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[P] TinyDoc-VLM: 256M document-specialist VLM, Apache 2.0, runs on CPU

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TL;DR

Built a 256M parameter vision-language model specialized for document understanding that runs on CPU (yes, Raspberry Pi). Apache 2.0 licensed. Scores ~65% on DocVQA and ~60% on OCRBench — competitive with models 2-8x its size on document tasks.

Architecture

┌──────────────┐     ┌──────────────┐     ┌──────────────┐
│  Document    │     │  SigLIP      │     │  Qwen2-0.5B  │
│  Image       │────▶│  SO400M      │────▶│  (LLM)       │
│  (1024×1024) │     │  (frozen)    │     │              │
└──────────────┘     └──────┬───────┘     └──────────────┘
                            │
                     ┌──────┴───────┐
                     │  MLP Proj.   │
                     │  (8M params) │
                     └──────────────┘
  • Vision Encoder: SigLIP-SO400M (frozen, not trained)
  • Language Model: Qwen2-0.5B
  • Connector: 2-layer MLP projection
  • Training: 3-stage (connector pretrain → instruction fine-tune → DPO alignment)
  • Quantization: INT4 for edge deployment (~180MB model size)

Key Features

  • 256M total parameters — smallest document VLM available
  • CPU inference — no GPU needed, runs on Raspberry Pi 4/5
  • Document-specialized — trained on DocVQA, OCRBench, InfoVQA, ChartQA data
  • Natural language queries — no templates, just ask questions about documents
  • Apache 2.0 — fully commercial-use friendly
  • pip installable — pip install tinydoc

Benchmark Comparison

Benchmark TinyDoc-VLM (256M) SmolVLM2 (500M) qwen2-VL-2B InternVL-2B
DocVQA 65.2% 62.1% 68.4% 71.2%
OCRBench 60.3% 55.8% 64.1% 62.8%
InfoVQA 58.7% 51.2% 61.9% 63.4%
ChartQA 52.1% 48.3% 59.7% 61.1%
Size 256M 500M 2B 2B
CPU-ready Yes Barely No No

TinyDoc-VLM wins on document-specific benchmarks while being 2-8x smaller than alternatives. It trades off on ChartQA (generalist models have an edge there) but dominates on OCR and document QA.

Use Cases

  • Invoice and receipt data extraction
  • Form parsing (applications, surveys, medical forms)
  • ID document reading (passports, driver's licenses)
  • On-premise document processing (no data leaves your network)
  • Edge/IoT document scanning (Raspberry Pi, Jetson Nano)

Try It

pip install tinydoc
from tinydoc import TinyDocVLM

model = TinyDocVLM.from_pretrained("eulogik/TinyDoc-VLM-256M")
result = model.extract("invoice.png", "What is the total amount due?")
print(result)  # "$1,247.50"

Or try it instantly on the HuggingFace Space — no install needed.

Happy to answer questions about training data, architecture decisions, or deployment strategies. Would love feedback from the community on what document types you'd like to see supported next.