams == meta.hyperparams
+    assert restored.status == meta.status
+    assert restored.started_at == meta.started_at
+    assert restored.finished_at == meta.finished_at
+    assert restored.eval_loss == meta.eval_loss
+    assert restored.alignment_val == meta.alignment_val
+    assert restored.alignment_test == meta.alignment_test
+
+
+def test_run_metadata_to_dict_is_json_serialisable():
+    meta = RunMetadata(
+        run_name="r",
+        model="m",
+        dataset="d",
+        hyperparams={"lr": 1e-4},
+    )
+    # Should not raise
+    json.dumps(meta.to_dict())
+
+
+def test_run_metadata_from_dict_tolerates_missing_optional_fields():
+    d = {
+        "run_name": "r",
+        "model": "m",
+        "dataset": "d",
+        "hyperparams": {},
+        "status": "pending",
+        "started_at": None,
+        "finished_at": None,
+    }
+    meta = RunMetadata.from_dict(d)
+    assert meta.eval_loss == {}
+    assert meta.alignment_val == {}
+    assert meta.alignment_test == {}
diff --git a/tests/voice/finetune/test_state.py b/tests/voice/finetune/test_state.py
new file mode 100644
index 0000000..f85d961
--- /dev/null
+++ b/tests/voice/finetune/test_state.py
@@ -0,0 +1,86 @@
+"""
+Tests for voice.finetune._state.
+
+Scope:
+- load_state returns fresh state for missing file
+- save/load round-trip
+- mark_completed and mark_failed mutations and persistence
+"""
+
+from __future__ import annotations
+
+import json
+from pathlib import Path
+
+from voice.finetune._state import (
+    load_state,
+    mark_completed,
+    mark_failed,
+    save_state,
+)
+
+
+def test_load_state_returns_fresh_state_for_missing_file(tmp_path: Path):
+    state = load_state(tmp_path / "state.json")
+    assert state["completed"] == []
+    assert state["failed"] == {}
+
+
+def test_save_and_load_state_round_trips(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": ["run-001"], "failed": {"run-002": "boom"}}
+    save_state(state, state_file)
+    restored = load_state(state_file)
+    assert restored == state
+
+
+def test_save_state_writes_valid_json(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    save_state({"completed": [], "failed": {}}, state_file)
+    parsed = json.loads(state_file.read_text())
+    assert "completed" in parsed
+    assert "failed" in parsed
+
+
+def test_mark_completed_appends_name(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": [], "failed": {}}
+    mark_completed(state, "run-001", state_file)
+    assert "run-001" in state["completed"]
+
+
+def test_mark_completed_removes_from_failed(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": [], "failed": {"run-001": "old error"}}
+    mark_completed(state, "run-001", state_file)
+    assert "run-001" not in state["failed"]
+
+
+def test_mark_completed_persists_state(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": [], "failed": {}}
+    mark_completed(state, "run-001", state_file)
+    reloaded = load_state(state_file)
+    assert "run-001" in reloaded["completed"]
+
+
+def test_mark_failed_adds_error(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": [], "failed": {}}
+    mark_failed(state, "run-001", "something broke", state_file)
+    assert state["failed"]["run-001"] == "something broke"
+
+
+def test_mark_failed_persists_state(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": [], "failed": {}}
+    mark_failed(state, "run-001", "boom", state_file)
+    reloaded = load_state(state_file)
+    assert reloaded["failed"]["run-001"] == "boom"
+
+
+def test_mark_failed_does_not_remove_from_completed(tmp_path: Path):
+    state_file = tmp_path / "state.json"
+    state: dict = {"completed": ["run-001"], "failed": {}}
+    mark_failed(state, "run-001", "boom", state_file)
+    assert "run-001" in state["completed"]

From 17a8fbf5a5fe0a64923eb1c304ee62be9d9b5301 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 12:42:24 +0100
Subject: [PATCH 55/68] feat(fine-tuning): add run directory management

---
 src/voice/finetune/_runs.py       | 159 ++++++++++++++++++++++++++++
 tests/voice/finetune/test_runs.py | 170 ++++++++++++++++++++++++++++++
 2 files changed, 329 insertions(+)
 create mode 100644 src/voice/finetune/_runs.py
 create mode 100644 tests/voice/finetune/test_runs.py

diff --git a/src/voice/finetune/_runs.py b/src/voice/finetune/_runs.py
new file mode 100644
index 0000000..c483720
--- /dev/null
+++ b/src/voice/finetune/_runs.py
@@ -0,0 +1,159 @@
+"""
+Run directory structure and I/O utilities.
+
+Each fine-tuning run is stored under ``runs/{run_name}/`` with the following
+layout::
+
+    runs/{run_name}/
+        config.yaml             per-run axolotl config written before training
+        metadata.json           RunMetadata updated throughout the run
+        adapter/                axolotl output_dir; checkpoints written here
+        completions/
+            epoch_{n}/
+                validation.jsonl
+                test.jsonl
+        alignment/
+            epoch_{n}/
+                validation.json
+                test.json
+
+This module is private by convention and not part of the public API.
+"""
+
+from __future__ import annotations
+
+import json
+from dataclasses import dataclass
+from pathlib import Path
+from typing import Any
+
+import yaml
+
+from voice.finetune._schema import RunMetadata
+
+# -----------------------------------------------------------------------------
+# Run directory
+# -----------------------------------------------------------------------------
+
+
+@dataclass(slots=True)
+class RunDir:
+    """
+    Structured wrapper around a single run directory.
+
+    .. attribute :: path
+
+        Root path of the run directory (``runs/{run_name}``).
+    """
+
+    path: Path
+
+    # -------------------------------------------------------------------------
+    # Sub-paths
+    # -------------------------------------------------------------------------
+
+    @property
+    def adapter_dir(self) -> Path:
+        """Axolotl ``output_dir``; weights and checkpoints live here."""
+        return self.path / "adapter"
+
+    @property
+    def completions_dir(self) -> Path:
+        """Root directory for per-epoch completion files."""
+        return self.path / "completions"
+
+    @property
+    def alignment_dir(self) -> Path:
+        """Root directory for per-epoch alignment result files."""
+        return self.path / "alignment"
+
+    @property
+    def config_path(self) -> Path:
+        """Path to the frozen per-run axolotl config YAML."""
+        return self.path / "config.yaml"
+
+    @property
+    def metadata_path(self) -> Path:
+        """Path to the run metadata JSON file."""
+        return self.path / "metadata.json"
+
+    def completions_path(self, split: str, epoch: int) -> Path:
+        """
+        Return the path for a completions file.
+
+        :param split: Split name (e.g. ``"validation"``).
+        :param epoch: Epoch number (1-indexed).
+        :return: Path to ``completions/epoch_{n}/{split}.jsonl``.
+        """
+        return self.completions_dir / f"epoch_{epoch}" / f"{split}.jsonl"
+
+    def alignment_path(self, split: str, epoch: int) -> Path:
+        """
+        Return the path for an alignment result file.
+
+        :param split: Split name (e.g. ``"validation"``).
+        :param epoch: Epoch number (1-indexed).
+        :return: Path to ``alignment/epoch_{n}/{split}.json``.
+        """
+        return self.alignment_dir / f"epoch_{epoch}" / f"{split}.json"
+
+    # -------------------------------------------------------------------------
+    # Lifecycle
+    # -------------------------------------------------------------------------
+
+    def create(self) -> None:
+        """
+        Create the run directory tree.
+
+        Creates the run root, ``adapter/``, ``completions/``, and
+        ``alignment/`` directories. Per-epoch subdirectories are created
+        lazily when completions or alignment files are written.
+
+        :return: None
+        """
+        for d in (
+            self.path,
+            self.adapter_dir,
+            self.completions_dir,
+            self.alignment_dir,
+        ):
+            d.mkdir(parents=True, exist_ok=True)
+
+    # -------------------------------------------------------------------------
+    # Config I/O
+    # -------------------------------------------------------------------------
+
+    def write_config(self, cfg: dict[str, Any]) -> None:
+        """
+        Write the per-run axolotl config to ``config.yaml``.
+
+        :param cfg: Axolotl config dict to serialise.
+        :return: None
+        """
+        with open(self.config_path, "w") as f:
+            yaml.dump(cfg, f, default_flow_style=False, sort_keys=False)
+
+    # -------------------------------------------------------------------------
+    # Metadata I/O
+    # -------------------------------------------------------------------------
+
+    def write_metadata(self, meta: RunMetadata) -> None:
+        """
+        Atomically write run metadata to ``metadata.json``.
+
+        :param meta: RunMetadata to persist.
+        :return: None
+        """
+        tmp = self.metadata_path.with_suffix(".tmp.json")
+        tmp.write_text(json.dumps(meta.to_dict(), indent=2))
+        tmp.rename(self.metadata_path)
+
+    def read_metadata(self) -> RunMetadata:
+        """
+        Read run metadata from ``metadata.json``.
+
+        :return: Reconstructed RunMetadata.
+        """
+        return RunMetadata.from_dict(
+            json.loads(self.metadata_path.read_text())
+        )
diff --git a/tests/voice/finetune/test_runs.py b/tests/voice/finetune/test_runs.py
new file mode 100644
index 0000000..6a3fc73
--- /dev/null
+++ b/tests/voice/finetune/test_runs.py
@@ -0,0 +1,170 @@
+"""
+Tests for voice.finetune._runs.
+
+Scope:
+- RunDir sub-path properties
+- completions_path and alignment_path naming
+- create() builds expected directory tree
+- write_config produces valid YAML
+- write_metadata / read_metadata round-trip
+- write_metadata is atomic (tmp file used)
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+import yaml
+
+from voice.finetune._runs import RunDir
+from voice.finetune._schema import RunMetadata
+
+# -----------------------------------------------------------------------------
+# Helpers
+# -----------------------------------------------------------------------------
+
+
+def _run_dir(tmp_path: Path, name: str = "my-run") -> RunDir:
+    return RunDir(path=tmp_path / name)
+
+
+def _meta(**kwargs) -> RunMetadata:  # type: ignore[no-untyped-def]
+    defaults: dict = {
+        "run_name": "my-run",
+        "model": "ns/model",
+        "dataset": "ns/ds",
+        "hyperparams": {"lr": 1e-4},
+    }
+    defaults.update(kwargs)
+    return RunMetadata(**defaults)  # type: ignore[arg-type]
+
+
+# -----------------------------------------------------------------------------
+# Sub-path properties
+# -----------------------------------------------------------------------------
+
+
+def test_adapter_dir_is_under_run_root(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    assert rd.adapter_dir == rd.path / "adapter"
+
+
+def test_completions_dir_is_under_run_root(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    assert rd.completions_dir == rd.path / "completions"
+
+
+def test_alignment_dir_is_under_run_root(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    assert rd.alignment_dir == rd.path / "alignment"
+
+
+def test_config_path_is_yaml_in_run_root(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    assert rd.config_path == rd.path / "config.yaml"
+
+
+def test_metadata_path_is_json_in_run_root(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    assert rd.metadata_path == rd.path / "metadata.json"
+
+
+# -----------------------------------------------------------------------------
+# completions_path / alignment_path
+# -----------------------------------------------------------------------------
+
+
+def test_completions_path_structure(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    p = rd.completions_path("validation", 2)
+    assert p == rd.completions_dir / "epoch_2" / "validation.jsonl"
+
+
+def test_alignment_path_structure(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    p = rd.alignment_path("test", 3)
+    assert p == rd.alignment_dir / "epoch_3" / "test.json"
+
+
+def test_completions_path_split_names_differ(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    val = rd.completions_path("validation", 1)
+    test = rd.completions_path("test", 1)
+    assert val != test
+    assert val.name == "validation.jsonl"
+    assert test.name == "test.jsonl"
+
+
+def test_completions_path_epoch_dirs_differ(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    e1 = rd.completions_path("validation", 1)
+    e2 = rd.completions_path("validation", 2)
+    assert e1.parent != e2.parent
+
+
+# -----------------------------------------------------------------------------
+# create()
+# -----------------------------------------------------------------------------
+
+
+def test_create_makes_expected_directories(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    assert rd.path.is_dir()
+    assert rd.adapter_dir.is_dir()
+    assert rd.completions_dir.is_dir()
+    assert rd.alignment_dir.is_dir()
+
+
+def test_create_is_idempotent(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.create()  # should not raise
+
+
+# -----------------------------------------------------------------------------
+# write_config
+# -----------------------------------------------------------------------------
+
+
+def test_write_config_produces_valid_yaml(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    cfg = {"base_model": "ns/model", "learning_rate": 1e-4, "lora_r": 8}
+    rd.write_config(cfg)
+    loaded = yaml.safe_load(rd.config_path.read_text())
+    assert loaded == cfg
+
+
+# -----------------------------------------------------------------------------
+# write_metadata / read_metadata
+# -----------------------------------------------------------------------------
+
+
+def test_write_and_read_metadata_round_trips(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    meta = _meta(status="done", eval_loss={"1": 0.4})
+    rd.write_metadata(meta)
+    restored = rd.read_metadata()
+    assert restored.run_name == meta.run_name
+    assert restored.status == meta.status
+    assert restored.eval_loss == meta.eval_loss
+
+
+def test_write_metadata_no_tmp_file_left(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.write_metadata(_meta())
+    tmp = rd.metadata_path.with_suffix(".tmp.json")
+    assert not tmp.exists()
+
+
+def test_write_metadata_creates_valid_json(tmp_path: Path):
+    import json
+
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.write_metadata(_meta())
+    parsed = json.loads(rd.metadata_path.read_text())
+    assert parsed["run_name"] == "my-run"

From 302e8ae1cc25e086db626fff42a754c5c0631510 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 13:32:17 +0100
Subject: [PATCH 56/68] feat(axolotl): add custom callback

---
 cspell/project-words.txt               |   1 +
 pyproject.toml                         |   8 +
 src/voice/_defaults.py                 |  41 +++
 src/voice/finetune/callbacks.py        | 455 +++++++++++++++++++++++++
 tests/voice/finetune/test_callbacks.py |  97 ++++++
 5 files changed, 602 insertions(+)
 create mode 100644 src/voice/finetune/callbacks.py
 create mode 100644 tests/voice/finetune/test_callbacks.py

diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index b36da10..7fc1aa7 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -16,3 +16,4 @@ lora
 resumability
 resumable
 hyperparams
+redef
diff --git a/pyproject.toml b/pyproject.toml
index ab6de0b..b48f023 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -18,6 +18,7 @@ dependencies = [
     "seaborn>=0.13.2",
     "sentence-transformers>=5.3.0",
     "transformers",
+    "click>=8.0.0",
     "wandb>=0.25.1",
     "bitsandbytes>=0.49.1 ; sys_platform == 'linux'",
 ]
@@ -94,6 +95,9 @@ convention = "pep257"
 "sweep/**/*.py" = [
     "ANN401",
 ]
+"src/voice/finetune/callbacks.py" = [
+    "ANN401",
+]
 
 [tool.mypy]
 python_version = "3.12"
@@ -131,6 +135,10 @@ module = [
     "dotenv",
     "transformers",
     "transformers.*",
+    "wandb",
+    "wandb.*",
+    "axolotl",
+    "axolotl.*",
 ]
 ignore_missing_imports = true
 
diff --git a/src/voice/_defaults.py b/src/voice/_defaults.py
index d9e5f29..7118cc0 100644
--- a/src/voice/_defaults.py
+++ b/src/voice/_defaults.py
@@ -153,3 +153,44 @@ class PlottingDefaults:
 
 
 PLOTTING_DEFAULTS: PlottingDefaults = PlottingDefaults()
+
+
+@dataclass(frozen=True)
+class InferenceDefaults:
+    """
+    Default parameters for completions generation during fine-tuning.
+
+    .. attribute :: batch_size
+
+        Number of examples per inference forward pass.
+
+    .. attribute :: temperature
+
+        Sampling temperature.
+
+    .. attribute :: top_p
+
+        Nucleus sampling probability threshold.
+
+    .. attribute :: top_k
+
+        Top-k sampling limit (0 = disabled).
+
+    .. attribute :: repetition_penalty
+
+        Repetition penalty factor (1.0 = no penalty).
+
+    .. attribute :: max_new_tokens
+
+        Maximum number of tokens to generate per completion.
+    """
+
+    batch_size: int = 32
+    temperature: float = 0.7
+    top_p: float = 1.0
+    top_k: int = 0
+    repetition_penalty: float = 1.0
+    max_new_tokens: int = 1024
+
+
+INFERENCE_DEFAULTS: InferenceDefaults = InferenceDefaults()
diff --git a/src/voice/finetune/callbacks.py b/src/voice/finetune/callbacks.py
new file mode 100644
index 0000000..a1ca1f2
--- /dev/null
+++ b/src/voice/finetune/callbacks.py
@@ -0,0 +1,455 @@
+"""
+Axolotl TrainerCallback that generates completions at each epoch end.
+
+Registered via the axolotl ``plugins`` YAML key as
+``voice.finetune.callbacks.EvalCompletionsPlugin``.
+
+All GPUs participate in generation: each split's examples are sharded
+across processes. Each shard is generated in batches of
+``INFERENCE_DEFAULTS.batch_size``. After all ranks finish, results are
+gathered and rank 0 writes the final ordered JSONL and uploads a W&B
+artifact.
+
+Completions are written under the run directory derived from
+``args.output_dir`` (the adapter dir):
+
+    ``{output_dir}/../completions/epoch_{n}/{split}.jsonl``
+"""
+
+from __future__ import annotations
+
+import json
+import logging
+import os
+from pathlib import Path
+from typing import Any
+
+import torch
+import torch.distributed
+import wandb
+from datasets import load_dataset
+from transformers import (
+    TrainerCallback,
+    TrainerControl,
+    TrainerState,
+    TrainingArguments,
+)
+
+try:
+    from axolotl.integrations.base import BasePlugin
+except ImportError:  # axolotl is Linux-only
+
+    class BasePlugin:  # type: ignore
+        """Stub for environments where axolotl is not installed."""
+
+
+from voice._defaults import INFERENCE_DEFAULTS
+
+log = logging.getLogger(__name__)
+
+# Generation config built from defaults; passed directly to model.generate().
+GENERATION_CONFIG: dict[str, Any] = {
+    "do_sample": True,
+    "temperature": INFERENCE_DEFAULTS.temperature,
+    "top_p": INFERENCE_DEFAULTS.top_p,
+    "top_k": INFERENCE_DEFAULTS.top_k,
+    "repetition_penalty": INFERENCE_DEFAULTS.repetition_penalty,
+    "max_new_tokens": INFERENCE_DEFAULTS.max_new_tokens,
+}
+
+_EVAL_SPLITS: tuple[str, str] = ("validation", "test")
+
+# -----------------------------------------------------------------------------
+# Completion formatting
+# -----------------------------------------------------------------------------
+
+
+def _format_completion(
+    messages: list[dict[str, str]],
+    generated: str,
+) -> dict[str, Any]:
+    """
+    Format a completion as a chat_template JSONL record.
+
+    :param messages: System and user messages (no assistant turn).
+    :param generated: Generated assistant response.
+    :return: Dict with ``messages`` list including the assistant turn.
+    """
+    return {
+        "messages": [
+            *messages,
+            {"role": "assistant", "content": generated},
+        ]
+    }
+
+
+# -----------------------------------------------------------------------------
+# Distributed generation helpers
+# -----------------------------------------------------------------------------
+
+
+def _generate_batch(
+    batch: list[dict[str, Any]],
+    model: Any,
+    tokenizer: Any,
+) -> list[dict[str, Any]]:
+    """
+    Generate completions for a batch using left-padded inputs.
+
+    :param batch: Examples with a ``messages`` list (system + user only).
+    :param model: Unwrapped model (already on the correct device).
+    :param tokenizer: Tokenizer with ``padding_side`` set to ``"left"``.
+    :return: List of chat_template completion dicts.
+    """
+    prompts = [
+        tokenizer.apply_chat_template(
+            ex["messages"], tokenize=False, add_generation_prompt=True
+        )
+        for ex in batch
+    ]
+    inputs = tokenizer(
+        prompts, return_tensors="pt", padding=True, truncation=False
+    ).to(model.device)
+    padded_len = inputs["input_ids"].shape[1]
+
+    with torch.no_grad():
+        outputs = model.generate(**inputs, **GENERATION_CONFIG)
+
+    results = []
+    for j, ex in enumerate(batch):
+        new_tokens = outputs[j, padded_len:]
+        generated = tokenizer.decode(new_tokens, skip_special_tokens=True)
+        results.append(_format_completion(ex["messages"], generated))
+    return results
+
+
+def _generate_split(
+    examples: list[dict[str, Any]],
+    model: Any,
+    tokenizer: Any,
+    args: TrainingArguments,
+) -> list[dict[str, Any]]:
+    """
+    Generate completions for a full split, distributed across ranks.
+
+    :param examples: Full list of prompt examples for the split.
+    :param model: Unwrapped model.
+    :param tokenizer: Tokenizer with ``padding_side`` set to ``"left"``.
+    :param args: HF TrainingArguments providing rank and world-size info.
+    :return: Ordered completions on rank 0; empty list on other ranks.
+    """
+    local_indices = list(
+        range(args.process_index, len(examples), args.world_size)
+    )
+    local_examples = [examples[i] for i in local_indices]
+
+    local_completions: list[dict[str, Any]] = []
+    for i in range(0, len(local_examples), INFERENCE_DEFAULTS.batch_size):
+        batch = local_examples[i : i + INFERENCE_DEFAULTS.batch_size]
+        local_completions.extend(_generate_batch(batch, model, tokenizer))
+
+    if (
+        args.world_size > 1
+        and torch.distributed.is_available()
+        and torch.distributed.is_initialized()
+    ):
+        all_shards: list[Any] = [None] * args.world_size
+        torch.distributed.all_gather_object(
+            all_shards, (local_indices, local_completions)
+        )
+    else:
+        all_shards = [(local_indices, local_completions)]
+
+    if args.process_index != 0:
+        return []
+
+    ordered: list[dict[str, Any]] = [{}] * len(examples)
+    for proc_indices, proc_completions in all_shards:
+        for orig_idx, comp in zip(  # noqa: B905
+            proc_indices, proc_completions
+        ):
+            ordered[orig_idx] = comp
+    return ordered
+
+
+def _write_split_completions(
+    examples: list[dict[str, Any]],
+    split: str,
+    completions_root: Path,
+    model: Any,
+    tokenizer: Any,
+    args: TrainingArguments,
+) -> Path | None:
+    """
+    Generate and write completions for one split on rank 0.
+
+    :param examples: Prompt examples for the split.
+    :param split: Split name (e.g. ``"validation"``).
+    :param completions_root: Directory to write the JSONL file into.
+    :param model: Unwrapped model.
+    :param tokenizer: Tokenizer.
+    :param args: HF TrainingArguments.
+    :return: Written file path on rank 0; ``None`` on other ranks.
+    """
+    completions = _generate_split(examples, model, tokenizer, args)
+    if args.process_index != 0:
+        return None
+    out_path = completions_root / f"{split}.jsonl"
+    out_path.write_text("\n".join(json.dumps(c) for c in completions))
+    log.info(
+        "EvalCompletionsCallback: wrote %d %s completions -> %s",
+        len(completions),
+        split,
+        out_path,
+    )
+    return out_path
+
+
+# -----------------------------------------------------------------------------
+# Callback
+# -----------------------------------------------------------------------------
+
+
+class EvalCompletionsCallback(TrainerCallback):  # type: ignore[misc]
+    """
+    Generate and log validation and test completions at each epoch end.
+
+    :param trainer: HF Trainer instance, used to resolve the tokenizer
+        and unwrap the model at epoch end.
+    :param dataset: HF dataset repo id to generate completions from.
+        Must be supplied; typically taken from ``cfg.datasets[0].path``.
+    """
+
+    def __init__(self, trainer: Any = None, dataset: str = "") -> None:
+        self._val_examples: list[dict[str, Any]] = []
+        self._test_examples: list[dict[str, Any]] = []
+        self._trainer = trainer
+        self._dataset = dataset
+
+    def on_train_begin(
+        self,
+        _args: TrainingArguments,
+        _state: TrainerState,
+        _control: TrainerControl,
+        **_kwargs: Any,
+    ) -> None:
+        """
+        Load and cache validation and test examples before training.
+
+        :param _args: HF TrainingArguments (unused).
+        :param _state: Current TrainerState (unused).
+        :param _control: TrainerControl object (unused).
+        :param **_kwargs: Keyword arguments passed by the HF Trainer.
+        :return: None
+        """
+        hf_token = os.environ.get("HF_TOKEN") or os.environ.get(
+            "HUGGINGFACE_TOKEN"
+        )
+
+        def _load(split: str) -> list[dict[str, Any]]:
+            ds = load_dataset(self._dataset, split=split, token=hf_token)
+            return [
+                {
+                    "messages": [
+                        m for m in ex["messages"] if m["role"] != "assistant"
+                    ]
+                }
+                for ex in ds
+            ]
+
+        self._val_examples = _load("validation")
+        self._test_examples = _load("test")
+        log.info(
+            "EvalCompletionsCallback: loaded %d validation, %d test examples",
+            len(self._val_examples),
+            len(self._test_examples),
+        )
+
+    def on_evaluate(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        _control: TrainerControl,
+        metrics: dict[str, Any] | None = None,
+        **_kwargs: Any,
+    ) -> None:
+        """
+        Persist eval_loss for this epoch to ``eval_losses.json``.
+
+        :param args: HF TrainingArguments.
+        :param state: Current TrainerState.
+        :param _control: TrainerControl object (unused).
+        :param metrics: Evaluation metrics dict containing ``eval_loss``.
+        :param **_kwargs: Keyword arguments passed by the HF Trainer.
+        :return: None
+        """
+        if (
+            args.process_index != 0
+            or not metrics
+            or "eval_loss" not in metrics
+        ):
+            return
+        epoch = round(state.epoch)
+        losses_path = Path(args.output_dir) / "eval_losses.json"
+        losses: dict[str, float] = (
+            json.loads(losses_path.read_text()) if losses_path.exists() else {}
+        )
+        losses[str(epoch)] = float(metrics["eval_loss"])
+        losses_path.write_text(json.dumps(losses, indent=2))
+        log.info(
+            "EvalCompletionsCallback: epoch %d eval_loss=%.4f",
+            epoch,
+            metrics["eval_loss"],
+        )
+
+    def _resolve_tokenizer(
+        self, tokenizer: Any, kwargs: dict[str, Any]
+    ) -> Any:
+        """
+        Resolve tokenizer from kwargs or trainer fallback.
+
+        :param tokenizer: Tokenizer passed to the callback, or None.
+        :param kwargs: Remaining keyword arguments from the Trainer.
+        :return: Resolved tokenizer, or None if unavailable.
+        """
+        if tokenizer is None:
+            tokenizer = kwargs.get("processing_class")
+        if tokenizer is None and self._trainer is not None:
+            tokenizer = getattr(
+                self._trainer, "processing_class", None
+            ) or getattr(self._trainer, "tokenizer", None)
+        return tokenizer
+
+    def _resolve_model(self, model: Any) -> Any:
+        """
+        Unwrap model from DDP/FSDP or fall back to trainer.
+
+        :param model: Model passed to the callback, or None.
+        :return: Unwrapped model.
+        """
+        if model is None and self._trainer is not None:
+            model = self._trainer.model
+        if self._trainer is not None and hasattr(self._trainer, "accelerator"):
+            model = self._trainer.accelerator.unwrap_model(model)
+        return model
+
+    def _upload_completions_artifact(
+        self, written: list[Path], epoch: int
+    ) -> None:
+        """
+        Upload a W&B artifact containing written completion files.
+
+        :param written: Paths to completion JSONL files.
+        :param epoch: Current epoch number, used in the artifact name.
+        :return: None
+        """
+        if wandb.run is None:
+            return
+        artifact = wandb.Artifact(
+            f"{wandb.run.name}-epoch{epoch}-completions",
+            type="completions",
+        )
+        for p in written:
+            artifact.add_file(str(p))
+        wandb.log_artifact(artifact)
+
+    def on_epoch_end(
+        self,
+        args: TrainingArguments,
+        state: TrainerState,
+        _control: TrainerControl,
+        model: Any = None,
+        tokenizer: Any = None,
+        **kwargs: Any,
+    ) -> None:
+        """
+        Generate completions for all validation and test examples.
+
+        All ranks generate their shard in parallel; rank 0 gathers,
+        writes ordered JSONL, and uploads a W&B artifact.
+
+        :param args: HF TrainingArguments.
+        :param state: TrainerState providing the current epoch.
+        :param _control: TrainerControl object (unused).
+        :param model: Live model (DDP/FSDP wrapped; unwrapped internally).
+        :param tokenizer: Tokenizer, or None in newer transformers.
+        :param **kwargs: Keyword arguments passed by the HF Trainer.
+        :return: None
+        """
+        epoch = round(state.epoch)
+        log.info(
+            "EvalCompletionsCallback: generating completions for epoch %d",
+            epoch,
+        )
+
+        tokenizer = self._resolve_tokenizer(tokenizer, kwargs)
+        if tokenizer is None:
+            log.error(
+                "EvalCompletionsCallback: tokenizer is None, "
+                "skipping epoch %d",
+                epoch,
+            )
+            return
+
+        model = self._resolve_model(model)
+        completions_root = (
+            Path(args.output_dir).parent / "completions" / f"epoch_{epoch}"
+        )
+        completions_root.mkdir(parents=True, exist_ok=True)
+
+        orig_padding_side = tokenizer.padding_side
+        tokenizer.padding_side = "left"
+        model.eval()
+
+        written: list[Path] = []
+        for split, examples in (
+            ("validation", self._val_examples),
+            ("test", self._test_examples),
+        ):
+            path = _write_split_completions(
+                examples, split, completions_root, model, tokenizer, args
+            )
+            if path is not None:
+                written.append(path)
+
+        model.train()
+        tokenizer.padding_side = orig_padding_side
+
+        if args.process_index == 0:
+            self._upload_completions_artifact(written, epoch)
+
+
+# -----------------------------------------------------------------------------
+# Plugin
+# -----------------------------------------------------------------------------
+
+
+class EvalCompletionsPlugin(BasePlugin):  # type: ignore[misc]
+    """
+    Axolotl plugin that registers EvalCompletionsCallback before training.
+
+    Register via the axolotl YAML config::
+
+        plugins:
+          - voice.finetune.callbacks.EvalCompletionsPlugin
+    """
+
+    def add_callbacks_post_trainer(
+        self, cfg: Any, trainer: Any
+    ) -> list[TrainerCallback]:
+        """
+        Return the completions callback to be added to the Trainer.
+
+        :param cfg: Axolotl config object.
+        :param trainer: The built HF Trainer instance.
+        :return: List containing the EvalCompletionsCallback instance.
+        :raises ValueError: If ``cfg.datasets[0].path`` is not accessible.
+        """
+        try:
+            dataset: str = cfg.datasets[0].path
+        except (AttributeError, IndexError) as exc:
+            raise ValueError(
+                "EvalCompletionsPlugin requires cfg.datasets[0].path "
+                "to be set in the axolotl config."
+            ) from exc
+        return [EvalCompletionsCallback(trainer=trainer, dataset=dataset)]
diff --git a/tests/voice/finetune/test_callbacks.py b/tests/voice/finetune/test_callbacks.py
new file mode 100644
index 0000000..b99ae24
--- /dev/null
+++ b/tests/voice/finetune/test_callbacks.py
@@ -0,0 +1,97 @@
+"""
+Tests for voice.finetune.callbacks.
+
+Scope:
+- _format_completion produces correct chat_template JSONL structure
+- GENERATION_CONFIG contains required keys derived from INFERENCE_DEFAULTS
+- Completions directory path derivation from args.output_dir
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+
+from voice._defaults import INFERENCE_DEFAULTS
+from voice.finetune.callbacks import GENERATION_CONFIG, _format_completion
+
+# -----------------------------------------------------------------------------
+# _format_completion
+# -----------------------------------------------------------------------------
+
+
+def test_format_completion_appends_assistant_turn():
+    messages = [
+        {"role": "system", "content": "sys"},
+        {"role": "user", "content": "q"},
+    ]
+    result = _format_completion(messages, "my response")
+    assert result["messages"][-1] == {
+        "role": "assistant",
+        "content": "my response",
+    }
+
+
+def test_format_completion_preserves_prior_messages():
+    messages = [
+        {"role": "system", "content": "sys"},
+        {"role": "user", "content": "q"},
+    ]
+    result = _format_completion(messages, "r")
+    assert result["messages"][:-1] == messages
+
+
+def test_format_completion_has_messages_key():
+    result = _format_completion([], "hi")
+    assert "messages" in result
+    assert isinstance(result["messages"], list)
+
+
+def test_format_completion_no_extra_keys():
+    result = _format_completion([], "hi")
+    assert set(result.keys()) == {"messages"}
+
+
+def test_format_completion_empty_messages_still_has_assistant():
+    result = _format_completion([], "response")
+    assert result["messages"] == [{"role": "assistant", "content": "response"}]
+
+
+# -----------------------------------------------------------------------------
+# GENERATION_CONFIG
+# -----------------------------------------------------------------------------
+
+
+def test_generation_config_has_required_keys():
+    required = {"do_sample", "temperature", "top_p", "top_k", "max_new_tokens"}
+    assert required.issubset(GENERATION_CONFIG.keys())
+
+
+def test_generation_config_temperature_matches_defaults():
+    assert GENERATION_CONFIG["temperature"] == INFERENCE_DEFAULTS.temperature
+
+
+def test_generation_config_max_new_tokens_matches_defaults():
+    assert (
+        GENERATION_CONFIG["max_new_tokens"]
+        == INFERENCE_DEFAULTS.max_new_tokens
+    )
+
+
+# -----------------------------------------------------------------------------
+# Completions path derivation
+# -----------------------------------------------------------------------------
+
+
+def test_completions_root_derived_from_output_dir():
+    output_dir = Path("/runs/my-run/adapter")
+    epoch = 2
+    root = output_dir.parent / "completions" / f"epoch_{epoch}"
+    assert root == Path("/runs/my-run/completions/epoch_2")
+
+
+def test_completions_jsonl_path_per_split():
+    output_dir = Path("/runs/my-run/adapter")
+    epoch = 1
+    root = output_dir.parent / "completions" / f"epoch_{epoch}"
+    assert (root / "validation.jsonl").name == "validation.jsonl"
+    assert (root / "test.jsonl").name == "test.jsonl"

From 609851d9a37151d1bbaa17e700d04e336b83ccc5 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 14:47:28 +0100
Subject: [PATCH 57/68] feat(fine-tuning): add orchestration module

---
 src/voice/finetune/_orchestrator.py | 335 ++++++++++++++++++++++++++++
 1 file changed, 335 insertions(+)
 create mode 100644 src/voice/finetune/_orchestrator.py

diff --git a/src/voice/finetune/_orchestrator.py b/src/voice/finetune/_orchestrator.py
new file mode 100644
index 0000000..0c9a5f6
--- /dev/null
+++ b/src/voice/finetune/_orchestrator.py
@@ -0,0 +1,335 @@
+"""
+Run orchestration for single fine-tuning runs.
+
+This module is private by convention and not part of the public API.
+
+Ties together run directory setup, axolotl training launch, completion
+scoring, metadata persistence and W&B summary logging.
+
+Entry point is ``execute_run``, which performs the full lifecycle:
+
+1. Build run axolotl config from base config and hyperparams.
+2. Create run directory and write config.
+3. Initialise and persist ``RunMetadata`` (status ``running``).
+4. Load reference dataset for post-training scoring.
+5. Launch axolotl training; on failure set status ``failed`` and raise.
+6. Score completions for each epoch and split.
+7. Persist final metadata (status ``done``) and log alignment to W&B.
+"""
+
+from __future__ import annotations
+
+import datetime
+import json
+import logging
+import subprocess
+from typing import Any
+
+import wandb
+
+from voice.comparison.comparison import make_comparison
+from voice.datasets._schema import Split
+from voice.datasets.dataset import (
+    DatasetSpec,
+    Example,
+    LocalDatasetSpec,
+    VoiceDataset,
+)
+from voice.datasets.get_dataset import get_dataset
+from voice.finetune._runs import RunDir
+from voice.finetune._schema import RunMetadata
+
+log = logging.getLogger(__name__)
+
+# Axolotl config keys whose values come from the run hyperparams dict.
+_HYPERPARAMS_CFG_KEYS: tuple[str, ...] = (
+    "learning_rate",
+    "lora_r",
+    "lora_alpha",
+    "micro_batch_size",
+    "gradient_accumulation_steps",
+    "lora_target_modules",
+)
+
+# Splits scored after each training epoch.
+_EVAL_SPLITS: tuple[str, str] = ("validation", "test")
+
+
+# -----------------------------------------------------------------------------
+# Config helpers
+# -----------------------------------------------------------------------------
+
+
+def _build_axolotl_cfg(
+    base_cfg: dict[str, Any],
+    hyperparams: dict[str, Any],
+    run_dir: RunDir,
+) -> dict[str, Any]:
+    """
+    Build a per-run axolotl config by merging hyperparams into the base.
+
+    Sets ``output_dir`` to ``run_dir.adapter_dir`` and copies any
+    recognised hyperparams keys present in ``hyperparams``.
+
+    :param base_cfg: Shared axolotl config from the sweep or single-run
+        config.
+    :param hyperparams: Concrete hyperparameter values for this run.
+    :param run_dir: Run directory; determines ``output_dir``.
+    :return: Merged per-run axolotl config dict.
+    """
+    cfg = dict(base_cfg)
+    cfg["output_dir"] = str(run_dir.adapter_dir)
+    for key in _HYPERPARAMS_CFG_KEYS:
+        if key in hyperparams:
+            cfg[key] = hyperparams[key]
+    return cfg
+
+
+def _now_iso() -> str:
+    """
+    Return the current UTC time as an ISO-8601 string.
+
+    :return: ISO-formatted UTC timestamp.
+    """
+    return datetime.datetime.now(datetime.UTC).isoformat()
+
+
+# -----------------------------------------------------------------------------
+# Training
+# -----------------------------------------------------------------------------
+
+
+def run_training(run_dir: RunDir) -> None:
+    """
+    Launch axolotl training via ``accelerate`` and wait for completion.
+
+    Reads the frozen config from ``run_dir.config_path``.
+
+    :param run_dir: Run directory containing the written axolotl config.
+    :return: None
+    """
+    cmd = [
+        "accelerate",
+        "launch",
+        "-m",
+        "axolotl.cli.train",
+        str(run_dir.config_path),
+    ]
+    log.info("Launching training: %s", " ".join(cmd))
+    subprocess.run(cmd, check=True)
+
+
+# -----------------------------------------------------------------------------
+# Scoring
+# -----------------------------------------------------------------------------
+
+
+def _load_completions(
+    run_dir: RunDir,
+    split: str,
+    epoch: int,
+) -> list[Example]:
+    """
+    Load per-epoch completions as a list of Examples.
+
+    Completions are stored in chat-template JSONL format and are
+    transparently canonicalised to ``(system, question, answer)`` by
+    ``get_dataset``.
+
+    :param run_dir: Run directory containing completions.
+    :param split: Split name (e.g. ``"validation"`` or ``"test"``).
+    :param epoch: Epoch number (1-indexed).
+    :return: List of Example objects loaded from the completions JSONL.
+    """
+    epoch_dir = run_dir.completions_dir / f"epoch_{epoch}"
+    spec = LocalDatasetSpec(path=epoch_dir, splits=(Split.parse(split),))
+    ds = get_dataset(spec)
+    return list(ds[split])
+
+
+def _score_split(
+    run_dir: RunDir,
+    split: str,
+    epoch: int,
+    reference_ds: VoiceDataset,
+) -> float:
+    """
+    Score completions for one split/epoch and persist results locally.
+
+    Alignment results are written to
+    ``alignment/epoch_{n}/{split}.json`` with keys ``score``,
+    ``metric_tails``, and ``group_tails``. Only the overall score is
+    returned; the full tails are available on disk only.
+
+    :param run_dir: Run directory.
+    :param split: Split name (``"validation"`` or ``"test"``).
+    :param epoch: Epoch number (1-indexed).
+    :param reference_ds: Reference VoiceDataset to compare against.
+        Must contain the split being scored and the ``train`` split
+        for self-Wasserstein calibration.
+    :return: Alignment score in [0, 1].
+    """
+    completions = _load_completions(run_dir, split, epoch)
+    results = make_comparison(completions, reference_ds)
+
+    out_path = run_dir.alignment_path(split, epoch)
+    out_path.parent.mkdir(parents=True, exist_ok=True)
+    out_path.write_text(
+        json.dumps(
+            {
+                "score": results.score,
+                "metric_tails": results.metric_tails,
+                "group_tails": {
+                    g.value: v for g, v in results.group_tails.items()
+                },
+            },
+            indent=2,
+        )
+    )
+    log.info("Scored %s epoch %d: alignment=%.4f", split, epoch, results.score)
+    return results.score
+
+
+def _try_score_split(
+    run_dir: RunDir,
+    split: str,
+    epoch: int,
+    reference_ds: VoiceDataset,
+    meta: RunMetadata,
+) -> None:
+    """
+    Attempt to score one split/epoch; update ``meta`` in place on success.
+
+    Skips silently if the completions file does not exist.
+    Logs and skips if scoring raises an exception.
+
+    :param run_dir: Run directory.
+    :param split: Split name (``"validation"`` or ``"test"``).
+    :param epoch: Epoch number (1-indexed).
+    :param reference_ds: Reference VoiceDataset for comparison.
+    :param meta: RunMetadata to update with the alignment score.
+    :return: None
+    """
+    if not run_dir.completions_path(split, epoch).exists():
+        log.warning(
+            "Completions missing: %s epoch %d (skipping)", split, epoch
+        )
+        return
+    try:
+        score = _score_split(run_dir, split, epoch, reference_ds)
+    except Exception:
+        log.exception("Scoring failed for %s epoch %d", split, epoch)
+        return
+    if split == "validation":
+        meta.alignment_val[str(epoch)] = score
+    else:
+        meta.alignment_test[str(epoch)] = score
+
+
+def _score_all_epochs(
+    run_dir: RunDir,
+    num_epochs: int,
+    reference_ds: VoiceDataset,
+    meta: RunMetadata,
+) -> None:
+    """
+    Score completions across all epochs and splits; update ``meta`` in place.
+
+    :param run_dir: Run directory.
+    :param num_epochs: Total number of training epochs.
+    :param reference_ds: Reference VoiceDataset for comparison.
+    :param meta: RunMetadata to update with per-epoch alignment scores.
+    :return: None
+    """
+    for epoch in range(1, num_epochs + 1):
+        for split in _EVAL_SPLITS:
+            _try_score_split(run_dir, split, epoch, reference_ds, meta)
+
+
+# -----------------------------------------------------------------------------
+# W&B logging
+# -----------------------------------------------------------------------------
+
+
+def _log_to_wandb(meta: RunMetadata) -> None:
+    """
+    Write alignment summary dicts to the active W&B run.
+
+    Logs ``alignment_val`` and ``alignment_test`` (full epoch-keyed dicts)
+    to ``wandb.run.summary``. No-ops if no W&B run is currently active.
+
+    :param meta: RunMetadata containing alignment score histories.
+    :return: None
+    """
+    if wandb.run is None:
+        return
+    wandb.run.summary["alignment_val"] = meta.alignment_val
+    wandb.run.summary["alignment_test"] = meta.alignment_test
+
+
+# -----------------------------------------------------------------------------
+# Entry point
+# -----------------------------------------------------------------------------
+
+
+def execute_run(
+    run_name: str,
+    base_axolotl_cfg: dict[str, Any],
+    hyperparams: dict[str, Any],
+    run_dir: RunDir,
+    reference_spec: DatasetSpec | LocalDatasetSpec,
+) -> RunMetadata:
+    """
+    Execute a single fine-tuning run end-to-end.
+
+    :param run_name: Unique run identifier (e.g. ``llama-3_a3f2b9c1``).
+    :param base_axolotl_cfg: Shared axolotl settings from the config file.
+    :param hyperparams: Concrete hyperparameter values for this run.
+    :param run_dir: RunDir instance for this run (not yet created on disk).
+    :param reference_spec: Dataset spec used as the reference corpus for
+        scoring. Must include ``train``, ``validation``, and ``test`` splits.
+    :return: Completed RunMetadata with alignment scores populated.
+    :raises RuntimeError: If axolotl training exits with a non-zero status.
+    """
+    axolotl_cfg = _build_axolotl_cfg(base_axolotl_cfg, hyperparams, run_dir)
+    num_epochs = int(axolotl_cfg.get("num_epochs", 1))
+
+    run_dir.create()
+    run_dir.write_config(axolotl_cfg)
+
+    datasets_list: list[dict[str, Any]] = axolotl_cfg.get("datasets") or [{}]
+    meta = RunMetadata(
+        run_name=run_name,
+        model=str(axolotl_cfg.get("base_model", "")),
+        dataset=str(datasets_list[0].get("path", "")),
+        hyperparams=hyperparams,
+        status="running",
+        started_at=_now_iso(),
+    )
+    run_dir.write_metadata(meta)
+
+    log.info("Loading reference dataset for run '%s'...", run_name)
+    reference_ds = get_dataset(reference_spec)
+
+    try:
+        run_training(run_dir)
+    except subprocess.CalledProcessError as exc:
+        meta.status = "failed"
+        meta.finished_at = _now_iso()
+        run_dir.write_metadata(meta)
+        raise RuntimeError(f"Training failed for run '{run_name}'") from exc
+
+    _score_all_epochs(run_dir, num_epochs, reference_ds, meta)
+
+    meta.status = "done"
+    meta.finished_at = _now_iso()
+    run_dir.write_metadata(meta)
+    _log_to_wandb(meta)
+
+    log.info(
+        "Run '%s' complete. alignment_val=%s alignment_test=%s",
+        run_name,
+        meta.alignment_val,
+        meta.alignment_test,
+    )
+    return meta

From 0bc728e911296fc96378efdff5cc0c0ce2e2ee39 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 15:03:45 +0100
Subject: [PATCH 58/68] feat(fine-tuning): add cli

---
 pyproject.toml                   |  10 ++
 src/voice/finetune/cli.py        | 162 +++++++++++++++++++
 tests/voice/finetune/test_cli.py | 264 +++++++++++++++++++++++++++++++
 3 files changed, 436 insertions(+)
 create mode 100644 src/voice/finetune/cli.py
 create mode 100644 tests/voice/finetune/test_cli.py

diff --git a/pyproject.toml b/pyproject.toml
index b48f023..2ec89c9 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -23,6 +23,9 @@ dependencies = [
     "bitsandbytes>=0.49.1 ; sys_platform == 'linux'",
 ]
 
+[project.scripts]
+voice = "voice.finetune.cli:main"
+
 [tool.uv]
 required-environments = ["sys_platform == 'linux'"]
 
@@ -109,6 +112,10 @@ no_implicit_optional = true
 module = ["tests.*"]
 ignore_errors = true
 
+[[tool.mypy.overrides]]
+module = ["voice.finetune.cli"]
+ignore_errors = true
+
 [[tool.mypy.overrides]]
 module = [
     "datasets",
@@ -139,6 +146,8 @@ module = [
     "wandb.*",
     "axolotl",
     "axolotl.*",
+    "click",
+    "click.*",
 ]
 ignore_missing_imports = true
 
@@ -153,3 +162,4 @@ skip-checking-short-docstrings = false
 skip-checking-raises = false
 require-return-section-when-returning-nothing = true
 check-return-types = false
+exclude = '\.git|\.tox|finetune/cli\.py'
diff --git a/src/voice/finetune/cli.py b/src/voice/finetune/cli.py
new file mode 100644
index 0000000..2db31a2
--- /dev/null
+++ b/src/voice/finetune/cli.py
@@ -0,0 +1,162 @@
+"""
+Click CLI for VOICE fine-tuning.
+
+Provides two subcommands under ``voice finetune``:
+
+- ``single`` - run one fine-tuning job from a single-run YAML config.
+- ``sweep`` - run a hyperparameter grid search from a sweep YAML config.
+
+Usage::
+
+    voice finetune single configs/single/my_run.yaml
+    voice finetune sweep  configs/sweep/pilot.yaml --resume
+"""
+
+from __future__ import annotations
+
+import logging
+import sys
+from pathlib import Path
+
+import click
+
+from voice.datasets._schema import Split
+from voice.datasets.dataset import DatasetSpec
+from voice.finetune._grid import expand_grid, generate_run_name, new_uid
+from voice.finetune._orchestrator import execute_run
+from voice.finetune._runs import RunDir
+from voice.finetune._schema import SingleRunConfig, SweepConfig
+from voice.finetune._state import load_state, mark_completed, mark_failed
+
+log = logging.getLogger(__name__)
+
+# All three splits are required: train for calibration, val/test for scoring.
+_ALL_SPLITS: tuple[Split, ...] = (Split.TRAIN, Split.VALIDATION, Split.TEST)
+
+
+# -----------------------------------------------------------------------------
+# CLI groups
+# -----------------------------------------------------------------------------
+
+
+@click.group()
+def main() -> None:
+    """VOICE – fine-tuning for stylistic fidelity."""
+
+
+@main.group()
+def finetune() -> None:
+    """Fine-tuning commands."""
+
+
+# -----------------------------------------------------------------------------
+# `voice finetune single`
+# -----------------------------------------------------------------------------
+
+
+@finetune.command("single")
+@click.argument("config", type=click.Path(exists=True, path_type=Path))
+@click.option(
+    "--runs-dir",
+    default="runs",
+    show_default=True,
+    type=click.Path(path_type=Path),
+    help="Root directory for run outputs.",
+)
+@click.option(
+    "--dataset-revision",
+    default="main",
+    show_default=True,
+    help="HF revision of the reference dataset.",
+)
+def single(
+    config: Path,
+    runs_dir: Path,
+    dataset_revision: str,
+) -> None:
+    """Run a single fine-tuning job from CONFIG."""
+    cfg = SingleRunConfig.from_yaml(config)
+    dataset: str = cfg.axolotl["datasets"][0]["path"]
+    model: str = str(cfg.axolotl.get("base_model", ""))
+    run_name = generate_run_name(model, new_uid())
+    run_dir = RunDir(path=runs_dir / run_name)
+    reference_spec = DatasetSpec(
+        repo_id=dataset,
+        revision=dataset_revision,
+        splits=_ALL_SPLITS,
+    )
+    click.echo(f"Starting run: {run_name}")
+    try:
+        execute_run(run_name, cfg.axolotl, {}, run_dir, reference_spec)
+    except RuntimeError as exc:
+        click.echo(f"Run failed: {exc}", err=True)
+        sys.exit(1)
+    click.echo(f"Run complete: {run_name}")
+
+
+# -----------------------------------------------------------------------------
+# `voice finetune sweep`
+# -----------------------------------------------------------------------------
+
+
+@finetune.command("sweep")
+@click.argument("config", type=click.Path(exists=True, path_type=Path))
+@click.option(
+    "--runs-dir",
+    default="runs",
+    show_default=True,
+    type=click.Path(path_type=Path),
+    help="Root directory for run outputs.",
+)
+@click.option(
+    "--resume/--no-resume",
+    default=False,
+    help="Skip runs already marked completed in state.json.",
+)
+@click.option(
+    "--dataset-revision",
+    default="main",
+    show_default=True,
+    help="HF revision of the reference dataset.",
+)
+def sweep(
+    config: Path,
+    runs_dir: Path,
+    resume: bool,
+    dataset_revision: str,
+) -> None:
+    """Run a hyperparameter sweep from CONFIG."""
+    cfg = SweepConfig.from_yaml(config)
+    dataset: str = cfg.axolotl["datasets"][0]["path"]
+    model: str = str(cfg.axolotl.get("base_model", ""))
+    grid = expand_grid(cfg.sweep)
+    runs_dir.mkdir(parents=True, exist_ok=True)
+    state_file = runs_dir / "state.json"
+    state = load_state(state_file)
+    reference_spec = DatasetSpec(
+        repo_id=dataset,
+        revision=dataset_revision,
+        splits=_ALL_SPLITS,
+    )
+    failed_count = 0
+    for hyperparams in grid:
+        run_idx = str(hyperparams["run_idx"])
+        if resume and run_idx in state["completed"]:
+            click.echo(f"Skipping run {run_idx} (already completed)")
+            continue
+        run_name = generate_run_name(model, new_uid())
+        run_dir = RunDir(path=runs_dir / run_name)
+        click.echo(f"Starting run {run_idx}/{len(grid) - 1}: {run_name}")
+        try:
+            execute_run(
+                run_name, cfg.axolotl, hyperparams, run_dir, reference_spec
+            )
+            mark_completed(state, run_idx, state_file)
+            click.echo(f"Run {run_idx} complete: {run_name}")
+        except RuntimeError as exc:
+            mark_failed(state, run_idx, str(exc), state_file)
+            click.echo(f"Run {run_idx} failed: {exc}", err=True)
+            failed_count += 1
+    if failed_count:
+        click.echo(f"{failed_count} run(s) failed.", err=True)
+        sys.exit(1)
diff --git a/tests/voice/finetune/test_cli.py b/tests/voice/finetune/test_cli.py
new file mode 100644
index 0000000..bfa8ac3
--- /dev/null
+++ b/tests/voice/finetune/test_cli.py
@@ -0,0 +1,264 @@
+"""
+Tests for voice.finetune.cli.
+
+Scope:
+- ``voice finetune single`` invokes execute_run with correct arguments.
+- ``voice finetune single`` exits non-zero on training failure.
+- ``voice finetune sweep`` invokes execute_run once per grid point.
+- ``voice finetune sweep --resume`` skips already-completed runs.
+- ``voice finetune sweep`` exits non-zero if any run fails.
+"""
+
+from __future__ import annotations
+
+from pathlib import Path
+from unittest.mock import patch
+
+import yaml
+from click.testing import CliRunner
+
+from voice.finetune.cli import main
+
+# ---------------------------------------------------------------------------
+# Config helpers
+# ---------------------------------------------------------------------------
+
+
+def _single_cfg(tmp_path: Path) -> Path:
+    cfg = {
+        "axolotl": {
+            "base_model": "namespace/model",
+            "datasets": [{"path": "namespace/dataset"}],
+            "num_epochs": 2,
+        }
+    }
+    p = tmp_path / "single.yaml"
+    p.write_text(yaml.dump(cfg))
+    return p
+
+
+def _sweep_cfg(tmp_path: Path) -> Path:
+    cfg = {
+        "sweep": {
+            "learning_rate": [1e-4],
+            "lora_r": [8],
+            "micro_batch_size": [4],
+            "gradient_accumulation_steps": [4],
+            "target_layers": [
+                {"name": "attention", "modules": ["q_proj", "v_proj"]}
+            ],
+        },
+        "axolotl": {
+            "base_model": "namespace/model",
+            "datasets": [{"path": "namespace/dataset"}],
+            "num_epochs": 1,
+        },
+    }
+    p = tmp_path / "sweep.yaml"
+    p.write_text(yaml.dump(cfg))
+    return p
+
+
+# ---------------------------------------------------------------------------
+# `voice finetune single`
+# ---------------------------------------------------------------------------
+
+
+def test_single_invokes_execute_run(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "single",
+                str(_single_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    assert result.exit_code == 0, result.output
+    assert mock_run.call_count == 1
+
+
+def test_single_passes_dataset_as_reference_repo(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        runner.invoke(
+            main,
+            [
+                "finetune",
+                "single",
+                str(_single_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    ref_spec = mock_run.call_args.args[4]
+    assert ref_spec.repo_id == "namespace/dataset"
+
+
+def test_single_default_dataset_revision_is_main(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        runner.invoke(
+            main,
+            [
+                "finetune",
+                "single",
+                str(_single_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    ref_spec = mock_run.call_args.args[4]
+    assert ref_spec.revision == "main"
+
+
+def test_single_respects_dataset_revision_option(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        runner.invoke(
+            main,
+            [
+                "finetune",
+                "single",
+                str(_single_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+                "--dataset-revision",
+                "abc123",
+            ],
+        )
+    ref_spec = mock_run.call_args.args[4]
+    assert ref_spec.revision == "abc123"
+
+
+def test_single_exits_nonzero_on_training_failure(tmp_path):
+    runner = CliRunner()
+    with patch(
+        "voice.finetune.cli.execute_run", side_effect=RuntimeError("boom")
+    ):
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "single",
+                str(_single_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    assert result.exit_code == 1
+
+
+def test_single_missing_config_exits_nonzero():
+    runner = CliRunner()
+    result = runner.invoke(main, ["finetune", "single", "missing.yaml"])
+    assert result.exit_code != 0
+
+
+# ---------------------------------------------------------------------------
+# `voice finetune sweep`
+# ---------------------------------------------------------------------------
+
+
+def test_sweep_calls_execute_run_per_grid_point(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "sweep",
+                str(_sweep_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    assert result.exit_code == 0, result.output
+    # One grid point in _sweep_cfg
+    assert mock_run.call_count == 1
+
+
+def test_sweep_resume_skips_completed_run(tmp_path):
+    runs_dir = tmp_path / "runs"
+    runs_dir.mkdir()
+    (runs_dir / "state.json").write_text('{"completed": ["0"], "failed": {}}')
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "sweep",
+                str(_sweep_cfg(tmp_path)),
+                "--runs-dir",
+                str(runs_dir),
+                "--resume",
+            ],
+        )
+    assert result.exit_code == 0, result.output
+    assert mock_run.call_count == 0
+
+
+def test_sweep_no_resume_reruns_completed(tmp_path):
+    runs_dir = tmp_path / "runs"
+    runs_dir.mkdir()
+    (runs_dir / "state.json").write_text('{"completed": ["0"], "failed": {}}')
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "sweep",
+                str(_sweep_cfg(tmp_path)),
+                "--runs-dir",
+                str(runs_dir),
+            ],
+        )
+    assert result.exit_code == 0, result.output
+    assert mock_run.call_count == 1
+
+
+def test_sweep_exits_nonzero_on_any_failure(tmp_path):
+    runner = CliRunner()
+    with patch(
+        "voice.finetune.cli.execute_run", side_effect=RuntimeError("fail")
+    ):
+        result = runner.invoke(
+            main,
+            [
+                "finetune",
+                "sweep",
+                str(_sweep_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    assert result.exit_code == 1
+
+
+def test_sweep_missing_config_exits_nonzero():
+    runner = CliRunner()
+    result = runner.invoke(main, ["finetune", "sweep", "missing.yaml"])
+    assert result.exit_code != 0
+
+
+def test_sweep_passes_hyperparams_to_execute_run(tmp_path):
+    runner = CliRunner()
+    with patch("voice.finetune.cli.execute_run") as mock_run:
+        runner.invoke(
+            main,
+            [
+                "finetune",
+                "sweep",
+                str(_sweep_cfg(tmp_path)),
+                "--runs-dir",
+                str(tmp_path / "runs"),
+            ],
+        )
+    hyperparams = mock_run.call_args.args[2]
+    assert hyperparams["learning_rate"] == 1e-4
+    assert hyperparams["lora_r"] == 8

From eca7f27ef214d2c0603c25808416c7b28c7099a0 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 15:33:37 +0100
Subject: [PATCH 59/68] chore: remove wandb post-hoc artefact upload

---
 src/voice/finetune/_orchestrator.py | 28 ++--------------------
 src/voice/finetune/callbacks.py     | 36 ++++-------------------------
 2 files changed, 6 insertions(+), 58 deletions(-)

diff --git a/src/voice/finetune/_orchestrator.py b/src/voice/finetune/_orchestrator.py
index 0c9a5f6..59d2365 100644
--- a/src/voice/finetune/_orchestrator.py
+++ b/src/voice/finetune/_orchestrator.py
@@ -4,7 +4,7 @@
 This module is private by convention and not part of the public API.
 
 Ties together run directory setup, axolotl training launch, completion
-scoring, metadata persistence and W&B summary logging.
+scoring, and metadata persistence.
 
 Entry point is ``execute_run``, which performs the full lifecycle:
 
@@ -14,7 +14,7 @@
 4. Load reference dataset for post-training scoring.
 5. Launch axolotl training; on failure set status ``failed`` and raise.
 6. Score completions for each epoch and split.
-7. Persist final metadata (status ``done``) and log alignment to W&B.
+7. Persist final metadata (status ``done``).
 """
 
 from __future__ import annotations
@@ -25,8 +25,6 @@
 import subprocess
 from typing import Any
 
-import wandb
-
 from voice.comparison.comparison import make_comparison
 from voice.datasets._schema import Split
 from voice.datasets.dataset import (
@@ -246,27 +244,6 @@ def _score_all_epochs(
             _try_score_split(run_dir, split, epoch, reference_ds, meta)
 
 
-# -----------------------------------------------------------------------------
-# W&B logging
-# -----------------------------------------------------------------------------
-
-
-def _log_to_wandb(meta: RunMetadata) -> None:
-    """
-    Write alignment summary dicts to the active W&B run.
-
-    Logs ``alignment_val`` and ``alignment_test`` (full epoch-keyed dicts)
-    to ``wandb.run.summary``. No-ops if no W&B run is currently active.
-
-    :param meta: RunMetadata containing alignment score histories.
-    :return: None
-    """
-    if wandb.run is None:
-        return
-    wandb.run.summary["alignment_val"] = meta.alignment_val
-    wandb.run.summary["alignment_test"] = meta.alignment_test
-
-
 # -----------------------------------------------------------------------------
 # Entry point
 # -----------------------------------------------------------------------------
@@ -324,7 +301,6 @@ def execute_run(
     meta.status = "done"
     meta.finished_at = _now_iso()
     run_dir.write_metadata(meta)
-    _log_to_wandb(meta)
 
     log.info(
         "Run '%s' complete. alignment_val=%s alignment_test=%s",
diff --git a/src/voice/finetune/callbacks.py b/src/voice/finetune/callbacks.py
index a1ca1f2..a7efc93 100644
--- a/src/voice/finetune/callbacks.py
+++ b/src/voice/finetune/callbacks.py
@@ -7,8 +7,7 @@
 All GPUs participate in generation: each split's examples are sharded
 across processes. Each shard is generated in batches of
 ``INFERENCE_DEFAULTS.batch_size``. After all ranks finish, results are
-gathered and rank 0 writes the final ordered JSONL and uploads a W&B
-artifact.
+gathered and rank 0 writes the final ordered JSONL.
 
 Completions are written under the run directory derived from
 ``args.output_dir`` (the adapter dir):
@@ -26,7 +25,6 @@
 
 import torch
 import torch.distributed
-import wandb
 from datasets import load_dataset
 from transformers import (
     TrainerCallback,
@@ -333,26 +331,6 @@ def _resolve_model(self, model: Any) -> Any:
             model = self._trainer.accelerator.unwrap_model(model)
         return model
 
-    def _upload_completions_artifact(
-        self, written: list[Path], epoch: int
-    ) -> None:
-        """
-        Upload a W&B artifact containing written completion files.
-
-        :param written: Paths to completion JSONL files.
-        :param epoch: Current epoch number, used in the artifact name.
-        :return: None
-        """
-        if wandb.run is None:
-            return
-        artifact = wandb.Artifact(
-            f"{wandb.run.name}-epoch{epoch}-completions",
-            type="completions",
-        )
-        for p in written:
-            artifact.add_file(str(p))
-        wandb.log_artifact(artifact)
-
     def on_epoch_end(
         self,
         args: TrainingArguments,
@@ -365,8 +343,8 @@ def on_epoch_end(
         """
         Generate completions for all validation and test examples.
 
-        All ranks generate their shard in parallel; rank 0 gathers,
-        writes ordered JSONL, and uploads a W&B artifact.
+        All ranks generate their shard in parallel; rank 0 gathers
+        and writes ordered JSONL.
 
         :param args: HF TrainingArguments.
         :param state: TrainerState providing the current epoch.
@@ -401,23 +379,17 @@ def on_epoch_end(
         tokenizer.padding_side = "left"
         model.eval()
 
-        written: list[Path] = []
         for split, examples in (
             ("validation", self._val_examples),
             ("test", self._test_examples),
         ):
-            path = _write_split_completions(
+            _write_split_completions(
                 examples, split, completions_root, model, tokenizer, args
             )
-            if path is not None:
-                written.append(path)
 
         model.train()
         tokenizer.padding_side = orig_padding_side
 
-        if args.process_index == 0:
-            self._upload_completions_artifact(written, epoch)
-
 
 # -----------------------------------------------------------------------------
 # Plugin

From 2d384b1c3c7e1fc25638692c2cddd044f2fa756f Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 15:43:11 +0100
Subject: [PATCH 60/68] chore: remove unicode escaping in axolotl custom
 callback

---
 src/voice/finetune/callbacks.py | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/src/voice/finetune/callbacks.py b/src/voice/finetune/callbacks.py
index a7efc93..603e27b 100644
--- a/src/voice/finetune/callbacks.py
+++ b/src/voice/finetune/callbacks.py
@@ -193,7 +193,9 @@ def _write_split_completions(
     if args.process_index != 0:
         return None
     out_path = completions_root / f"{split}.jsonl"
-    out_path.write_text("\n".join(json.dumps(c) for c in completions))
+    out_path.write_text(
+        "\n".join(json.dumps(c, ensure_ascii=False) for c in completions)
+    )
     log.info(
         "EvalCompletionsCallback: wrote %d %s completions -> %s",
         len(completions),

From dd2ac292b3bed57956be24804f9cf24981e70c37 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Fri, 22 May 2026 16:18:21 +0100
Subject: [PATCH 61/68] fix: fix PYTHONPATH handling in axolotl subprocess

---
 src/voice/finetune/_orchestrator.py | 48 +++++++++++++++++++++--------
 src/voice/finetune/cli.py           | 25 +++++++++++++--
 2 files changed, 59 insertions(+), 14 deletions(-)

diff --git a/src/voice/finetune/_orchestrator.py b/src/voice/finetune/_orchestrator.py
index 59d2365..f06727c 100644
--- a/src/voice/finetune/_orchestrator.py
+++ b/src/voice/finetune/_orchestrator.py
@@ -22,7 +22,9 @@
 import datetime
 import json
 import logging
+import os
 import subprocess
+import sys
 from typing import Any
 
 from voice.comparison.comparison import make_comparison
@@ -97,24 +99,43 @@ def _now_iso() -> str:
 # -----------------------------------------------------------------------------
 
 
-def run_training(run_dir: RunDir) -> None:
+def run_training(run_dir: RunDir, gpu_ids: str = "0") -> None:
     """
-    Launch axolotl training via ``accelerate`` and wait for completion.
+    Launch axolotl training and wait for completion.
 
-    Reads the frozen config from ``run_dir.config_path``.
+    Sets ``CUDA_VISIBLE_DEVICES`` to ``gpu_ids``. For a single GPU uses
+    ``python -m axolotl.cli.train`` directly; for multiple GPUs uses
+    ``accelerate launch`` with DDP.
 
     :param run_dir: Run directory containing the written axolotl config.
+    :param gpu_ids: Comma-separated GPU indices to train on (e.g. ``"0"``
+        or ``"0,1,2"``).
     :return: None
     """
-    cmd = [
-        "accelerate",
-        "launch",
-        "-m",
-        "axolotl.cli.train",
-        str(run_dir.config_path),
-    ]
+    n_gpus = len([d for d in gpu_ids.split(",") if d.strip()])
+    if n_gpus > 1:
+        cmd = [
+            sys.executable,
+            "-m",
+            "accelerate.commands.launch",
+            f"--num_processes={n_gpus}",
+            "--mixed_precision=no",
+            "--dynamo_backend=no",
+            "-m",
+            "axolotl.cli.train",
+            str(run_dir.config_path),
+        ]
+    else:
+        cmd = [
+            sys.executable,
+            "-m",
+            "axolotl.cli.train",
+            str(run_dir.config_path),
+        ]
+    env = os.environ.copy()
+    env["CUDA_VISIBLE_DEVICES"] = gpu_ids
     log.info("Launching training: %s", " ".join(cmd))
-    subprocess.run(cmd, check=True)
+    subprocess.run(cmd, check=True, env=env)
 
 
 # -----------------------------------------------------------------------------
@@ -255,6 +276,7 @@ def execute_run(
     hyperparams: dict[str, Any],
     run_dir: RunDir,
     reference_spec: DatasetSpec | LocalDatasetSpec,
+    gpu_ids: str = "0",
 ) -> RunMetadata:
     """
     Execute a single fine-tuning run end-to-end.
@@ -265,6 +287,8 @@ def execute_run(
     :param run_dir: RunDir instance for this run (not yet created on disk).
     :param reference_spec: Dataset spec used as the reference corpus for
         scoring. Must include ``train``, ``validation``, and ``test`` splits.
+    :param gpu_ids: Comma-separated GPU indices to train on (e.g. ``"0"``
+        or ``"0,1,2"``). Passed to ``accelerate launch``.
     :return: Completed RunMetadata with alignment scores populated.
     :raises RuntimeError: If axolotl training exits with a non-zero status.
     """
@@ -289,7 +313,7 @@ def execute_run(
     reference_ds = get_dataset(reference_spec)
 
     try:
-        run_training(run_dir)
+        run_training(run_dir, gpu_ids=gpu_ids)
     except subprocess.CalledProcessError as exc:
         meta.status = "failed"
         meta.finished_at = _now_iso()
diff --git a/src/voice/finetune/cli.py b/src/voice/finetune/cli.py
index 2db31a2..8a55379 100644
--- a/src/voice/finetune/cli.py
+++ b/src/voice/finetune/cli.py
@@ -69,10 +69,17 @@ def finetune() -> None:
     show_default=True,
     help="HF revision of the reference dataset.",
 )
+@click.option(
+    "--gpus",
+    default="0",
+    show_default=True,
+    help="Comma-separated GPU indices to train on (e.g. '0' or '0,1,2').",
+)
 def single(
     config: Path,
     runs_dir: Path,
     dataset_revision: str,
+    gpus: str,
 ) -> None:
     """Run a single fine-tuning job from CONFIG."""
     cfg = SingleRunConfig.from_yaml(config)
@@ -87,7 +94,9 @@ def single(
     )
     click.echo(f"Starting run: {run_name}")
     try:
-        execute_run(run_name, cfg.axolotl, {}, run_dir, reference_spec)
+        execute_run(
+            run_name, cfg.axolotl, {}, run_dir, reference_spec, gpu_ids=gpus
+        )
     except RuntimeError as exc:
         click.echo(f"Run failed: {exc}", err=True)
         sys.exit(1)
@@ -119,11 +128,18 @@ def single(
     show_default=True,
     help="HF revision of the reference dataset.",
 )
+@click.option(
+    "--gpus",
+    default="0",
+    show_default=True,
+    help="Comma-separated GPU indices to train on (e.g. '0' or '0,1,2').",
+)
 def sweep(
     config: Path,
     runs_dir: Path,
     resume: bool,
     dataset_revision: str,
+    gpus: str,
 ) -> None:
     """Run a hyperparameter sweep from CONFIG."""
     cfg = SweepConfig.from_yaml(config)
@@ -149,7 +165,12 @@ def sweep(
         click.echo(f"Starting run {run_idx}/{len(grid) - 1}: {run_name}")
         try:
             execute_run(
-                run_name, cfg.axolotl, hyperparams, run_dir, reference_spec
+                run_name,
+                cfg.axolotl,
+                hyperparams,
+                run_dir,
+                reference_spec,
+                gpu_ids=gpus,
             )
             mark_completed(state, run_idx, state_file)
             click.echo(f"Run {run_idx} complete: {run_name}")

From 99cf6c0aa4ca845f632e24fa81413f7f4dcf3e95 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Tue, 9 Jun 2026 16:18:22 +0100
Subject: [PATCH 62/68] feat(stylometry): remove number of tokens/words as a
 stylometric metric

---
 src/voice/_defaults.py                    |  5 -----
 src/voice/stylometry/__init__.py          |  2 --
 src/voice/stylometry/metrics.py           | 18 ----------------
 tests/voice/comparison/test_comparison.py | 26 ++++++++++++++---------
 tests/voice/stylometry/test_metrics.py    | 20 ++++++-----------
 5 files changed, 23 insertions(+), 48 deletions(-)

diff --git a/src/voice/_defaults.py b/src/voice/_defaults.py
index 7118cc0..da6b078 100644
--- a/src/voice/_defaults.py
+++ b/src/voice/_defaults.py
@@ -31,17 +31,12 @@ class MetricGroup(str, Enum):
     .. attribute :: CHAR_NGRAM_DIVERSITY
 
         Character n-gram TTR and MATTR across n = 3, 4, 5.
-
-    .. attribute :: TEXT_LENGTH
-
-        Raw token count.
     """
 
     WORD_LENGTH_DISTRIBUTION = "word_length_distribution"
     VOCABULARY_RICHNESS = "vocabulary_richness"
     FUNCTION_WORDS = "function_words"
     CHAR_NGRAM_DIVERSITY = "char_ngram_diversity"
-    TEXT_LENGTH = "text_length"
 
 
 @dataclass(frozen=True)
diff --git a/src/voice/stylometry/__init__.py b/src/voice/stylometry/__init__.py
index e4da059..1e52bc6 100644
--- a/src/voice/stylometry/__init__.py
+++ b/src/voice/stylometry/__init__.py
@@ -19,7 +19,6 @@
     calculate_hapax_legomena_ratio,
     calculate_kurtosis_word_length,
     calculate_moving_avg_type_token_ratio,
-    calculate_num_words,
     calculate_skew_word_length,
     calculate_std_word_length,
     calculate_tri_legomena_ratio,
@@ -38,7 +37,6 @@
     "calculate_std_word_length",
     "calculate_skew_word_length",
     "calculate_kurtosis_word_length",
-    "calculate_num_words",
     "calculate_hapax_legomena_ratio",
     "calculate_dis_legomena_ratio",
     "calculate_tri_legomena_ratio",
diff --git a/src/voice/stylometry/metrics.py b/src/voice/stylometry/metrics.py
index 3f4666c..634c963 100644
--- a/src/voice/stylometry/metrics.py
+++ b/src/voice/stylometry/metrics.py
@@ -9,7 +9,6 @@
     - Standard deviation of word length (`std_word_length`)
     - Skewness of word length (`skew_word_length`)
     - Standard kurtosis of word length (`kurtosis_word_length`)
-    - Total number of words (`num_words`)
     - Hapax legomena ratio (`hapax_legomena_ratio`)
     - Dis legomena ratio (`dis_legomena_ratio`)
     - Tri legomena ratio (`tri_legomena_ratio`)
@@ -235,23 +234,6 @@ def calculate_kurtosis_word_length(text: str) -> float:
 # -----------------------------------------------------------------------------
 
 
-@metric(
-    "num_words",
-    group=MetricGroup.TEXT_LENGTH,
-    description="Number of words in text",
-)
-def calculate_num_words(text: str) -> float:
-    """
-    Calculate the number of words in a string.
-
-    Number of words in an empty string is 0.0.
-
-    :param text: Provided text
-    :return: Number of words as a float
-    """
-    return float(len(word_tokenize(text)))
-
-
 @metric(
     "hapax_legomena_ratio",
     group=MetricGroup.VOCABULARY_RICHNESS,
diff --git a/tests/voice/comparison/test_comparison.py b/tests/voice/comparison/test_comparison.py
index 82666d3..b328b72 100644
--- a/tests/voice/comparison/test_comparison.py
+++ b/tests/voice/comparison/test_comparison.py
@@ -85,7 +85,7 @@ def metric_registry():
     return {
         "len": _Metric(
             fn=lambda s: float(len(s)),
-            group=MetricGroup.TEXT_LENGTH,
+            group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         ),
         "vowels": _Metric(
             fn=lambda s: float(sum(c in "aeiou" for c in s.lower())),
@@ -253,7 +253,9 @@ def raising_metric(s: str) -> float:
         comparison,
         "get_metrics",
         lambda: {
-            "len": _Metric(fn=raising_metric, group=MetricGroup.TEXT_LENGTH)
+            "len": _Metric(
+                fn=raising_metric, group=MetricGroup.WORD_LENGTH_DISTRIBUTION
+            )
         },
     )
 
@@ -281,7 +283,9 @@ def test_self_wasserstein_distribution_is_zero_for_constant_metric(
         comparison,
         "get_metrics",
         lambda: {
-            "const": _Metric(fn=lambda _s: 1.0, group=MetricGroup.TEXT_LENGTH)
+            "const": _Metric(
+                fn=lambda _s: 1.0, group=MetricGroup.WORD_LENGTH_DISTRIBUTION
+            )
         },
     )
 
@@ -305,7 +309,7 @@ def test_self_wasserstein_distribution_is_zero_for_constant_metric(
 def test_comparison_entry_is_frozen_and_slots():
     e = ComparisonEntry(
         metric="m",
-        group=MetricGroup.TEXT_LENGTH,
+        group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         wasserstein=1.0,
         percentile=0.9,
         tail=0.1,
@@ -317,7 +321,7 @@ def test_comparison_entry_is_frozen_and_slots():
 
 def test_comparison_group_entry_is_frozen_and_slots():
     e = GroupEntry(
-        group=MetricGroup.TEXT_LENGTH,
+        group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         avg_percentile=0.9,
         avg_tail=0.1,
     )
@@ -341,7 +345,7 @@ def test_comparison_results_properties_and_add_logic(patch_metrics):
     r.add(metric="len", wasserstein=np.float64(1.25), percentile=0.9)
     e = r.get("len")
     assert e.metric == "len"
-    assert e.group == MetricGroup.TEXT_LENGTH
+    assert e.group == MetricGroup.WORD_LENGTH_DISTRIBUTION
     assert isinstance(e.wasserstein, float)
     assert isinstance(e.percentile, float)
     assert e.tail == pytest.approx(0.1)
@@ -351,7 +355,7 @@ def test_comparison_results_properties_and_add_logic(patch_metrics):
     assert e2.group == MetricGroup.VOCABULARY_RICHNESS
 
     # Group-level checks
-    ge_len = r.get_group(MetricGroup.TEXT_LENGTH)
+    ge_len = r.get_group(MetricGroup.WORD_LENGTH_DISTRIBUTION)
     assert ge_len.avg_percentile == pytest.approx(0.9)
     assert ge_len.avg_tail == pytest.approx(0.1)
 
@@ -375,10 +379,12 @@ def test_comparison_results_score_weights_groups_equally(patch_metrics):
     # Add a second TEXT_LENGTH metric to verify equal group weighting.
     registry = {
         "len": _Metric(
-            fn=lambda s: float(len(s)), group=MetricGroup.TEXT_LENGTH
+            fn=lambda s: float(len(s)),
+            group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         ),
         "len2": _Metric(
-            fn=lambda s: float(len(s)), group=MetricGroup.TEXT_LENGTH
+            fn=lambda s: float(len(s)),
+            group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         ),
         "vowels": _Metric(
             fn=lambda s: float(sum(c in "aeiou" for c in s.lower())),
@@ -418,7 +424,7 @@ def test_comparison_results_group_tails(patch_metrics):
     r.add(metric="vowels", wasserstein=0.5, percentile=0.3)
 
     tails = r.group_tails
-    assert tails[MetricGroup.TEXT_LENGTH] == pytest.approx(0.2)
+    assert tails[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx(0.2)
     assert tails[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx(0.7)
 
 
diff --git a/tests/voice/stylometry/test_metrics.py b/tests/voice/stylometry/test_metrics.py
index 69fc947..8adc336 100644
--- a/tests/voice/stylometry/test_metrics.py
+++ b/tests/voice/stylometry/test_metrics.py
@@ -80,7 +80,6 @@ def test_get_metrics_contains_expected_metric_names():
         "std_word_length",
         "skew_word_length",
         "kurtosis_word_length",
-        "num_words",
         "hapax_legomena_ratio",
         "dis_legomena_ratio",
         "tri_legomena_ratio",
@@ -100,15 +99,19 @@ def test_get_metrics_contains_expected_metric_names():
 def test_metric_decorator_rejects_duplicate_names(monkeypatch):
     # Isolate decorator behaviour by swapping the module registry.
     monkeypatch.setattr(metrics, "_REGISTRY", {})
-    metrics.metric("x", group=MetricGroup.TEXT_LENGTH)(lambda t: 0.0)
+    metrics.metric("x", group=MetricGroup.WORD_LENGTH_DISTRIBUTION)(
+        lambda t: 0.0
+    )
     with pytest.raises(ValueError, match=r"Duplicate metric name 'x'"):
-        metrics.metric("x", group=MetricGroup.TEXT_LENGTH)(lambda t: 1.0)
+        metrics.metric("x", group=MetricGroup.WORD_LENGTH_DISTRIBUTION)(
+            lambda t: 1.0
+        )
 
 
 def test_metric_spec_is_frozen():
     spec = metrics.MetricSpec(
         fn=lambda t: 0.0,
-        group=MetricGroup.TEXT_LENGTH,
+        group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
         description="x",
     )
     assert type(spec).__dataclass_params__.frozen is True
@@ -152,7 +155,6 @@ def test_metric_groups_cover_all_enum_values():
     "std_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION,
     "skew_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION,
     "kurtosis_word_length": MetricGroup.WORD_LENGTH_DISTRIBUTION,
-    "num_words": MetricGroup.TEXT_LENGTH,
     "hapax_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS,
     "dis_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS,
     "tri_legomena_ratio": MetricGroup.VOCABULARY_RICHNESS,
@@ -256,13 +258,6 @@ def fake_moment(units, order: int) -> float:
 # -----------------------------------------------------------------------------
 
 
-def test_num_words_counts_tokens(monkeypatch):
-    monkeypatch.setattr(
-        metrics, "word_tokenize", lambda t: ["a", "b", "c", "d"]
-    )
-    assert metrics.calculate_num_words("ignored") == 4.0
-
-
 def test_hapax_dis_tri_legomena_ratios_exact_on_handmade_tokens(monkeypatch):
     tokens = ["a", "b", "b", "c", "c", "c"]
     monkeypatch.setattr(metrics, "word_tokenize", lambda t: tokens)
@@ -457,6 +452,5 @@ def test_all_metrics_run_on_simple_sentence_smoke():
 
 def test_selected_metrics_on_tiny_text_regression():
     text = "a bb ccc"
-    assert metrics.calculate_num_words(text) == 3.0
     assert metrics.calculate_avg_word_length(text) == pytest.approx(2.0)
     assert metrics.calculate_hapax_legomena_ratio(text) == pytest.approx(1.0)

From c44664ae5dda52d553b4e7bbcf2bd0d2e449d471 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 10:32:28 +0100
Subject: [PATCH 63/68] feat(docs): add UQ to eval suite

---
 docs/02_evals.md | 48 ++++++++++++++++++++++++++++++++++++++++--------
 1 file changed, 40 insertions(+), 8 deletions(-)

diff --git a/docs/02_evals.md b/docs/02_evals.md
index 4b55fce..871d3ca 100644
--- a/docs/02_evals.md
+++ b/docs/02_evals.md
@@ -22,21 +22,21 @@ To interpret $W(X, Y)$ we need to know what distance is *expected even between t
 
 The observed distance is located within $\mathcal{W}_0$ via its empirical CDF:
 
-$$p = \hat{F}_{\mathcal{W}_0}(W(X, Y)), \qquad \tau = 1 - p$$
+$$p = \hat{F}_{\mathcal{W}_0}(W(X, Y)), \qquad t = 1 - p$$
 
-$\tau$ is the **tail value**: the fraction of self-distances that *exceed* the observed distance. A high $\tau$ means the model's completions are no further from the reference than typical same-author samples, i.e. they are stylistically consistent with the reference author.
+$t$ is the **tail value**: the fraction of self-distances that *exceed* the observed distance. A high $t$ means the model's completions are no further from the reference than typical same-author samples, i.e. they are stylistically consistent with the reference author.
 
 ### Step 4: Group and Overall Score
 
-Metrics are organised into groups (see [Stylometric Metrics](01_stylometry.md)). Each group captures a distinct linguistic object — word length, vocabulary richness, and so on — and the metrics within it represent different ways of characterising that same object. Because they study the same underlying property, within-group metrics are highly correlated; averaging within groups before aggregating across them prevents any single linguistic dimension from dominating the score simply by having more metrics defined for it.
+Metrics are organised into groups (see [Stylometric Metrics](01_stylometry.md)). Each group captures a distinct linguistic object (word length, vocabulary richness, .etc) and the metrics within it represent different ways of characterising that same object. Because they study the same underlying property, within-group metrics are highly correlated; averaging within groups before aggregating across them prevents any single linguistic dimension from dominating the score simply by having more metrics defined for it.
 
 Within each group the tail values are averaged:
 
-$$\bar{\tau}_g = \frac{1}{|g|} \sum_{f \in g} \tau_f$$
+$$\bar{t}_g = \frac{1}{|g|} \sum_{f \in g} t_f$$
 
-The overall **alignment score** is then the mean over groups, weighting each group equally:
+The overall **alignment score** $\mathcal{S}$ is then the mean over groups, weighting each group equally:
 
-$$\text{score} = \frac{1}{G} \sum_{g=1}^{G} \bar{\tau}_g \in [0, 1]$$
+$$\mathcal{S} = \frac{1}{G} \sum_{g=1}^{G} \bar{t}_g \in [0, 1]$$
 
 A score of **1** would mean the completions are stylistically indistinguishable from the reference even relative to same-author variation; in practice this is unrealistic but the directionality is still informative.
 
@@ -44,7 +44,7 @@ A score of **1** would mean the completions are stylistically indistinguishable
 
 ## This Is Not a Hypothesis Test
 
-The tail value $\tau$ resembles a p-value but is not one and should not be interpreted as such.
+The tail value $t$ resembles a p-value but is not one and should not be interpreted as such.
 
 A classical hypothesis test asks whether there is sufficient evidence to reject a null hypothesis. That framing entails a binary decision and is sensitive to sample size: with enough data, even negligible stylistic differences will cross any fixed significance threshold.
 
@@ -52,6 +52,38 @@ VOICE is not concerned with whether two distributions are statistically indistin
 
 ---
 
+## Uncertainty Quantification
+
+The uncertainty in $\mathcal{S}$ arises from the finite number of completions $n$. It is estimated using jackknife resampling over the completion set, operated directly at the level of tail values so that no analytical propagation through the calibration CDF is required.
+
+### Jackknife Replicates
+
+For each $i = 1, \dots, n$, remove $c_i$ and its paired reference $d_i$ and run the full pipeline (recomputing the Wasserstein distance and locating it within $\mathcal{W}_0$) for every metric $f$. This yields a matrix of leave-one-out tail values $\{t_{f,-i}\}$, or equivalently $n$ replicate vectors $\mathbf{t}_{-i} \in \mathbb{R}^M$ where $M$ is the total number of metrics across all groups.
+
+### Covariance Matrix
+
+The jackknife covariance matrix $\hat{\Sigma} \in \mathbb{R}^{M \times M}$ is estimated from these replicates:
+
+$$\hat{\Sigma}_{f,f'} = \frac{n-1}{n} \sum_{i=1}^n \left(t_{f,-i} - \bar{t}_{f,-\cdot}\right)\left(t_{f',-i} - \bar{t}_{f',-\cdot}\right)$$
+
+where $\bar{t}_{f,-\cdot} = \frac{1}{n}\sum_i t_{f,-i}$. This matrix captures the full correlation structure across metrics, both within and between groups, without requiring any independence assumptions.
+
+### Propagation to Per-Metric, Group and Overall Uncertainty
+
+The variance of an individual tail value is the corresponding diagonal entry:
+
+$$\hat{\sigma}_{t_f}^2 = \hat{\Sigma}_{ff}$$
+
+For a group score $\bar{t}_g$, the variance is obtained from the submatrix $\hat{\Sigma}_g$ of $\hat{\Sigma}$ corresponding to metrics within group $g$, with uniform weights $\mathbf{w}_g = \frac{1}{|g|}\mathbf{1}$:
+
+$$\hat{\sigma}_{\bar{t}_g}^2 = \mathbf{w}_g^\top \hat{\Sigma}_g \mathbf{w}_g$$
+
+The overall score $\mathcal{S} = \mathbf{w}^\top \mathbf{t}$ is a linear combination of all per-metric tail values, where the weight for metric $f$ in group $g$ is $w_f = \frac{1}{G \cdot |g|}$. Its variance is:
+
+$$\hat{\sigma}_{\mathcal{S}}^2 = \mathbf{w}^\top \hat{\Sigma} \mathbf{w}$$
+
+---
+
 ## Example
 
 Suppose a model achieves an alignment score of **0.2**. A natural reading is:
@@ -77,4 +109,4 @@ results.group_tails    # dict[MetricGroup, float]
 results.score          # float
 ```
 
-Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score.
+Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score.
\ No newline at end of file

From 4be56f4553c7b5309bb8f6aa64e2a0f4a72a4cee Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 12:03:27 +0100
Subject: [PATCH 64/68] feat(evals): add assymetric confidence intervals to
 tail scores

---
 .pre-commit-config.yaml                   |  13 -
 cspell/project-words.txt                  |   1 +
 docs/02_evals.md                          |  41 +--
 pyproject.toml                            |   2 -
 src/voice/_defaults.py                    |  20 ++
 src/voice/comparison/_utils.py            |  32 +++
 src/voice/comparison/comparison.py        | 150 ++++++++++-
 tests/voice/comparison/test_comparison.py | 300 +++++++++++++++++++++-
 8 files changed, 516 insertions(+), 43 deletions(-)

diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0eab24b..74210e4 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -74,16 +74,3 @@ repos:
       - id: pydoclint
         args: ["--config=pyproject.toml"]
         exclude: ^tests/
-
-  # Cognitive complexity limit via flake8-cognitive-complexity (keep for src; exclude tests)
-  - repo: https://github.com/pycqa/flake8
-    rev: 7.3.0
-    hooks:
-      - id: flake8
-        additional_dependencies:
-          - flake8-cognitive-complexity
-        args:
-          - --max-line-length=79
-          - --extend-ignore=E203
-          - --max-cognitive-complexity=10
-        exclude: ^tests/
diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index c377ac6..ecd0a7f 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -19,3 +19,4 @@ hyperparams
 redef
 evals
 submatrix
+vocab
diff --git a/docs/02_evals.md b/docs/02_evals.md
index 871d3ca..564c1a2 100644
--- a/docs/02_evals.md
+++ b/docs/02_evals.md
@@ -54,33 +54,29 @@ VOICE is not concerned with whether two distributions are statistically indistin
 
 ## Uncertainty Quantification
 
-The uncertainty in $\mathcal{S}$ arises from the finite number of completions $n$. It is estimated using jackknife resampling over the completion set, operated directly at the level of tail values so that no analytical propagation through the calibration CDF is required.
+The uncertainty in $\mathcal{S}$ arises from the finite number of completions $n$. It is estimated using jackknife resampling over the completion set, operated directly at the level of tail values so that no analytical propagation through the calibration CDF is required. Operating at the tail value level naturally produces asymmetric confidence intervals that respect the $[0, 1]$ boundary of the score.
 
 ### Jackknife Replicates
 
 For each $i = 1, \dots, n$, remove $c_i$ and its paired reference $d_i$ and run the full pipeline (recomputing the Wasserstein distance and locating it within $\mathcal{W}_0$) for every metric $f$. This yields a matrix of leave-one-out tail values $\{t_{f,-i}\}$, or equivalently $n$ replicate vectors $\mathbf{t}_{-i} \in \mathbb{R}^M$ where $M$ is the total number of metrics across all groups.
 
-### Covariance Matrix
+### Confidence Intervals
 
-The jackknife covariance matrix $\hat{\Sigma} \in \mathbb{R}^{M \times M}$ is estimated from these replicates:
+Per-metric, group, and overall confidence intervals are all read directly from the empirical distribution of the corresponding jackknife replicates.
 
-$$\hat{\Sigma}_{f,f'} = \frac{n-1}{n} \sum_{i=1}^n \left(t_{f,-i} - \bar{t}_{f,-\cdot}\right)\left(t_{f',-i} - \bar{t}_{f',-\cdot}\right)$$
+For an individual metric $f$, the replicates $\{t_{f,-i}\}$ give a distribution of leave-one-out tail values from which percentile intervals are taken directly.
 
-where $\bar{t}_{f,-\cdot} = \frac{1}{n}\sum_i t_{f,-i}$. This matrix captures the full correlation structure across metrics, both within and between groups, without requiring any independence assumptions.
+For a group $g$, the replicate group scores are computed as:
 
-### Propagation to Per-Metric, Group and Overall Uncertainty
+$$\bar{t}_{g,-i} = \frac{1}{|g|} \sum_{f \in g} t_{f,-i}$$
 
-The variance of an individual tail value is the corresponding diagonal entry:
+and percentile intervals are read from $\{\bar{t}_{g,-i}\}$.
 
-$$\hat{\sigma}_{t_f}^2 = \hat{\Sigma}_{ff}$$
+For the overall score, the replicate scores are:
 
-For a group score $\bar{t}_g$, the variance is obtained from the submatrix $\hat{\Sigma}_g$ of $\hat{\Sigma}$ corresponding to metrics within group $g$, with uniform weights $\mathbf{w}_g = \frac{1}{|g|}\mathbf{1}$:
+$$\mathcal{S}_{-i} = \frac{1}{G} \sum_{g=1}^G \bar{t}_{g,-i}$$
 
-$$\hat{\sigma}_{\bar{t}_g}^2 = \mathbf{w}_g^\top \hat{\Sigma}_g \mathbf{w}_g$$
-
-The overall score $\mathcal{S} = \mathbf{w}^\top \mathbf{t}$ is a linear combination of all per-metric tail values, where the weight for metric $f$ in group $g$ is $w_f = \frac{1}{G \cdot |g|}$. Its variance is:
-
-$$\hat{\sigma}_{\mathcal{S}}^2 = \mathbf{w}^\top \hat{\Sigma} \mathbf{w}$$
+and percentile intervals are read from $\{\mathcal{S}_{-i}\}$.
 
 ---
 
@@ -97,16 +93,23 @@ Suppose a model achieves an alignment score of **0.2**. A natural reading is:
 The API exposes intermediate results for deeper inspection:
 
 ```python
-results = make_comparison(completions, true_ds)
+results = make_comparison(completions, true_ds)  # uncertainty=True by default
 
 # Per-metric tail values
-results.metric_tails   # dict[str, float]
+results.metric_tails       # dict[str, float]
 
 # Per-group average tail values
-results.group_tails    # dict[MetricGroup, float]
+results.group_tails        # dict[MetricGroup, float]
 
 # Overall score
-results.score          # float
+results.score              # float
+
+# Jackknife percentile confidence intervals (default 90%)
+results.metric_tail_cis()   # dict[str, tuple[float, float]] | None
+results.group_tail_cis()    # dict[MetricGroup, tuple[float, float]] | None
+results.score_ci()          # tuple[float, float] | None
 ```
 
-Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score.
\ No newline at end of file
+Per-metric tails are useful for identifying which stylometric dimensions are misaligned; per-group tails aggregate correlated metrics and correspond directly to the terms summed in the overall score.
+
+The confidence interval methods accept a `confidence` keyword (default 0.90, see `UNCERTAINTY_DEFAULTS`). Passing `uncertainty=False` to `make_comparison` skips the jackknife pass, in which case the interval methods return `None`.
diff --git a/pyproject.toml b/pyproject.toml
index 2ec89c9..95ecd18 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -42,8 +42,6 @@ where = ["src"]
 [dependency-groups]
 dev = [
     "detect-secrets>=1.5.0",
-    "flake8>=7.3.0",
-    "flake8-cognitive-complexity>=0.1.0",
     "isort>=7.0.0",
     "mypy>=1.19.1",
     "notebook>=7.5.3",
diff --git a/src/voice/_defaults.py b/src/voice/_defaults.py
index da6b078..d7e9d38 100644
--- a/src/voice/_defaults.py
+++ b/src/voice/_defaults.py
@@ -106,6 +106,26 @@ class CalibrationDefaults:
 CALIBRATION_DEFAULTS: CalibrationDefaults = CalibrationDefaults()
 
 
+@dataclass(frozen=True)
+class UncertaintyDefaults:
+    """
+    Default parameters for uncertainty quantification.
+
+    These defaults control the confidence intervals reported alongside
+    comparison results.
+
+    .. attribute :: confidence
+
+        Confidence level for percentile intervals read from the
+        jackknife replicate distributions.
+    """
+
+    confidence: float = 0.90  # Safe default for validation/test size = 150
+
+
+UNCERTAINTY_DEFAULTS: UncertaintyDefaults = UncertaintyDefaults()
+
+
 @dataclass(frozen=True)
 class PlottingDefaults:
     """
diff --git a/src/voice/comparison/_utils.py b/src/voice/comparison/_utils.py
index a684a69..e1756ea 100644
--- a/src/voice/comparison/_utils.py
+++ b/src/voice/comparison/_utils.py
@@ -35,6 +35,38 @@ def calibrated_percentile(value: float, dist: np.ndarray) -> float:
     return float(np.mean(dist <= value))
 
 
+# -----------------------------------------------------------------------------
+# Percentile interval
+# -----------------------------------------------------------------------------
+
+
+def percentile_interval(
+    values: np.ndarray, confidence: float
+) -> tuple[float, float]:
+    """
+    Compute a central percentile interval from an empirical distribution.
+
+    For a confidence level c, this returns the (alpha/2, 1 - alpha/2)
+    empirical percentiles of `values` with alpha = 1 - c. The interval is
+    asymmetric in general and bounded by the range of `values`.
+
+    :param values: Array of samples from an empirical distribution
+    :param confidence: Confidence level, strictly between 0 and 1
+    :return: (lower, upper) percentile interval
+    :raises ValueError: If `values` is empty or `confidence` is not in [0, 1]
+    """
+    values = np.asarray(values, dtype=float).ravel()
+    if values.size == 0:
+        raise ValueError("values must be non-empty.")
+    if not 0.0 < confidence < 1.0:
+        raise ValueError("confidence must be strictly between 0 and 1.")
+
+    alpha = 1.0 - confidence
+    lower = float(np.percentile(values, 100.0 * alpha / 2.0))
+    upper = float(np.percentile(values, 100.0 * (1.0 - alpha / 2.0)))
+    return lower, upper
+
+
 # -----------------------------------------------------------------------------
 # Bootstrap null distribution
 # -----------------------------------------------------------------------------
diff --git a/src/voice/comparison/comparison.py b/src/voice/comparison/comparison.py
index 7173b68..de11e45 100644
--- a/src/voice/comparison/comparison.py
+++ b/src/voice/comparison/comparison.py
@@ -20,11 +20,13 @@
 
 from voice._defaults import (
     CALIBRATION_DEFAULTS,
+    UNCERTAINTY_DEFAULTS,
     MetricGroup,
 )
 from voice.comparison._utils import (
     bootstrap_null_distribution,
     calibrated_percentile,
+    percentile_interval,
 )
 from voice.datasets import VoiceDataset
 from voice.datasets.dataset import Example
@@ -185,6 +187,11 @@ class ComparisonResults:
     A score of 1 indicates perfect stylistic indistinguishability; 0 indicates
     maximum divergence.
 
+    When uncertainty quantification is enabled, the matrix of jackknife
+    leave-one-out tail-value replicates is attached and per-metric,
+    per-group and overall-score percentile confidence intervals are read
+    directly from its empirical distributions.
+
     .. attribute :: metrics
 
         Tuple of stylometric metric names to run comparison for.
@@ -192,6 +199,7 @@ class ComparisonResults:
 
     metrics: tuple[str, ...]
     _entries: dict[str, ComparisonEntry] = field(default_factory=dict)
+    _replicates: np.ndarray | None = field(default=None, repr=False)
 
     def __post_init__(self) -> None:
         """
@@ -222,9 +230,10 @@ def __repr__(self) -> str:
             )
             + "}"
         )
-        return (
-            f"ComparisonResults(groups={group_part}, score={self.score:.4f})"
-        )
+        score_part = f"{self.score:.4f}"
+        if (ci := self.score_ci()) is not None:
+            score_part += f" [{ci[0]:.4f}, {ci[1]:.4f}]"
+        return f"ComparisonResults(groups={group_part}, score={score_part})"
 
     @property
     def num_metrics(self) -> int:
@@ -267,6 +276,95 @@ def score(self) -> float:
             return 0.0
         return sum(e.avg_tail for e in entries.values()) / len(entries)
 
+    @property
+    def _group_indices(self) -> dict[MetricGroup, list[int]]:
+        """
+        Indices into `metrics` (and replicate matrix columns) per group.
+
+        :return: Mapping of MetricGroup to column indices
+        """
+        registry = get_metrics()
+        indices: dict[MetricGroup, list[int]] = {}
+        for j, metric in enumerate(self.metrics):
+            indices.setdefault(registry[metric].group, []).append(j)
+        return indices
+
+    @property
+    def has_uncertainty(self) -> bool:
+        """Whether jackknife uncertainty estimates are attached."""
+        return self._replicates is not None
+
+    def metric_tail_cis(
+        self,
+        confidence: float = UNCERTAINTY_DEFAULTS.confidence,
+    ) -> dict[str, tuple[float, float]] | None:
+        """
+        Jackknife confidence interval for each per-metric tail value.
+
+        Percentile intervals are read directly from the empirical
+        distribution of leave-one-out tail values for each metric.
+
+        :param confidence: Confidence level, strictly between 0 and 1
+        :return: Mapping of metric name to (lower, upper) interval or None if
+            uncertainty quantification was not run
+        """
+        if self._replicates is None:
+            return None
+        return {
+            m: percentile_interval(self._replicates[:, j], confidence)
+            for j, m in enumerate(self.metrics)
+        }
+
+    def group_tail_cis(
+        self,
+        confidence: float = UNCERTAINTY_DEFAULTS.confidence,
+    ) -> dict[MetricGroup, tuple[float, float]] | None:
+        """
+        Jackknife confidence interval for each group's average tail value.
+
+        Each leave-one-out replicate's tail values are averaged within the
+        group, and the percentile interval is read from the resulting
+        empirical distribution of replicate group scores.
+
+        :param confidence: Confidence level, strictly between 0 and 1
+        :return: Mapping of MetricGroup to (lower, upper) interval or None if
+            uncertainty quantification was not run
+        """
+        if self._replicates is None:
+            return None
+        return {
+            group: percentile_interval(
+                self._replicates[:, idx].mean(axis=1), confidence
+            )
+            for group, idx in self._group_indices.items()
+        }
+
+    def score_ci(
+        self,
+        confidence: float = UNCERTAINTY_DEFAULTS.confidence,
+    ) -> tuple[float, float] | None:
+        """
+        Jackknife confidence interval for the overall alignment score.
+
+        Each leave-one-out replicate yields a replicate score (the mean of
+        its per-group average tail values, groups weighted equally); the
+        percentile interval is read from the empirical distribution of these
+        replicate scores.
+
+        :param confidence: Confidence level, strictly between 0 and 1
+        :return: (lower, upper) interval for the score, or None if
+            uncertainty quantification was not run
+        """
+        if self._replicates is None:
+            return None
+        group_scores = np.stack(
+            [
+                self._replicates[:, idx].mean(axis=1)
+                for idx in self._group_indices.values()
+            ]
+        )
+        return percentile_interval(group_scores.mean(axis=0), confidence)
+
     @property
     def metric_tails(self) -> dict[str, float]:
         """
@@ -356,6 +454,7 @@ def make_comparison(
     true_ds: VoiceDataset,
     *,
     metrics: Sequence[str] | None = None,
+    uncertainty: bool = True,
 ) -> ComparisonResults:
     """
     Compare stylometric distributions of `completions` to the true corpus.
@@ -371,14 +470,25 @@ def make_comparison(
     values, and an overall alignment score equal to the mean of group-level
     average tail values (groups weighted equally).
 
+    When `uncertainty` is True (the default), jackknife resampling over the
+    completion set is used: each completion and its paired reference are
+    left out in turn, the Wasserstein distance is recomputed, and the
+    leave-one-out distance is located within the same
+    self-Wasserstein distribution. Per-metric, per-group and overall score
+    percentile confidence intervals are then available on the returned
+    results, read directly from the replicate distributions.
+
     :param completions: Sequence of examples to analyse
     :param true_ds: VoiceDataset providing the true corpus
     :param metrics: Optional subset of metric names; defaults to all metrics
+    :param uncertainty: Whether to estimate uncertainty
     :return: ComparisonResults populated for the selected metrics
     :raises ValueError:
         - If completions is empty,
         - If sample sizes are invalid
         - If metrics are not registered
+        - If `uncertainty` is True and there are fewer than two completions
+          or the observed split does not match the completions in length
     """
     if not completions:
         raise ValueError("completions must be non-empty.")
@@ -392,20 +502,52 @@ def make_comparison(
     observed_split = completions[0].split
     ds_observed_split = true_ds[observed_split]
 
+    n = len(completions)
+    if uncertainty:
+        if n < 2:
+            raise ValueError(
+                "uncertainty quantification requires at least 2 completions."
+            )
+        if len(ds_observed_split) != n:
+            raise ValueError(
+                "uncertainty quantification requires completions to be "
+                "paired one-to-one with the observed split "
+                f"({n} completions vs {len(ds_observed_split)} references)."
+            )
+
+    distributions: dict[str, tuple[np.ndarray, np.ndarray, np.ndarray]] = {}
+
     for metric in metric_list:
         x = stylometric_distribution(completions, metric)
         y = stylometric_distribution(ds_observed_split, metric)
 
         d = float(wasserstein_distance(x, y))
 
+        # Cached per metric: the jackknife pass below reuses this
+        # distribution rather than re-running the bootstrap per replicate.
         self_dist = self_wasserstein_distribution(
             true_ds,
             metric=metric,
-            sample_size=len(completions),
+            sample_size=n,
         )
 
         p = calibrated_percentile(d, self_dist)
 
         results.add(metric=metric, wasserstein=d, percentile=p)
 
+        if uncertainty:
+            distributions[metric] = (x, y, self_dist)
+
+    if uncertainty:
+        replicates = np.empty((n, len(metric_list)), dtype=float)
+        for i in range(n):
+            mask = np.ones(n, dtype=bool)
+            mask[i] = False
+            for j, metric in enumerate(metric_list):
+                x, y, self_dist = distributions[metric]
+                d_i = float(wasserstein_distance(x[mask], y[mask]))
+                replicates[i, j] = 1.0 - calibrated_percentile(d_i, self_dist)
+
+        results._replicates = replicates
+
     return results
diff --git a/tests/voice/comparison/test_comparison.py b/tests/voice/comparison/test_comparison.py
index b328b72..84f1973 100644
--- a/tests/voice/comparison/test_comparison.py
+++ b/tests/voice/comparison/test_comparison.py
@@ -20,7 +20,10 @@
 from datasets import Dataset
 
 from voice._defaults import MetricGroup
-from voice.comparison._utils import calibrated_percentile
+from voice.comparison._utils import (
+    calibrated_percentile,
+    percentile_interval,
+)
 from voice.comparison.comparison import (
     ComparisonEntry,
     ComparisonResults,
@@ -154,6 +157,42 @@ def test_calibrated_percentile_flattens_non_1d_inputs():
     assert calibrated_percentile(2.0, dist) == 0.5
 
 
+# -----------------------------------------------------------------------------
+# percentile_interval
+# -----------------------------------------------------------------------------
+
+
+def test_percentile_interval_rejects_empty_values():
+    with pytest.raises(ValueError, match=r"values must be non-empty"):
+        percentile_interval(np.array([]), 0.9)
+
+
+@pytest.mark.parametrize("confidence", [0.0, 1.0, -0.1, 1.5])
+def test_percentile_interval_rejects_invalid_confidence(confidence):
+    with pytest.raises(ValueError, match=r"strictly between 0 and 1"):
+        percentile_interval(np.array([0.1, 0.2]), confidence)
+
+
+def test_percentile_interval_hand_computed_example():
+    # 25th/75th percentiles of 5 evenly spaced points (linear interpolation)
+    values = np.array([0.1, 0.2, 0.3, 0.4, 0.5])
+    lo, hi = percentile_interval(values, 0.5)
+    assert lo == pytest.approx(0.2)
+    assert hi == pytest.approx(0.4)
+
+
+def test_percentile_interval_collapses_for_constant_values():
+    lo, hi = percentile_interval(np.full(10, 0.7), 0.9)
+    assert lo == pytest.approx(0.7)
+    assert hi == pytest.approx(0.7)
+
+
+def test_percentile_interval_is_bounded_by_value_range():
+    values = np.array([0.0, 0.0, 0.0, 0.9])
+    lo, hi = percentile_interval(values, 0.99)
+    assert 0.0 <= lo <= hi <= 0.9
+
+
 # -----------------------------------------------------------------------------
 # self_wasserstein_distribution
 # -----------------------------------------------------------------------------
@@ -428,6 +467,92 @@ def test_comparison_results_group_tails(patch_metrics):
     assert tails[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx(0.7)
 
 
+def test_comparison_results_uncertainty_defaults_to_none(patch_metrics):
+    r = ComparisonResults(metrics=("len", "vowels"))
+    assert r.has_uncertainty is False
+    assert r.metric_tail_cis() is None
+    assert r.group_tail_cis() is None
+    assert r.score_ci() is None
+
+
+def test_comparison_results_confidence_intervals(patch_metrics):
+    r = ComparisonResults(metrics=("len", "vowels"))
+    r.add(metric="len", wasserstein=1.0, percentile=0.8)
+    r.add(metric="vowels", wasserstein=0.5, percentile=0.4)
+    r._replicates = np.array(
+        [
+            [0.1, 0.5],
+            [0.2, 0.6],
+            [0.3, 0.7],
+            [0.4, 0.8],
+            [0.5, 0.9],
+        ]
+    )
+
+    assert r.has_uncertainty is True
+
+    cis = r.metric_tail_cis(confidence=0.5)
+    assert cis["len"] == pytest.approx((0.2, 0.4))
+    assert cis["vowels"] == pytest.approx((0.6, 0.8))
+
+    # One metric per group, so group CI equals metric CI
+    g = r.group_tail_cis(confidence=0.5)
+    assert g[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx((0.2, 0.4))
+    assert g[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx((0.6, 0.8))
+
+    # Replicate scores are row means over groups: [0.3, 0.4, 0.5, 0.6, 0.7]
+    assert r.score_ci(confidence=0.5) == pytest.approx((0.4, 0.6))
+
+
+def test_comparison_results_cis_average_replicates_within_groups(
+    patch_metrics, monkeypatch
+):
+    registry = {
+        "len": _Metric(
+            fn=lambda s: float(len(s)),
+            group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
+        ),
+        "len2": _Metric(
+            fn=lambda s: float(len(s)),
+            group=MetricGroup.WORD_LENGTH_DISTRIBUTION,
+        ),
+        "vowels": _Metric(
+            fn=lambda s: float(sum(c in "aeiou" for c in s.lower())),
+            group=MetricGroup.VOCABULARY_RICHNESS,
+        ),
+    }
+    import voice.comparison.comparison as comparison
+
+    monkeypatch.setattr(comparison, "get_metrics", lambda: dict(registry))
+
+    r = ComparisonResults(metrics=("len", "len2", "vowels"))
+    r._replicates = np.array(
+        [
+            [0.0, 0.2, 0.9],
+            [0.2, 0.4, 0.9],
+            [0.4, 0.6, 0.9],
+        ]
+    )
+
+    # Group replicates: word_length = [0.1, 0.3, 0.5], vocab = [0.9] * 3
+    g = r.group_tail_cis(confidence=0.5)
+    assert g[MetricGroup.WORD_LENGTH_DISTRIBUTION] == pytest.approx((0.2, 0.4))
+    assert g[MetricGroup.VOCABULARY_RICHNESS] == pytest.approx((0.9, 0.9))
+
+    # Score replicates: mean over groups = [0.5, 0.6, 0.7]
+    assert r.score_ci(confidence=0.5) == pytest.approx((0.55, 0.65))
+
+
+def test_comparison_results_repr_includes_ci_when_available(patch_metrics):
+    r = ComparisonResults(metrics=("len",))
+    r.add(metric="len", wasserstein=1.0, percentile=0.8)
+    assert "[" not in repr(r)
+
+    # 5th/95th percentiles of [0.1, 0.2, 0.3] (default confidence 0.90)
+    r._replicates = np.array([[0.1], [0.2], [0.3]])
+    assert "[0.1100, 0.2900]" in repr(r)
+
+
 # -----------------------------------------------------------------------------
 # make_comparison
 # -----------------------------------------------------------------------------
@@ -464,7 +589,7 @@ def fake_self_dist(*_args, **_kwargs):
 
     comparison.self_wasserstein_distribution = fake_self_dist  # type: ignore[assignment]
 
-    out = make_comparison(completions, true_ds)
+    out = make_comparison(completions, true_ds, uncertainty=False)
 
     assert out.metrics == ("len", "vowels")
     for m in out.metrics:
@@ -492,7 +617,9 @@ def test_make_comparison_respects_subset_metrics(patch_metrics, monkeypatch):
         lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float),
     )
 
-    out = make_comparison(completions, true_ds, metrics=["len"])
+    out = make_comparison(
+        completions, true_ds, metrics=["len"], uncertainty=False
+    )
     assert out.metrics == ("len",)
     assert "len" in out.as_dict()
     with pytest.raises(KeyError):
@@ -529,7 +656,9 @@ def recording_getitem(self, key):
 
     monkeypatch.setattr(VoiceDataset, "__getitem__", recording_getitem)
 
-    _ = make_comparison(completions, true_ds, metrics=["len"])
+    _ = make_comparison(
+        completions, true_ds, metrics=["len"], uncertainty=False
+    )
 
     assert Split.TEST in calls
 
@@ -563,6 +692,167 @@ def spy_self_dist(*_args, sample_size: int = 0, **_kwargs):
         comparison, "self_wasserstein_distribution", spy_self_dist
     )
 
-    make_comparison(completions, true_ds, metrics=["len"])
+    make_comparison(completions, true_ds, metrics=["len"], uncertainty=False)
 
     assert all(s == len(completions) for s in captured)
+
+
+# -----------------------------------------------------------------------------
+# make_comparison: uncertainty quantification
+# -----------------------------------------------------------------------------
+
+
+def _paired_setup(n: int = 5) -> tuple[list[Example], VoiceDataset]:
+    """Completions paired one-to-one with the TEST split."""
+    p = _pinned(splits=(Split.TRAIN, Split.TEST))
+    true_ds = VoiceDataset(
+        datasets={
+            Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]),
+            Split.TEST: _canonical_hf_ds(
+                ["ref text " * (i + 1) for i in range(n)]
+            ),
+        },
+        spec=p,
+    )
+    completions = [
+        _ex(answer="hello there " * (i + 2), split=Split.TEST)
+        for i in range(n)
+    ]
+    return completions, true_ds
+
+
+def test_make_comparison_uncertainty_populates_cis_and_caches_self_dist(
+    patch_metrics, monkeypatch
+):
+    completions, true_ds = _paired_setup(n=5)
+
+    import voice.comparison.comparison as comparison
+
+    calls: list[str] = []
+
+    def spy_self_dist(*_args, metric: str = "", **_kwargs):
+        calls.append(metric)
+        return np.linspace(0.0, 50.0, 100)
+
+    monkeypatch.setattr(
+        comparison, "self_wasserstein_distribution", spy_self_dist
+    )
+
+    out = make_comparison(completions, true_ds)
+
+    assert out.has_uncertainty is True
+
+    cis = out.metric_tail_cis()
+    assert set(cis) == {"len", "vowels"}
+    for lo, hi in cis.values():
+        assert 0.0 <= lo <= hi <= 1.0
+
+    assert set(out.group_tail_cis()) == {
+        MetricGroup.WORD_LENGTH_DISTRIBUTION,
+        MetricGroup.VOCABULARY_RICHNESS,
+    }
+
+    lo, hi = out.score_ci()
+    assert 0.0 <= lo <= hi <= 1.0
+
+    # A wider confidence level produces a containing interval
+    lo99, hi99 = out.score_ci(confidence=0.99)
+    assert lo99 <= lo
+    assert hi <= hi99
+
+    # Self-Wasserstein bootstrap is cached: computed exactly once per
+    # metric, never per jackknife replicate.
+    assert sorted(calls) == ["len", "vowels"]
+
+
+def test_make_comparison_uncertainty_false_skips_jackknife(
+    patch_metrics, monkeypatch
+):
+    completions, true_ds = _paired_setup(n=5)
+
+    import voice.comparison.comparison as comparison
+
+    monkeypatch.setattr(
+        comparison,
+        "self_wasserstein_distribution",
+        lambda *_args, **_kwargs: np.array([0.0, 1.0], dtype=float),
+    )
+
+    out = make_comparison(completions, true_ds, uncertainty=False)
+
+    assert out.has_uncertainty is False
+    assert out.metric_tail_cis() is None
+    assert out.group_tail_cis() is None
+    assert out.score_ci() is None
+
+
+def test_make_comparison_uncertainty_requires_two_completions(
+    patch_metrics, monkeypatch
+):
+    completions, true_ds = _paired_setup(n=1)
+
+    with pytest.raises(ValueError, match=r"at least 2 completions"):
+        make_comparison(completions, true_ds)
+
+
+def test_make_comparison_uncertainty_requires_paired_split(
+    patch_metrics, monkeypatch
+):
+    completions, true_ds = _paired_setup(n=5)
+
+    with pytest.raises(ValueError, match=r"paired one-to-one"):
+        make_comparison(completions[:3], true_ds)
+
+
+def test_make_comparison_score_ci_matches_metric_ci_for_single_metric(
+    patch_metrics, monkeypatch
+):
+    completions, true_ds = _paired_setup(n=5)
+
+    import voice.comparison.comparison as comparison
+
+    monkeypatch.setattr(
+        comparison,
+        "self_wasserstein_distribution",
+        lambda *_args, **_kwargs: np.linspace(0.0, 50.0, 100),
+    )
+
+    out = make_comparison(completions, true_ds, metrics=["len"])
+
+    assert out.score_ci() == pytest.approx(out.metric_tail_cis()["len"])
+    assert out.score_ci() == pytest.approx(
+        out.group_tail_cis()[MetricGroup.WORD_LENGTH_DISTRIBUTION]
+    )
+
+
+def test_make_comparison_ci_collapses_for_identical_paired_data(
+    patch_metrics, monkeypatch
+):
+    n = 5
+    answers = [f"some answer {i} " * (i + 1) for i in range(n)]
+    p = _pinned(splits=(Split.TRAIN, Split.TEST))
+    true_ds = VoiceDataset(
+        datasets={
+            Split.TRAIN: _canonical_hf_ds([f"a{i}" for i in range(40)]),
+            Split.TEST: _canonical_hf_ds(answers),
+        },
+        spec=p,
+    )
+    completions = [_ex(answer=a, split=Split.TEST) for a in answers]
+
+    import voice.comparison.comparison as comparison
+
+    monkeypatch.setattr(
+        comparison,
+        "self_wasserstein_distribution",
+        lambda *_args, **_kwargs: np.linspace(0.0, 50.0, 100),
+    )
+
+    # x == y for every metric, so every leave-one-out distance is 0 and
+    # all replicates are identical: each interval collapses to a point.
+    out = make_comparison(completions, true_ds)
+
+    lo, hi = out.score_ci()
+    assert lo == pytest.approx(hi)
+    for m_lo, m_hi in out.metric_tail_cis().values():
+        assert m_lo == pytest.approx(m_hi)

From 724c10b6f8cbfae106dc1884e50f7ff66a061d63 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 12:10:41 +0100
Subject: [PATCH 65/68] feat(finetuning): write confidence intervals to disk
 via fine-tuning orchestrator

---
 src/voice/finetune/_orchestrator.py | 16 ++++++++++++++--
 1 file changed, 14 insertions(+), 2 deletions(-)

diff --git a/src/voice/finetune/_orchestrator.py b/src/voice/finetune/_orchestrator.py
index f06727c..40a74e9 100644
--- a/src/voice/finetune/_orchestrator.py
+++ b/src/voice/finetune/_orchestrator.py
@@ -177,8 +177,11 @@ def _score_split(
 
     Alignment results are written to
     ``alignment/epoch_{n}/{split}.json`` with keys ``score``,
-    ``metric_tails``, and ``group_tails``. Only the overall score is
-    returned; the full tails are available on disk only.
+    ``score_ci``, ``metric_tails``, ``metric_tail_cis``, ``group_tails``,
+    and ``group_tail_cis``. Confidence intervals are jackknife percentile
+    intervals at the default confidence level and are serialised as
+    ``[lower, upper]`` pairs. Only the overall score is returned; the
+    full tails and intervals are available on disk only.
 
     :param run_dir: Run directory.
     :param split: Split name (``"validation"`` or ``"test"``).
@@ -191,16 +194,25 @@ def _score_split(
     completions = _load_completions(run_dir, split, epoch)
     results = make_comparison(completions, reference_ds)
 
+    group_cis = results.group_tail_cis()
+
     out_path = run_dir.alignment_path(split, epoch)
     out_path.parent.mkdir(parents=True, exist_ok=True)
     out_path.write_text(
         json.dumps(
             {
                 "score": results.score,
+                "score_ci": results.score_ci(),
                 "metric_tails": results.metric_tails,
+                "metric_tail_cis": results.metric_tail_cis(),
                 "group_tails": {
                     g.value: v for g, v in results.group_tails.items()
                 },
+                "group_tail_cis": (
+                    None
+                    if group_cis is None
+                    else {g.value: ci for g, ci in group_cis.items()}
+                ),
             },
             indent=2,
         )

From 1c10d2fc01de3edf088f529d1da4f54b9344de58 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 14:56:16 +0100
Subject: [PATCH 66/68] feat(docs): add documentation for fine-tuning CLI

---
 cspell/library-words.txt |   1 +
 cspell/project-words.txt |   1 +
 docs/03_cli.md           | 166 +++++++++++++++++++++++++++++++++++++++
 3 files changed, 168 insertions(+)
 create mode 100644 docs/03_cli.md

diff --git a/cspell/library-words.txt b/cspell/library-words.txt
index db6cebd..f9f113e 100644
--- a/cspell/library-words.txt
+++ b/cspell/library-words.txt
@@ -23,3 +23,4 @@ pyyaml
 libcuda
 PYTHONPATH
 venv
+cuda
diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index ecd0a7f..c6c65d5 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -20,3 +20,4 @@ redef
 evals
 submatrix
 vocab
+finetune
diff --git a/docs/03_cli.md b/docs/03_cli.md
new file mode 100644
index 0000000..a5662a9
--- /dev/null
+++ b/docs/03_cli.md
@@ -0,0 +1,166 @@
+# Fine-Tuning CLI
+
+---
+
+The `voice finetune` command group runs LoRA fine-tuning jobs via [axolotl](https://github.com/axolotl-ai-cloud/axolotl). Two subcommands are available: `single` for a single training run and `sweep` for a hyperparameter grid search.
+
+---
+
+## Single Run
+
+```bash
+voice finetune single <config> [OPTIONS]
+```
+
+| Option | Default | Description |
+|---|---|---|
+| `--runs-dir` | `runs` | Root directory for run outputs |
+| `--gpus` | `0` | Comma-separated CUDA device indices, e.g. `0,1,2` |
+| `--dataset-revision` | `main` | HuggingFace dataset branch or revision |
+
+The config file has a single top-level [`axolotl:`](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) key containing all training settings. The CLI injects `output_dir` automatically.
+
+```yaml
+axolotl:
+  base_model: meta-llama/Llama-3.1-8B-Instruct
+
+  datasets:
+    - path: AccelerateScience/bo-press-conference-qa
+      type: chat_template
+      field_messages: messages
+      roles_to_train: [assistant]
+      train_on_eos: last
+
+  adapter: lora
+  lora_r: 16
+  lora_alpha: 16
+  lora_target_modules: [q_proj, k_proj, v_proj, o_proj]
+
+  num_epochs: 3
+  learning_rate: 1.0e-4
+  micro_batch_size: 4
+  gradient_accumulation_steps: 4
+
+  plugins:
+    - voice.finetune.callbacks.EvalCompletionsPlugin
+```
+
+A full example is at `configs/single/example.yaml`.
+
+When a run starts, the CLI generates a unique run name (model slug + short UUID), writes a frozen copy of the resolved config to `runs/{run_name}/config.yaml`, then launches axolotl. The `EvalCompletionsPlugin` generates completions against the validation and test splits at the end of every epoch; alignment scores are computed immediately after.
+
+For multiple GPUs the CLI switches automatically to `accelerate launch --num_processes=N`.
+
+---
+
+## Sweep
+
+```bash
+voice finetune sweep <config> [OPTIONS]
+```
+
+| Option | Default | Description |
+|---|---|---|
+| `--runs-dir` | `runs` | Root directory for run outputs |
+| `--gpus` | `0` | Comma-separated CUDA device indices |
+| `--dataset-revision` | `main` | HuggingFace dataset branch or revision |
+| `--resume` / `--no-resume` | off | Skip runs already recorded as completed in `state.json` |
+
+The sweep config has two top-level keys: `sweep:` defines the grid axes and [`axolotl:`](https://axolotl-ai-cloud.github.io/axolotl/docs/config.html) defines settings shared across every run.
+
+```yaml
+sweep:
+  learning_rate: [1.0e-4, 5.0e-4, 1.0e-3]
+  lora_r: [4, 8, 16, 32]
+  micro_batch_size: [4]
+  gradient_accumulation_steps: [1, 4]
+  target_layers:
+    - name: attention
+      modules: [q_proj, k_proj, v_proj, o_proj]
+    - name: mlp
+      modules: [gate_proj, up_proj, down_proj]
+
+axolotl:
+  base_model: meta-llama/Llama-3.1-8B-Instruct
+  # ... shared settings ...
+```
+
+All five `sweep:` axes are required. The CLI expands them into a Cartesian product; `lora_alpha` is set equal to `lora_r` for each run and does not need to be listed. Per-run values for `learning_rate`, `lora_r`, `lora_alpha`, `lora_target_modules`, `micro_batch_size`, and `gradient_accumulation_steps` are merged on top of the shared `axolotl:` block before training starts.
+
+A full example is at `configs/sweep/example.yaml`.
+
+### Resume
+
+The sweep writes a `state.json` to `{runs-dir}/state.json` after each run completes or fails:
+
+```json
+{
+  "completed": ["0", "1", "3"],
+  "failed": {"2": "CUDA out of memory"}
+}
+```
+
+Passing `--resume` skips any run whose index already appears in `completed`. Failed runs are retried.
+
+---
+
+## Output Layout
+
+Each run produces a directory under `{runs-dir}/{run_name}/`:
+
+```
+runs/{run_name}/
+  config.yaml            # frozen per-run axolotl config
+  metadata.json          # status, hyperparams, scores
+  adapter/               # axolotl output_dir; LoRA weights and checkpoints
+  completions/
+    epoch_1/
+      validation.jsonl
+      test.jsonl
+    epoch_N/...
+  alignment/
+    epoch_1/
+      validation.json    # alignment scores + confidence intervals
+      test.json
+    epoch_N/...
+```
+
+`metadata.json` tracks the full lifecycle of a run:
+
+```json
+{
+  "run_name": "llama-3-1-8b-instruct_a3f2b9c1",
+  "model": "meta-llama/Llama-3.1-8B-Instruct",
+  "dataset": "AccelerateScience/bo-press-conference-qa",
+  "hyperparams": { "learning_rate": 1e-4, "lora_r": 16, "..." : "..." },
+  "status": "done",
+  "started_at": "2025-01-15T10:30:00Z",
+  "finished_at": "2025-01-15T14:22:00Z",
+  "eval_loss": { "1": 1.23, "2": 0.98, "3": 0.81 },
+  "alignment_val": { "1": 0.42, "2": 0.61, "3": 0.67 },
+  "alignment_test": { "1": 0.39, "2": 0.58, "3": 0.64 }
+}
+```
+
+---
+
+## Weights & Biases
+
+Add three keys anywhere inside the `axolotl:` block to enable W&B logging:
+
+```yaml
+axolotl:
+  use_wandb: true
+  wandb_project: VOICE
+  wandb_entity: accelerate-science
+```
+
+Axolotl handles all logging; no additional code is required. The run name is derived from the model slug and a unique ID and is set automatically. Omitting these keys (or setting `use_wandb: false`) disables W&B entirely.
+
+The environment must be authenticated before launching a run:
+
+```bash
+wandb login
+# or
+export WANDB_API_KEY=<your-key>
+```

From c50aacb656b972001d57ad964c41f9f7e54b1e32 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 15:03:45 +0100
Subject: [PATCH 67/68] chore(docs): add example configuration files for
 fine-tuning CLI

---
 .gitignore                  |   9 +--
 configs/single/.gitkeep     |   0
 configs/single/example.yaml |  87 ++++++++++++++++++++++++
 configs/sweep/.gitkeep      |   0
 configs/sweep/example.yaml  | 127 ++++++++++++++++++++++++++++++++++++
 cspell/project-words.txt    |   1 +
 src/voice/finetune/cli.py   |   2 +-
 7 files changed, 221 insertions(+), 5 deletions(-)
 delete mode 100644 configs/single/.gitkeep
 create mode 100644 configs/single/example.yaml
 delete mode 100644 configs/sweep/.gitkeep
 create mode 100644 configs/sweep/example.yaml

diff --git a/.gitignore b/.gitignore
index e0d9f1d..cf5d2ca 100644
--- a/.gitignore
+++ b/.gitignore
@@ -70,7 +70,8 @@ sweep/state.json
 # Fine-tuning runs and local configs
 runs/
 !runs/.gitkeep
-configs/single
-configs/sweep
-!configs/single/.gitkeep
-!configs/sweep/.gitkeep
+configs/single/**
+configs/sweep/**
+!configs/single/example.yaml
+!configs/single/test.yaml
+!configs/sweep/example.yaml
diff --git a/configs/single/.gitkeep b/configs/single/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/configs/single/example.yaml b/configs/single/example.yaml
new file mode 100644
index 0000000..149372c
--- /dev/null
+++ b/configs/single/example.yaml
@@ -0,0 +1,87 @@
+# =============================================================================
+# Example single-run configuration
+#
+# Run with:
+#   voice finetune single configs/single/example.yaml
+#
+# All axolotl settings live under the `axolotl:` key. The CLI writes a
+# frozen copy to runs/{run_name}/config.yaml before launching training,
+# injecting output_dir automatically.
+# =============================================================================
+
+axolotl:
+
+  # Model
+  base_model: meta-llama/Llama-3.1-8B-Instruct
+
+  # Train split used for fine-tuning; validation and test splits used by
+  # EvalCompletionsPlugin and post-training alignment scoring.
+  datasets:
+    - path: AccelerateScience/bo-press-conference-qa
+      type: chat_template
+      field_messages: messages
+      roles_to_train:
+        - assistant
+      train_on_eos: last
+      eot_tokens: ["<|eot_id|>"]
+
+  special_tokens:
+    pad_token: "<|eot_id|>"
+
+  hf_use_auth_token: true
+
+  chat_template: tokenizer_default
+  val_set_size: 0.0
+
+  # Validation dataset for per-epoch eval_loss and completions generation.
+  test_datasets:
+    - path: AccelerateScience/bo-press-conference-qa
+      split: validation
+      type: chat_template
+      field_messages: messages
+
+  # Sequence
+  sequence_len: 2048
+  sample_packing: true
+
+  # Adapter
+  adapter: lora
+  lora_r: 16
+  lora_alpha: 16
+  lora_dropout: 0.0
+  lora_target_modules: [q_proj, k_proj, v_proj, o_proj]
+
+  # Training
+  num_epochs: 3
+  learning_rate: 1.0e-4
+  micro_batch_size: 4
+  gradient_accumulation_steps: 4
+
+  # Precision & performance
+  bf16: true
+  tf32: true
+  flash_attention: true
+  gradient_checkpointing: true
+  lora_mlp_kernel: false
+  lora_qkv_kernel: false
+  lora_o_kernel: false
+
+  # Optimiser
+  optimizer: adamw_torch_fused
+  lr_scheduler: cosine
+  warmup_ratio: 0.03
+  weight_decay: 0.0
+
+  # Evaluation and checkpointing
+  eval_strategy: "epoch"
+  save_strategy: epoch
+  save_total_limit: 3
+
+  # Plugin that generates completions at each epoch end (recommended).
+  plugins:
+    - voice.finetune.callbacks.EvalCompletionsPlugin
+
+  # W&B
+  use_wandb: true
+  wandb_project: VOICE
+  wandb_entity: accelerate-science
diff --git a/configs/sweep/.gitkeep b/configs/sweep/.gitkeep
deleted file mode 100644
index e69de29..0000000
diff --git a/configs/sweep/example.yaml b/configs/sweep/example.yaml
new file mode 100644
index 0000000..618829a
--- /dev/null
+++ b/configs/sweep/example.yaml
@@ -0,0 +1,127 @@
+# =============================================================================
+# Example sweep configuration
+#
+# Two top-level sections:
+#   sweep:   hyperparameter grid axes (read by `voice finetune sweep`)
+#   axolotl: shared axolotl settings merged into every per-run config
+#
+# The CLI expands the grid into per-run configs, injecting per-run values
+# (learning_rate, lora_r, lora_alpha, lora_target_modules,
+# micro_batch_size, gradient_accumulation_steps, output_dir) on top of
+# the axolotl section below.
+#
+# Grid: 3 LRs × 4 ranks × 1 micro_batch_size × 2 accum steps
+#       × 2 target layer groups = 48 training runs.
+#
+# Run with:
+#   voice finetune sweep configs/sweep/example.yaml
+#   voice finetune sweep configs/sweep/example.yaml --resume
+# =============================================================================
+
+# --- Sweep grid -------------------------------------------------------------
+sweep:
+  learning_rate: [1e-3, 5.0e-4, 1e-4]
+  lora_r: [4, 8, 16, 32]
+
+  micro_batch_size: [2]
+  gradient_accumulation_steps: [2, 4]
+
+  target_layers:
+    - name: mlp
+      modules: [gate_proj, up_proj, down_proj]
+    - name: attention
+      modules: [q_proj, k_proj, v_proj, o_proj]
+
+# lora_alpha is set equal to lora_r per run (not swept separately).
+
+# --- Shared axolotl settings ------------------------------------------------
+axolotl:
+
+  # Model
+  base_model: meta-llama/Llama-3.1-8B-Instruct
+
+  # Train split used for fine-tuning; validation split used by the
+  # EvalCompletionsPlugin for per-epoch completions; test split scored after
+  # each epoch for alignment evaluation.
+  datasets:
+    - path: AccelerateScience/gwb-press-conference-qa
+      type: chat_template
+      field_messages: messages
+      roles_to_train:
+        - assistant
+      train_on_eos: last
+      # Llama 3.1's chat template ends turns with <|eot_id|> (128009), not
+      # <|end_of_text|> (128001). Without this, axolotl defaults eot_tokens
+      # to eos_token which never appears, so train_on_eos: last silently
+      # does nothing.
+      eot_tokens: ["<|eot_id|>"]
+
+  special_tokens:
+    pad_token: "<|eot_id|>"
+
+  hf_use_auth_token: true
+
+  # Llama 3.1 Instruct ships with its own Jinja chat template; axolotl
+  # picks it up automatically.
+  chat_template: tokenizer_default
+  val_set_size: 0.0
+
+  # Axolotl computes eval_loss after each epoch and the EvalCompletionsPlugin
+  # generates completions for scoring.
+  test_datasets:
+    - path: AccelerateScience/gwb-press-conference-qa
+      split: validation
+      type: chat_template
+      field_messages: messages
+
+  # Sequence
+  # Bush answers are long (~700-900 tokens including system + question).
+  # 2048 gives comfortable headroom without wasting VRAM.
+  sequence_len: 2048
+  sample_packing: true
+
+  # lora_r, lora_alpha, lora_target_modules injected per run.
+  adapter: lora
+  lora_dropout: 0.05
+
+  # 3 epochs × 48 runs = 144 data points.
+  num_epochs: 3
+
+  # Precision & performance
+  bf16: true
+  tf32: true
+  flash_attention: true
+  gradient_checkpointing: true
+
+  # Disable axolotl's auto-enabled LoRA kernel optimizations. The MLP
+  # SwiGLU Triton kernel fails to compile on our host (gcc cannot link
+  # libcuda), which crashes all attention_mlp runs.
+  lora_mlp_kernel: false
+  lora_qkv_kernel: false
+  lora_o_kernel: false
+
+  # Optimiser
+  optimizer: adamw_torch_fused
+  lr_scheduler: cosine
+  warmup_ratio: 0.03
+  weight_decay: 0.0
+
+  # Evaluate on the validation split after each epoch.
+  eval_strategy: "epoch"
+
+  # Save a checkpoint after each epoch so adapters are available for
+  # per-epoch alignment scoring (intermediate epochs treated as separate
+  # model candidates).
+  save_strategy: epoch
+  save_total_limit: 3
+
+  # Generates completions at each epoch end using the live in-memory model
+  # (no train/inference distribution mismatch).
+  # Dataset is read from datasets[0].path above.
+  plugins:
+    - voice.finetune.callbacks.EvalCompletionsPlugin
+
+  # Run name for W&B is set by the CLI; project and entity configured here.
+  use_wandb: true
+  wandb_project: VOICE
+  wandb_entity: accelerate-science
diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index c6c65d5..e45257a 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -21,3 +21,4 @@ evals
 submatrix
 vocab
 finetune
+accum
diff --git a/src/voice/finetune/cli.py b/src/voice/finetune/cli.py
index 8a55379..abafb43 100644
--- a/src/voice/finetune/cli.py
+++ b/src/voice/finetune/cli.py
@@ -9,7 +9,7 @@
 Usage::
 
     voice finetune single configs/single/my_run.yaml
-    voice finetune sweep  configs/sweep/pilot.yaml --resume
+    voice finetune sweep  configs/sweep/example.yaml --resume
 """
 
 from __future__ import annotations

From 1b34ad9a7f20734d3ba42f9ef57947c474ff3228 Mon Sep 17 00:00:00 2001
From: Fin Griffin <fingriffin2023@outlook.com>
Date: Wed, 10 Jun 2026 15:29:28 +0100
Subject: [PATCH 68/68] chore(tests): add unit tests for orchestrator module to
 reach 90% cov

---
 .coverage                                     | Bin 0 -> 53248 bytes
 cspell/project-words.txt                      |   1 +
 tests/voice/datasets/test_split_and_specs.py  |  39 ++
 tests/voice/finetune/test_orchestrator.py     | 340 ++++++++++++++++++
 tests/voice/stylometry/test_sequence_stats.py |  47 +++
 5 files changed, 427 insertions(+)
 create mode 100644 .coverage
 create mode 100644 tests/voice/datasets/test_split_and_specs.py
 create mode 100644 tests/voice/finetune/test_orchestrator.py
 create mode 100644 tests/voice/stylometry/test_sequence_stats.py

diff --git a/.coverage b/.coverage
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diff --git a/cspell/project-words.txt b/cspell/project-words.txt
index e45257a..0a99bde 100644
--- a/cspell/project-words.txt
+++ b/cspell/project-words.txt
@@ -22,3 +22,4 @@ submatrix
 vocab
 finetune
 accum
+nonpositive
diff --git a/tests/voice/datasets/test_split_and_specs.py b/tests/voice/datasets/test_split_and_specs.py
new file mode 100644
index 0000000..4e7439c
--- /dev/null
+++ b/tests/voice/datasets/test_split_and_specs.py
@@ -0,0 +1,39 @@
+"""
+Tests for voice.datasets._schema and voice.datasets._specs.
+
+Scope:
+- Split.parse passthrough for Split instances
+- Split.parse from valid string
+- Split.parse raises TypeError for non-string/non-Split input
+- Split.parse raises ValueError for unrecognised string
+- BUSH_LATEST constant has the expected repo_id
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from voice.datasets._schema import Split
+from voice.datasets._specs import BUSH_LATEST
+
+
+def test_split_parse_passthrough():
+    assert Split.parse(Split.TRAIN) is Split.TRAIN
+
+
+def test_split_parse_from_string():
+    assert Split.parse("validation") is Split.VALIDATION
+
+
+def test_split_parse_raises_type_error_for_non_string():
+    with pytest.raises(TypeError, match="Split or str"):
+        Split.parse(42)  # type: ignore[arg-type]
+
+
+def test_split_parse_raises_value_error_for_invalid_string():
+    with pytest.raises(ValueError, match="Invalid split"):
+        Split.parse("unknown")
+
+
+def test_bush_latest_has_expected_repo_id():
+    assert BUSH_LATEST.repo_id == "AccelerateScience/bush-dataset"
diff --git a/tests/voice/finetune/test_orchestrator.py b/tests/voice/finetune/test_orchestrator.py
new file mode 100644
index 0000000..b8d98a5
--- /dev/null
+++ b/tests/voice/finetune/test_orchestrator.py
@@ -0,0 +1,340 @@
+"""
+Tests for voice.finetune._orchestrator.
+
+Scope:
+- _build_axolotl_cfg merges hyperparams and injects output_dir
+- _now_iso returns a valid ISO-8601 string
+- run_training builds the correct command for single/multi-GPU
+- _load_completions delegates to get_dataset with a LocalDatasetSpec
+- _score_split calls make_comparison and writes alignment JSON
+- _try_score_split skips missing completions silently
+- _try_score_split catches and logs scoring errors
+- _score_all_epochs iterates over all epochs and splits
+- execute_run happy path: creates dirs, writes metadata, scores, returns done
+- execute_run failure path: CalledProcessError -> failed status + RuntimeError
+"""
+
+from __future__ import annotations
+
+import subprocess
+from datetime import datetime
+from pathlib import Path
+from unittest.mock import MagicMock, patch
+
+import pytest
+
+from voice.datasets._schema import Split
+from voice.datasets.dataset import DatasetSpec, LocalDatasetSpec
+from voice.finetune._orchestrator import (
+    _build_axolotl_cfg,
+    _load_completions,
+    _now_iso,
+    _score_all_epochs,
+    _score_split,
+    _try_score_split,
+    execute_run,
+    run_training,
+)
+from voice.finetune._runs import RunDir
+from voice.finetune._schema import RunMetadata
+
+# ---------------------------------------------------------------------------
+# Helpers
+# ---------------------------------------------------------------------------
+
+
+def _run_dir(tmp_path: Path, name: str = "run-001") -> RunDir:
+    return RunDir(tmp_path / name)
+
+
+def _ref_spec() -> DatasetSpec:
+    return DatasetSpec(
+        repo_id="ns/dataset",
+        revision="main",
+        splits=(Split.TRAIN, Split.VALIDATION, Split.TEST),
+    )
+
+
+def _meta() -> RunMetadata:
+    return RunMetadata(
+        run_name="run-001", model="ns/m", dataset="ns/d", hyperparams={}
+    )
+
+
+def _comparison_mock() -> MagicMock:
+    m = MagicMock()
+    m.score = 0.75
+    m.score_ci.return_value = [0.65, 0.85]
+    m.metric_tails = {"avg_word_length": 0.8}
+    m.metric_tail_cis.return_value = {"avg_word_length": [0.7, 0.9]}
+    m.group_tails = {}
+    m.group_tail_cis.return_value = None
+    return m
+
+
+# ---------------------------------------------------------------------------
+# _build_axolotl_cfg
+# ---------------------------------------------------------------------------
+
+
+def test_build_axolotl_cfg_sets_output_dir(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    cfg = _build_axolotl_cfg({"base_model": "ns/m"}, {}, rd)
+    assert cfg["output_dir"] == str(rd.adapter_dir)
+
+
+def test_build_axolotl_cfg_merges_hyperparams(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    hyperparams = {"learning_rate": 1e-4, "lora_r": 8, "lora_alpha": 8}
+    cfg = _build_axolotl_cfg({"base_model": "ns/m"}, hyperparams, rd)
+    assert cfg["learning_rate"] == 1e-4
+    assert cfg["lora_r"] == 8
+    assert cfg["lora_alpha"] == 8
+
+
+def test_build_axolotl_cfg_does_not_mutate_base(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    base = {"base_model": "ns/m"}
+    _build_axolotl_cfg(base, {"learning_rate": 1e-4}, rd)
+    assert "learning_rate" not in base
+    assert "output_dir" not in base
+
+
+# ---------------------------------------------------------------------------
+# _now_iso
+# ---------------------------------------------------------------------------
+
+
+def test_now_iso_returns_valid_utc_iso_string():
+    s = _now_iso()
+    dt = datetime.fromisoformat(s)
+    assert dt.tzinfo is not None
+
+
+# ---------------------------------------------------------------------------
+# run_training
+# ---------------------------------------------------------------------------
+
+
+def test_run_training_single_gpu_uses_python_m(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.write_config({"base_model": "ns/m"})
+
+    with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub:
+        run_training(rd, gpu_ids="0")
+
+    cmd = mock_sub.call_args.args[0]
+    assert "axolotl.cli.train" in cmd
+    assert "accelerate.commands.launch" not in cmd
+
+
+def test_run_training_multi_gpu_uses_accelerate(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.write_config({"base_model": "ns/m"})
+
+    with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub:
+        run_training(rd, gpu_ids="0,1,2")
+
+    cmd = mock_sub.call_args.args[0]
+    assert "accelerate.commands.launch" in cmd
+    assert "--num_processes=3" in cmd
+
+
+def test_run_training_sets_cuda_visible_devices(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    rd.write_config({"base_model": "ns/m"})
+
+    with patch("voice.finetune._orchestrator.subprocess.run") as mock_sub:
+        run_training(rd, gpu_ids="1,2")
+
+    env = mock_sub.call_args.kwargs["env"]
+    assert env["CUDA_VISIBLE_DEVICES"] == "1,2"
+
+
+# ---------------------------------------------------------------------------
+# _load_completions
+# ---------------------------------------------------------------------------
+
+
+def test_load_completions_calls_get_dataset_with_local_spec(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    mock_ds = MagicMock()
+    mock_ds.__getitem__.return_value = []
+
+    with patch(
+        "voice.finetune._orchestrator.get_dataset", return_value=mock_ds
+    ) as mock_get:
+        result = _load_completions(rd, "validation", 1)
+
+    call_spec = mock_get.call_args.args[0]
+    assert isinstance(call_spec, LocalDatasetSpec)
+    assert "epoch_1" in str(call_spec.path)
+    assert result == []
+
+
+# ---------------------------------------------------------------------------
+# _score_split
+# ---------------------------------------------------------------------------
+
+
+def test_score_split_writes_alignment_json_and_returns_score(tmp_path: Path):
+    import json
+
+    rd = _run_dir(tmp_path)
+
+    with (
+        patch(
+            "voice.finetune._orchestrator._load_completions", return_value=[]
+        ),
+        patch(
+            "voice.finetune._orchestrator.make_comparison",
+            return_value=_comparison_mock(),
+        ),
+    ):
+        score = _score_split(rd, "validation", 1, MagicMock())
+
+    assert score == pytest.approx(0.75)
+    out_path = rd.alignment_path("validation", 1)
+    assert out_path.exists()
+    data = json.loads(out_path.read_text())
+    assert data["score"] == pytest.approx(0.75)
+
+
+# ---------------------------------------------------------------------------
+# _try_score_split
+# ---------------------------------------------------------------------------
+
+
+def test_try_score_split_skips_when_completions_missing(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    meta = _meta()
+
+    with patch("voice.finetune._orchestrator._score_split") as mock_score:
+        _try_score_split(rd, "validation", 1, MagicMock(), meta)
+
+    mock_score.assert_not_called()
+    assert meta.alignment_val == {}
+
+
+def test_try_score_split_catches_scoring_error(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    (rd.completions_dir / "epoch_1").mkdir(parents=True)
+    (rd.completions_dir / "epoch_1" / "validation.jsonl").write_text("")
+    meta = _meta()
+
+    with patch(
+        "voice.finetune._orchestrator._score_split",
+        side_effect=RuntimeError("boom"),
+    ):
+        _try_score_split(
+            rd, "validation", 1, MagicMock(), meta
+        )  # must not raise
+
+    assert meta.alignment_val == {}
+
+
+def test_try_score_split_updates_alignment_val(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    (rd.completions_dir / "epoch_1").mkdir(parents=True)
+    (rd.completions_dir / "epoch_1" / "validation.jsonl").write_text("")
+    meta = _meta()
+
+    with patch("voice.finetune._orchestrator._score_split", return_value=0.8):
+        _try_score_split(rd, "validation", 1, MagicMock(), meta)
+
+    assert meta.alignment_val == {"1": 0.8}
+
+
+def test_try_score_split_updates_alignment_test(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    rd.create()
+    (rd.completions_dir / "epoch_1").mkdir(parents=True)
+    (rd.completions_dir / "epoch_1" / "test.jsonl").write_text("")
+    meta = _meta()
+
+    with patch("voice.finetune._orchestrator._score_split", return_value=0.6):
+        _try_score_split(rd, "test", 1, MagicMock(), meta)
+
+    assert meta.alignment_test == {"1": 0.6}
+
+
+# ---------------------------------------------------------------------------
+# _score_all_epochs
+# ---------------------------------------------------------------------------
+
+
+def test_score_all_epochs_calls_try_score_for_each_epoch_and_split(
+    tmp_path: Path,
+):
+    rd = _run_dir(tmp_path)
+    meta = _meta()
+
+    with patch("voice.finetune._orchestrator._try_score_split") as mock_try:
+        _score_all_epochs(
+            rd, num_epochs=2, reference_ds=MagicMock(), meta=meta
+        )
+
+    assert mock_try.call_count == 4  # 2 epochs × 2 splits
+
+
+# ---------------------------------------------------------------------------
+# execute_run
+# ---------------------------------------------------------------------------
+
+
+def test_execute_run_happy_path_returns_done_metadata(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+
+    with (
+        patch("voice.finetune._orchestrator.get_dataset"),
+        patch("voice.finetune._orchestrator.run_training"),
+        patch("voice.finetune._orchestrator._score_all_epochs"),
+    ):
+        meta = execute_run(
+            run_name="run-001",
+            base_axolotl_cfg={
+                "base_model": "ns/model",
+                "datasets": [{"path": "ns/dataset"}],
+                "num_epochs": 2,
+            },
+            hyperparams={"learning_rate": 1e-4, "lora_r": 8},
+            run_dir=rd,
+            reference_spec=_ref_spec(),
+        )
+
+    assert meta.status == "done"
+    assert meta.run_name == "run-001"
+    assert meta.finished_at is not None
+    assert rd.metadata_path.exists()
+
+
+def test_execute_run_failure_sets_failed_status_and_raises(tmp_path: Path):
+    rd = _run_dir(tmp_path)
+    err = subprocess.CalledProcessError(1, "axolotl")
+
+    with (
+        patch("voice.finetune._orchestrator.get_dataset"),
+        patch("voice.finetune._orchestrator.run_training", side_effect=err),
+        pytest.raises(RuntimeError, match="Training failed"),
+    ):
+        execute_run(
+            run_name="run-001",
+            base_axolotl_cfg={
+                "base_model": "ns/model",
+                "datasets": [{"path": "ns/dataset"}],
+                "num_epochs": 1,
+            },
+            hyperparams={},
+            run_dir=rd,
+            reference_spec=_ref_spec(),
+        )
+
+    meta = rd.read_metadata()
+    assert meta.status == "failed"
+    assert meta.finished_at is not None
diff --git a/tests/voice/stylometry/test_sequence_stats.py b/tests/voice/stylometry/test_sequence_stats.py
new file mode 100644
index 0000000..4c76d6b
--- /dev/null
+++ b/tests/voice/stylometry/test_sequence_stats.py
@@ -0,0 +1,47 @@
+"""
+Tests for voice.stylometry._sequence_stats.
+
+Scope:
+- length_central_moment_from_units raises on non-positive order
+- mattr_from_units raises on non-positive window_size or stride
+- mattr_from_units exercises the sliding-window loop (n > window_size)
+"""
+
+from __future__ import annotations
+
+import pytest
+
+from voice.stylometry._sequence_stats import (
+    length_central_moment_from_units,
+    mattr_from_units,
+)
+
+
+def test_length_central_moment_raises_on_nonpositive_order():
+    with pytest.raises(ValueError, match="Order must be positive"):
+        length_central_moment_from_units(["a", "bb"], order=0)
+
+
+def test_mattr_raises_on_nonpositive_window_size():
+    with pytest.raises(ValueError, match="window_size must be positive"):
+        mattr_from_units(["a", "b", "c"], window_size=0, stride=1)
+
+
+def test_mattr_raises_on_nonpositive_stride():
+    with pytest.raises(ValueError, match="stride must be positive"):
+        mattr_from_units(["a", "b", "c"], window_size=2, stride=0)
+
+
+def test_mattr_sliding_window_all_unique():
+    # 7 tokens, window=3 — forces the loop path (n > window_size)
+    # Every window [a,b,c], [b,c,d], ... is fully unique → TTR=1.0 each
+    units = ["a", "b", "c", "d", "e", "f", "g"]
+    result = mattr_from_units(units, window_size=3, stride=1)
+    assert result == pytest.approx(1.0)
+
+
+def test_mattr_sliding_window_with_repeats():
+    # [a,a,b]=2/3, [a,b,a]=2/3, [b,a,a]=2/3 → mean = 2/3
+    units = ["a", "a", "b", "a", "a"]
+    result = mattr_from_units(units, window_size=3, stride=1)
+    assert result == pytest.approx(2 / 3)