diff --git a/gliclass/__init__.py b/gliclass/__init__.py
index 8f9e7e0..ce42569 100644
--- a/gliclass/__init__.py
+++ b/gliclass/__init__.py
@@ -6,7 +6,7 @@
     ZeroShotClassificationWithChunkingPipeline,
 )
 
-__version__ = "0.1.18"
+__version__ = "0.1.19"
 
 # Serve module (optional import)
 try:
diff --git a/gliclass/pipeline.py b/gliclass/pipeline.py
index 6dfdfd5..44743f2 100644
--- a/gliclass/pipeline.py
+++ b/gliclass/pipeline.py
@@ -213,6 +213,42 @@ def __init__(
         # Ensure model is in evaluation mode for inference
         self.model.eval()
 
+    def _normalize_classification_type(self, classification_type: str | None) -> str:
+        if classification_type is None:
+            return self.classification_type
+
+        normalized = classification_type.strip().lower()
+        if normalized in {"single", "single-label", "single_label"}:
+            return "single-label"
+        if normalized in {"multi", "multi-label", "multi_label"}:
+            return "multi-label"
+        raise ValueError("Unsupported classification type: choose 'single-label' or 'multi-label'")
+
+    def _normalize_texts(self, texts: str | List[str]) -> List[str]:
+        if isinstance(texts, str):
+            return [texts]
+        return texts
+
+    def _normalize_thresholds(self, threshold: float | List[float], num_texts: int) -> List[float]:
+        if isinstance(threshold, list):
+            if len(threshold) != num_texts:
+                raise ValueError("Length of threshold list must match number of texts.")
+            return threshold
+        return [threshold] * num_texts
+
+    def _normalize_classification_types(
+        self,
+        classification_type: str | List[str] | None,
+        num_texts: int,
+    ) -> List[str]:
+        if isinstance(classification_type, list):
+            if len(classification_type) != num_texts:
+                raise ValueError("Length of classification_type list must match number of texts.")
+            return [self._normalize_classification_type(item) for item in classification_type]
+
+        normalized = self._normalize_classification_type(classification_type)
+        return [normalized] * num_texts
+
     def _process_labels(
         self, labels: List[str] | Dict[str, Any] | List[List[str]] | List[Dict[str, Any]]
     ) -> List[str] | List[List[str]]:
@@ -244,10 +280,29 @@ def _process_labels(
 
     def _format_examples_for_input(self, examples: List[Dict[str, Any]] | None = None) -> str:
         """Format few-shot examples using <<EXAMPLE>> and <<SEP>> tokens."""
+        if not examples:
+            return ""
+        examples = [example for example in examples if example is not None]
         if not examples:
             return ""
         return format_examples_prompt(examples, example_token=self.example_token, sep_token=self.sep_token)
 
+    def _examples_are_per_text(self, examples) -> bool:
+        """Detect whether examples are provided per text rather than shared."""
+        if not isinstance(examples, list) or len(examples) == 0:
+            return False
+        if all(isinstance(example, dict) for example in examples):
+            return False
+        return all(example is None or isinstance(example, list) for example in examples)
+
+    def _get_text_examples(self, examples, index: int):
+        """Get examples for a single text from shared or per-text input."""
+        if not examples:
+            return None
+        if self._examples_are_per_text(examples):
+            return examples[index] if index < len(examples) else None
+        return examples
+
     def _format_prompt(self, prompt: str | List[str] | None = None, index: int = 0) -> str:
         """Format the task description prompt."""
         if prompt is None:
@@ -278,7 +333,7 @@ def _get_batch_examples(self, examples, start_idx, batch_size):
         """Get examples for current batch."""
         if not examples:
             return None
-        if isinstance(examples[0], list):
+        if self._examples_are_per_text(examples):
             return examples[start_idx : start_idx + batch_size]
         return examples
 
@@ -343,8 +398,9 @@ def __call__(
         self,
         texts: str | List[str],
         labels: List[str] | Dict[str, Any] | List[List[str]] | List[Dict[str, Any]],
-        threshold: float = 0.5,
+        threshold: float | List[float] = 0.5,
         batch_size: int = 8,
+        classification_type: str | List[str] | None = None,
         rac_examples: List | None = None,
         examples: List[Dict[str, Any]] | None = None,
         prompt: str | List[str] | None = None,
@@ -356,8 +412,11 @@ def __call__(
         Args:
             texts: Single text or list of texts to classify
             labels: Labels in various formats (flat list or hierarchical dict)
-            threshold: Classification threshold for multi-label (default: 0.5)
+            threshold: Classification threshold for multi-label, either one
+                value for all texts or one value per text
             batch_size: Batch size for processing
+            classification_type: Override classification mode globally or per text.
+                If None, uses the pipeline's configured classification_type
             rac_examples: Retrieval augmented examples (legacy)
             examples: Few-shot examples with 'text' and 'labels'/'true_labels' keys
             prompt: Task description - string (same for all) or list (per-text)
@@ -368,12 +427,15 @@ def __call__(
         """
         original_labels = labels
 
-        if isinstance(texts, str):
-            if rac_examples:
-                texts = retrieval_augmented_text(texts, rac_examples)
-            texts = [texts]
-        elif rac_examples:
-            texts = [retrieval_augmented_text(text, ex) for text, ex in zip(texts, rac_examples)]
+        texts = self._normalize_texts(texts)
+        thresholds = self._normalize_thresholds(threshold, len(texts))
+        classification_types = self._normalize_classification_types(classification_type, len(texts))
+
+        if rac_examples:
+            if len(texts) == 1 and not isinstance(rac_examples[0], list):
+                texts = [retrieval_augmented_text(texts[0], rac_examples)]
+            else:
+                texts = [retrieval_augmented_text(text, ex) for text, ex in zip(texts, rac_examples)]
 
         processed_labels = self._process_labels(labels)
 
@@ -405,14 +467,20 @@ def __call__(
             max_num_classes = self._resolve_max_num_classes(batch_labels, same_labels)
             model_output = self.model(**tokenized_inputs, max_num_classes=max_num_classes)
             logits = model_output.logits
+            probs = torch.sigmoid(logits)
 
-            if self.classification_type == "single-label":
-                for i in range(len(batch_texts)):
-                    score = torch.softmax(logits[i], dim=-1)
-                    if same_labels:
-                        curr_labels = batch_labels
-                    else:
-                        curr_labels = batch_labels[i]
+            for i in range(len(batch_texts)):
+                global_idx = idx + i
+                item_classification_type = classification_types[global_idx]
+                item_threshold = thresholds[global_idx]
+
+                if same_labels:
+                    curr_labels = batch_labels
+                else:
+                    curr_labels = batch_labels[i]
+
+                if item_classification_type == "single-label":
+                    score = torch.softmax(logits[i][: len(curr_labels)], dim=-1)
 
                     if return_hierarchical:
                         all_scores = {curr_labels[j]: score[j].item() for j in range(len(curr_labels))}
@@ -420,16 +488,8 @@ def __call__(
 
                     pred_label = curr_labels[torch.argmax(score).item()]
                     results.append([{"label": pred_label, "score": score.max().item()}])
-
-            elif self.classification_type == "multi-label":
-                sigmoid = torch.nn.Sigmoid()
-                probs = sigmoid(logits)
-                for i in range(len(batch_texts)):
+                elif item_classification_type == "multi-label":
                     text_results = []
-                    if same_labels:
-                        curr_labels = batch_labels
-                    else:
-                        curr_labels = batch_labels[i]
 
                     if return_hierarchical:
                         all_scores = {curr_labels[j]: probs[i][j].item() for j in range(len(curr_labels))}
@@ -437,11 +497,11 @@ def __call__(
 
                     for j, prob in enumerate(probs[i][: len(curr_labels)]):
                         score = prob.item()
-                        if score >= threshold:
+                        if score >= item_threshold:
                             text_results.append({"label": curr_labels[j], "score": score})
                     results.append(text_results)
-            else:
-                raise ValueError("Unsupported classification type: choose 'single-label' or 'multi-label'")
+                else:
+                    raise ValueError("Unsupported classification type: choose 'single-label' or 'multi-label'")
 
         if return_hierarchical:
             hierarchical_results = []
@@ -507,24 +567,12 @@ def prepare_inputs(self, texts, labels, same_labels=False, examples=None, prompt
 
         if same_labels:
             for i, text in enumerate(texts):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
-
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 inputs.append(self.prepare_input(text, labels, text_examples, text_prompt))
         else:
             for i, (text, labels_) in enumerate(zip(texts, labels)):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
-
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 inputs.append(self.prepare_input(text, labels_, text_examples, text_prompt))
 
@@ -572,24 +620,14 @@ def prepare_inputs(self, texts, labels, same_labels=False, examples=None, prompt
 
         if same_labels:
             for i, text in enumerate(texts):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 prompts.append(self.prepare_labels_prompt(labels, text_prompt))
                 examples_str = self._format_examples_for_input(text_examples) if text_examples else ""
                 processed_texts.append(text + examples_str)
         else:
             for i, labels_ in enumerate(labels):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 prompts.append(self.prepare_labels_prompt(labels_, text_prompt))
                 examples_str = self._format_examples_for_input(text_examples) if text_examples else ""
@@ -651,22 +689,12 @@ def prepare_inputs(self, texts, labels, same_labels=False, examples=None, prompt
             inputs = []
             if same_labels:
                 for i, text in enumerate(texts):
-                    text_examples = None
-                    if examples:
-                        if isinstance(examples[0], list):
-                            text_examples = examples[i] if i < len(examples) else None
-                        else:
-                            text_examples = examples
+                    text_examples = self._get_text_examples(examples, i)
                     text_prompt = self._format_prompt(prompt, i)
                     inputs.append(self.prepare_input(text, labels, text_examples, text_prompt))
             else:
                 for i, (text, labels_) in enumerate(zip(texts, labels)):
-                    text_examples = None
-                    if examples:
-                        if isinstance(examples[0], list):
-                            text_examples = examples[i] if i < len(examples) else None
-                        else:
-                            text_examples = examples
+                    text_examples = self._get_text_examples(examples, i)
                     text_prompt = self._format_prompt(prompt, i)
                     inputs.append(self.prepare_input(text, labels_, text_examples, text_prompt))
         else:
@@ -859,8 +887,9 @@ def __call__(
         self,
         texts: str | List[str],
         labels: List[str] | Dict[str, Any] | List[List[str]] | List[Dict[str, Any]],
-        threshold: float = 0.5,
+        threshold: float | List[float] = 0.5,
         batch_size: int = 8,
+        classification_type: str | List[str] | None = None,
         rac_examples: List | None = None,
         examples: List[Dict[str, Any]] | None = None,
         prompt: str | List[str] | None = None,
@@ -875,8 +904,11 @@ def __call__(
                 Examples:
                 - ["positive", "negative"] - flat labels
                 - {"sentiment": ["positive", "negative"], "topic": ["product", "service"]}
-            threshold: Classification threshold for multi-label (default: 0.5)
+            threshold: Classification threshold for multi-label, either one
+                value for all texts or one value per text
             batch_size: Batch size for processing
+            classification_type: Override classification mode globally or per text.
+                If None, uses the pipeline's configured classification_type
             rac_examples: Retrieval augmented examples (legacy)
             examples: Few-shot examples, each with 'text' and 'labels' keys
             prompt: Task description - string or list of strings (per-text)
@@ -890,6 +922,7 @@ def __call__(
             labels,
             threshold=threshold,
             batch_size=batch_size,
+            classification_type=classification_type,
             rac_examples=rac_examples,
             examples=examples,
             prompt=prompt,
@@ -980,22 +1013,12 @@ def prepare_inputs(self, texts, labels, same_labels=False, examples=None, prompt
 
         if same_labels:
             for i, text in enumerate(texts):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 inputs.append(self.prepare_input(text, labels, text_examples, text_prompt))
         else:
             for i, (text, labels_) in enumerate(zip(texts, labels)):
-                text_examples = None
-                if examples:
-                    if isinstance(examples[0], list):
-                        text_examples = examples[i] if i < len(examples) else None
-                    else:
-                        text_examples = examples
+                text_examples = self._get_text_examples(examples, i)
                 text_prompt = self._format_prompt(prompt, i)
                 inputs.append(self.prepare_input(text, labels_, text_examples, text_prompt))
 
diff --git a/gliclass/serve/__init__.py b/gliclass/serve/__init__.py
index 36bcb35..8728f9c 100644
--- a/gliclass/serve/__init__.py
+++ b/gliclass/serve/__init__.py
@@ -4,7 +4,6 @@
 from .config import GLiClassServeConfig
 from .memory import GLiClassMemoryEstimator
 from .server import GLiClassServer, GLiClassFactory, shutdown, serve_gliclass
-from .server_model import GLiClassServerModel
 
 __all__ = [
     "GLiClassClient",
@@ -12,7 +11,6 @@
     "GLiClassMemoryEstimator",
     "GLiClassServeConfig",
     "GLiClassServer",
-    "GLiClassServerModel",
     "serve_gliclass",
     "shutdown",
 ]
diff --git a/gliclass/serve/server.py b/gliclass/serve/server.py
index fbd8d7c..28e12fd 100644
--- a/gliclass/serve/server.py
+++ b/gliclass/serve/server.py
@@ -6,10 +6,13 @@
 
 import torch
 from ray import serve
+from transformers import AutoTokenizer
+
+from gliclass.model import GLiClassModel
+from gliclass.pipeline import ZeroShotClassificationPipeline
 
 from .config import GLiClassServeConfig
 from .memory import GLiClassMemoryEstimator
-from .server_model import GLiClassServerModel
 
 logger = logging.getLogger("ray.serve")
 
@@ -56,13 +59,23 @@ def __init__(self, config: GLiClassServeConfig):
 
         logger.info("Loading model: %s", config.model)
 
-        self.model = GLiClassServerModel(
-            model_name=config.model,
-            device=self.device,
-            dtype=self.torch_dtype,
-            max_length=config.max_model_len,
-            max_classes=config.max_labels if config.max_labels > 0 else 100,
-            max_labels_alloc=config.max_labels_alloc,
+        self.model = GLiClassModel.from_pretrained(config.model)
+        self.model.config.max_labels_alloc = config.max_labels_alloc
+        self.model.to(device=self.device, dtype=self.torch_dtype)
+        self.model.eval()
+
+        self.tokenizer = AutoTokenizer.from_pretrained(config.model)
+        pipeline_kwargs = {
+            "model": self.model,
+            "tokenizer": self.tokenizer,
+            "max_classes": config.max_labels if config.max_labels > 0 else 100,
+            "max_length": config.max_model_len,
+            "device": self.device,
+            "progress_bar": False,
+        }
+        self.pipeline = ZeroShotClassificationPipeline(
+            classification_type="multi-label",
+            **pipeline_kwargs,
         )
 
         if torch.cuda.is_available():
@@ -79,8 +92,8 @@ def __init__(self, config: GLiClassServeConfig):
     def _precompile(self) -> None:
         logger.info("Precompiling model for batch sizes: %s", self.config.precompiled_batch_sizes)
 
-        if hasattr(self.model.model, "compile"):
-            self.model.model.compile()
+        if hasattr(self.model, "compile"):
+            self.model.compile()
 
         dummy_labels = ["person", "organization", "location"]
 
@@ -136,7 +149,7 @@ def batch_size_fn(self, seq_len: int | None = None) -> int:
     def observed_seq_len(
         self,
         texts: list[str],
-        labels: list[str] | None = None,
+        labels: list[str] | list[list[str]] | None = None,
     ) -> int:
         """Total input word count: longest text + all label words.
 
@@ -153,7 +166,10 @@ def observed_seq_len(
         max_text_words = max((len(t.split()) for t in texts if t.strip()), default=0)
         prompt_words = 0
         if labels:
-            prompt_words += sum(len(label.split()) for label in labels)
+            if isinstance(labels[0], list):
+                prompt_words += max(sum(len(label.split()) for label in label_set) for label_set in labels)
+            else:
+                prompt_words += sum(len(label.split()) for label in labels)
         total = max_text_words + prompt_words
         return min(max(total, self.config.calibration_min_seq_len), self.config.max_model_len)
 
@@ -167,34 +183,39 @@ def _filter_labels(self, labels: list[str]) -> list[str]:
     def _run_batch_internal(
         self,
         texts: list[str],
-        labels: list[str],
-        threshold: float = 0.5,
-        multi_label: bool = True,
-        examples: list[dict[str, Any]] | None = None,
+        labels: list[str] | list[list[str]],
+        threshold: float | list[float] = 0.5,
+        multi_label: bool | list[bool] = True,
+        examples: list[dict[str, Any]] | list[list[dict[str, Any]] | None] | None = None,
         prompt: str | list[str] | None = None,
-    ) -> list[dict[str, Any]]:
-        """Run batch inference using low-level methods.
-
-        This is the core inference method that uses prepare_batch, tokenize_batch,
-        collate_batch, run_batch, and decode_batch directly.
+    ) -> list[list[dict[str, Any]]]:
+        """Run batch inference using the shared zero-shot pipeline.
 
         Args:
             texts: List of input texts
-            labels: Label list (same for all texts)
-            threshold: Classification threshold
-            multi_label: Multi-label classification
-            examples: Few-shot examples
-            prompt: Task description
+            labels: Shared label list or one label list per text
+            threshold: Shared threshold or one threshold per text
+            multi_label: Shared mode or one mode flag per text
+            examples: Shared examples or one example set per text
+            prompt: Shared prompt or one prompt per text
 
         Returns:
             List of prediction dicts
         """
-        prepared = self.model.prepare_batch(texts, labels, examples, prompt)
-        tokenized = self.model.tokenize_batch(prepared)
-        batch = self.model.collate_batch(tokenized)
-        outputs = self.model.run_batch(batch)
-        results = self.model.decode_batch(outputs, threshold, multi_label)
-        return results
+        if isinstance(multi_label, list):
+            classification_type = ["multi-label" if item else "single-label" for item in multi_label]
+        else:
+            classification_type = "multi-label" if multi_label else "single-label"
+
+        return self.pipeline(
+            texts,
+            labels,
+            threshold=threshold,
+            batch_size=max(len(texts), 1),
+            classification_type=classification_type,
+            examples=examples,
+            prompt=prompt,
+        )
 
     def predict(
         self,
@@ -204,7 +225,7 @@ def predict(
         multi_label: bool = True,
         examples: list[dict[str, Any]] | None = None,
         prompt: str | list[str] | None = None,
-    ) -> list[dict[str, Any]]:
+    ) -> list[list[dict[str, Any]]]:
         if isinstance(texts, str):
             texts = [texts]
 
@@ -267,33 +288,27 @@ async def _infer_batch(
             using batch_size_fn on the observed seq length — so the next
             accumulation picks the largest precompiled size that fits.
 
-            Assumes batch requests are homogeneous — labels/thresholds/flags
-            are taken from the first request.
+            Supports heterogeneous request parameters by passing per-text
+            thresholds, classification types, labels, examples, and prompts
+            through to the shared pipeline.
             """
             # Dynamically adjust batch size based on observed sequence length
             next_max_batch = self.server.batch_size_fn(
                 seq_len=self.server.observed_seq_len(
                     texts,
-                    labels=labels_list[0] if labels_list else None,
+                    labels=labels_list,
                 )
             )
             self._infer_batch.set_max_batch_size(next_max_batch)
 
-            # Use first request's parameters (assume homogeneous)
-            labels = labels_list[0]
-            threshold = thresholds[0]
-            multi_label = multi_label_list[0]
-            examples = examples_list[0]
-            prompt = prompts_list[0]
-
             # Process entire batch at once
             results = self.server._run_batch_internal(
                 texts,
-                labels,
-                threshold=threshold,
-                multi_label=multi_label,
-                examples=examples,
-                prompt=prompt,
+                labels_list,
+                threshold=thresholds,
+                multi_label=multi_label_list,
+                examples=examples_list,
+                prompt=prompts_list,
             )
 
             return results
diff --git a/gliclass/serve/server_model.py b/gliclass/serve/server_model.py
deleted file mode 100644
index b93955c..0000000
--- a/gliclass/serve/server_model.py
+++ /dev/null
@@ -1,336 +0,0 @@
-"""Server-optimized model wrapper for GLiClass with low-level batch processing."""
-
-import logging
-from typing import Any
-
-import torch
-from transformers import AutoTokenizer
-
-from gliclass.model import GLiClassModel
-from gliclass.pipeline import format_examples_prompt
-
-logger = logging.getLogger(__name__)
-
-
-class GLiClassServerModel:
-    """Server-optimized wrapper combining pipeline logic and model.
-
-    Splits inference into low-level stages for CPU/GPU parallelism:
-    - prepare_batch (CPU): text formatting, label flattening
-    - tokenize_batch (CPU): tokenization, input creation
-    - collate_batch (CPU): tensor creation, padding
-    - run_batch (GPU): forward pass, encoding
-    - decode_batch (CPU/GPU): sigmoid, threshold filtering, result formatting
-
-    Avoids DataLoader overhead by keeping tokenizers/collators alive
-    and calling model methods directly.
-    """
-
-    def __init__(
-        self,
-        model_name: str,
-        device: torch.device,
-        dtype: torch.dtype,
-        max_length: int = 2048,
-        max_classes: int = 25,
-        max_labels_alloc: str | int = "dynamic",  # "dynamic" | int | "fixed"
-    ):
-        self.device = device
-        self.dtype = dtype
-        self.max_length = max_length
-        self.max_classes = max_classes
-        self.max_labels_alloc = max_labels_alloc
-
-        logger.info(f"Loading model: {model_name}")
-        self.model = GLiClassModel.from_pretrained(model_name)
-        self.model.config.max_labels_alloc = max_labels_alloc
-        self.model.to(device=device, dtype=dtype)
-        self.model.eval()
-
-        self.architecture_type = self.model.config.architecture_type
-        self.prompt_first = getattr(self.model.config, "prompt_first", False)
-
-        logger.info(f"Loading tokenizers for {self.architecture_type}")
-        # Use model_name, not encoder_model_name, to get tokenizer with special tokens
-        self.tokenizer = AutoTokenizer.from_pretrained(model_name)
-
-        if self.architecture_type in {"bi-encoder", "bi-encoder-fused"}:
-            # For bi-encoder, load labels tokenizer from model (not label_model_name)
-            # to ensure special tokens are available
-            self.labels_tokenizer = AutoTokenizer.from_pretrained(model_name)
-        else:
-            self.labels_tokenizer = None
-
-        self.label_token = "<<LABEL>>"
-        self.sep_token = "<<SEP>>"
-        self.example_token = "<<EXAMPLE>>"
-
-    def _resolve_max_num_classes(self, labels: list[str] | list[list[str]], same_labels: bool) -> int | None:
-        if self.max_labels_alloc == "dynamic":
-            return len(labels) if same_labels else max(len(lbl) for lbl in labels)
-        if isinstance(self.max_labels_alloc, int):
-            return self.max_labels_alloc
-        return None  # 'fixed': model uses config.max_num_classes
-
-    def prepare_batch(
-        self,
-        texts: list[str],
-        labels: list[str] | list[list[str]],
-        examples: list[dict[str, Any]] | None = None,
-        prompt: str | list[str] | None = None,
-    ) -> dict[str, Any]:
-        same_labels = isinstance(labels[0], str) if labels else True
-
-        prepared = {
-            "texts": texts,
-            "labels": labels,
-            "same_labels": same_labels,
-            "examples_str": None,
-        }
-
-        if examples:
-            prepared["examples_str"] = format_examples_prompt(
-                examples,
-                example_token=self.example_token,
-                sep_token=self.sep_token,
-            )
-
-        if prompt is not None:
-            if isinstance(prompt, str):
-                prepared["prompts"] = [prompt] * len(texts)
-            else:
-                prepared["prompts"] = prompt
-        else:
-            prepared["prompts"] = [None] * len(texts)
-
-        return prepared
-
-    def _format_input_bi_encoder_fused(
-        self,
-        text: str,
-        labels: list[str],
-        examples_str: str | None,
-        prompt: str | None,
-    ) -> str:
-        input_parts = []
-
-        if prompt:
-            input_parts.append(prompt)
-            input_parts.append(" ")
-
-        for _label in labels:
-            input_parts.append(self.label_token)
-        input_parts.append(self.sep_token)
-
-        examples_str = examples_str or ""
-
-        if self.prompt_first:
-            return "".join(input_parts) + text + examples_str
-        else:
-            return text + "".join(input_parts) + examples_str
-
-    def _format_input_bi_encoder(
-        self,
-        text: str,
-        prompt: str | None,
-    ) -> str:
-        if prompt:
-            return f"{prompt} {text}"
-        return text
-
-    def _format_input_uni_encoder(
-        self,
-        text: str,
-        labels: list[str],
-        examples_str: str | None,
-        prompt: str | None,
-    ) -> str:
-        """Match format from pipeline.prepare_input."""
-        input_parts = []
-
-        # Add labels
-        for label in labels:
-            input_parts.append(f"{self.label_token}{label}")
-        input_parts.append(self.sep_token)
-
-        # Add task description prompt
-        if prompt:
-            input_parts.append(prompt)
-
-        # Format examples
-        examples_str = examples_str or ""
-
-        # Use prompt_first to determine order (match pipeline logic)
-        if self.prompt_first:
-            return "".join(input_parts) + text + examples_str
-        else:
-            return text + "".join(input_parts) + examples_str
-
-    def tokenize_batch(self, prepared: dict[str, Any]) -> dict[str, Any]:
-        texts = prepared["texts"]
-        labels = prepared["labels"]
-        same_labels = prepared["same_labels"]
-        examples_str = prepared["examples_str"]
-        prompts = prepared["prompts"]
-
-        if self.architecture_type == "bi-encoder-fused":
-            inputs = []
-            for i, text in enumerate(texts):
-                curr_labels = labels if same_labels else labels[i]
-                inputs.append(self._format_input_bi_encoder_fused(text, curr_labels, examples_str, prompts[i]))
-        elif self.architecture_type == "bi-encoder":
-            inputs = [self._format_input_bi_encoder(text, prompts[i]) for i, text in enumerate(texts)]
-        elif self.architecture_type in {"uni-encoder", "encoder-decoder", "encoder-decoder-cls"}:
-            inputs = []
-            for i, text in enumerate(texts):
-                curr_labels = labels if same_labels else labels[i]
-                inputs.append(self._format_input_uni_encoder(text, curr_labels, examples_str, prompts[i]))
-        else:
-            raise NotImplementedError(f"Architecture {self.architecture_type} not implemented")
-
-        tokenized = self.tokenizer(
-            inputs,
-            truncation=True,
-            max_length=self.max_length,
-            padding="longest",
-            return_tensors="pt",
-        )
-
-        result = {
-            "input_ids": tokenized["input_ids"],
-            "attention_mask": tokenized["attention_mask"],
-            "labels": labels,
-            "same_labels": same_labels,
-            "num_samples": len(texts),
-        }
-
-        if self.labels_tokenizer is not None:
-            if same_labels:
-                tokenized_labels = self.labels_tokenizer(
-                    labels,
-                    truncation=True,
-                    max_length=self.max_length,
-                    padding="longest",
-                    return_tensors="pt",
-                )
-                result["class_input_ids"] = tokenized_labels["input_ids"].unsqueeze(0).expand(len(texts), -1, -1)
-                result["class_attention_mask"] = (
-                    tokenized_labels["attention_mask"].unsqueeze(0).expand(len(texts), -1, -1)
-                )
-                result["labels_mask"] = torch.ones(len(texts), len(labels), dtype=torch.long)
-            else:
-                class_input_ids = []
-                class_attention_mask = []
-                for labels_set in labels:
-                    tokenized_labels = self.labels_tokenizer(
-                        labels_set,
-                        truncation=True,
-                        max_length=self.max_length,
-                        padding="max_length",
-                        return_tensors="pt",
-                    )
-                    class_input_ids.append(tokenized_labels["input_ids"])
-                    class_attention_mask.append(tokenized_labels["attention_mask"])
-                result["class_input_ids"] = torch.stack(class_input_ids)
-                result["class_attention_mask"] = torch.stack(class_attention_mask)
-
-        return result
-
-    def collate_batch(self, tokenized: dict[str, Any]) -> dict[str, Any]:
-        batch = {}
-        for key, value in tokenized.items():
-            if isinstance(value, torch.Tensor):
-                batch[key] = value.to(self.device)
-            else:
-                batch[key] = value
-        return batch
-
-    @torch.inference_mode()
-    def run_batch(self, batch: dict[str, Any]) -> dict[str, Any]:
-        labels = batch["labels"]
-        same_labels = batch["same_labels"]
-        max_num_classes = self._resolve_max_num_classes(labels, same_labels)
-
-        model_inputs = {
-            "input_ids": batch["input_ids"],
-            "attention_mask": batch["attention_mask"],
-            "max_num_classes": max_num_classes,
-        }
-
-        if "class_input_ids" in batch:
-            model_inputs["class_input_ids"] = batch["class_input_ids"]
-            model_inputs["class_attention_mask"] = batch["class_attention_mask"]
-
-        if "labels_mask" in batch:
-            model_inputs["labels_mask"] = batch["labels_mask"]
-
-        outputs = self.model(**model_inputs)
-
-        return {
-            "logits": outputs.logits,
-            "labels": labels,
-            "same_labels": same_labels,
-            "num_samples": batch["num_samples"],
-        }
-
-    def decode_batch(
-        self,
-        outputs: dict[str, Any],
-        threshold: float = 0.5,
-        multi_label: bool = True,
-    ) -> list[dict[str, Any]]:
-        logits = outputs["logits"]
-        labels = outputs["labels"]
-        same_labels = outputs["same_labels"]
-        num_samples = outputs["num_samples"]
-
-        results = []
-
-        if multi_label:
-            sigmoid = torch.nn.Sigmoid()
-            probs = sigmoid(logits)
-            probs_cpu = probs.float().cpu().numpy()
-
-            for i in range(num_samples):
-                curr_labels = labels if same_labels else labels[i]
-                text_results = []
-
-                for j in range(len(curr_labels)):
-                    score = float(probs_cpu[i, j])
-                    if score >= threshold:
-                        text_results.append({"label": curr_labels[j], "score": score})
-
-                results.append(text_results)
-        else:
-            for i in range(num_samples):
-                curr_labels = labels if same_labels else labels[i]
-                scores = torch.softmax(logits[i][: len(curr_labels)], dim=-1)
-                scores_cpu = scores.float().cpu().numpy()  # Convert to float32 for numpy
-                max_idx = int(scores_cpu.argmax())
-
-                results.append(
-                    [
-                        {
-                            "label": curr_labels[max_idx],
-                            "score": float(scores_cpu[max_idx]),
-                        }
-                    ]
-                )
-
-        return results
-
-    def predict(
-        self,
-        texts: list[str],
-        labels: list[str] | list[list[str]],
-        threshold: float = 0.5,
-        multi_label: bool = True,
-        examples: list[dict[str, Any]] | None = None,
-        prompt: str | list[str] | None = None,
-    ) -> list[dict[str, Any]]:
-        prepared = self.prepare_batch(texts, labels, examples, prompt)
-        tokenized = self.tokenize_batch(prepared)
-        batch = self.collate_batch(tokenized)
-        outputs = self.run_batch(batch)
-        results = self.decode_batch(outputs, threshold, multi_label)
-        return results
diff --git a/uv.lock b/uv.lock
new file mode 100644
index 0000000..1589796
--- /dev/null
+++ b/uv.lock
@@ -0,0 +1,3760 @@
+version = 1
+revision = 3
+requires-python = ">=3.10"
+resolution-markers = [
+    "python_full_version >= '3.13'",
+    "python_full_version == '3.12.*'",
+    "python_full_version == '3.11.*'",
+    "python_full_version < '3.11'",
+]
+
+[[package]]
+name = "aiohappyeyeballs"
+version = "2.6.1"
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+]
+
+[[package]]
+name = "aiohttp"
+version = "3.13.5"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+    { name = "aiosignal" },
+    { name = "async-timeout", marker = "python_full_version < '3.11'" },
+    { name = "attrs" },
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+]
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