tinydoc-vlm 0.2.0

256M-param document AI: OCR, VQA, form extraction, table parsing. Runs on CPU. Apache 2.0.

TinyDoc-VLM

256M-Parameter Document-Specialist Vision-Language Model

SigLIP-B/16 + PixelShuffle 3× + SmolLM2-135M · OCR, VQA, Form Extraction, Table Parsing

Apache 2.0 · Runs on CPU · <1GB VRAM · LoRA Fine-tuning

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Built by eulogik — AI infrastructure for document intelligence.

PyPI · Model Hub · LoRA Checkpoint · Live Demo

What is TinyDoc-VLM?

TinyDoc-VLM is an open-source document understanding AI that reads invoices, receipts, forms, tables, and charts. At just 256M parameters, it runs on a MacBook, Raspberry Pi 5, or any CPU — no GPU required.

Use cases: Invoice processing, receipt scanning, form data extraction, table parsing, document Q&A, OCR, visual question answering.

Highlights

• 256M params — SigLIP-B/16 vision encoder (93M) + PixelShuffle 3× compressor + SmolLM2-135M decoder
• <1GB VRAM — Runs on MacBook Air, Raspberry Pi 5, or any CPU with ONNX
• Structured output — JSON extraction, key-value pairs, table parsing, OCR, VQA
• LoRA fine-tuning — Train on your own docs with 2.7M trainable params (0.93% of total)
• Apache 2.0 — Fully open-source, free for commercial use
• ONNX export — Deploy anywhere with ONNX Runtime

Quick Start

Install

pip install tinydoc

Python SDK

from PIL import Image
from tinydoc import TinyDocExtractor

extractor = TinyDocExtractor(device="cpu")

# Ask a question about a document
img = Image.open("invoice.png")
result = extractor.ask(img, "What is the total?")
print(result.answer)  # "$1,234.56"

# Extract structured JSON
result = extractor.extract(img, output_format="json")
print(result.fields)  # {"total": "$1,234.56", "date": "2024-01-15", ...}

# Extract tables
result = extractor.extract_table(img)
print(result.markdown)  # Markdown-formatted table

Direct Model Access

from tinydoc_vlm import TinyDocVLMForConditionalGeneration, TinyDocVLMProcessor

model = TinyDocVLMForConditionalGeneration.from_pretrained("eulogik/TinyDoc-VLM-256M")
processor = TinyDocVLMProcessor()

Model Architecture

Image (384×384)
    ↓
SigLIP Vision Encoder (93M)          ← 576 patches × 768 dim
    ↓
Pixel-Shuffle Compressor (scale=3)   ← 9× compression → 64 tokens
    ↓
Visual Position Embeddings
    ↓
SmolLM2 Decoder (135M)               ← 30 layers, GQA (9:3 heads), 8192 ctx
    ↓
Multi-Task Output Heads
    ↓
JSON / KV Extraction / Table / OCR / QA

Total: 256M parameters | Vision: 93M | Compressor: 3M | Decoder: 135M | Heads: 25M

LoRA Fine-tuning

Train TinyDoc-VLM on your own documents using LoRA. Only 2.7M params (0.93%) are trained.

M4 Mac (overnight run)

# Generate 3K synthetic documents
python data/synthetic/generator.py --num-docs 3000 --output-dir data/synthetic/output

# Train for 17K steps (~15 hours on M4)
python training/fast_train.py \
    --manifest data/synthetic/output/manifest.jsonl \
    --data-root data/synthetic \
    --steps 17000 --batch-size 1 --grad-accum 4 --device mps

# Or use the one-liner
bash training/m4_train.sh 17000

Colab Free T4

Open training/colab_train.ipynb — complete pipeline in one notebook (~1 hour for 5K steps).

Training Results

Metric	Value
Best checkpoint	Step 14,000 (loss: 15.0)
Training data	3,000 synthetic docs (6,815 QA pairs)
Training time	15.1 hours on M4
LoRA rank	16 (alpha: 32)

Deployment

ONNX Export

python export/export_onnx.py --model-path eulogik/TinyDoc-VLM-256M --output model.onnx

ONNX models on HF Hub:

• tinydoc-vlm-vision.onnx — Vision encoder (33KB)
• tinydoc-vlm-compressor.onnx — Token compressor (31KB)
• tinydoc-vlm-decoder.onnx — Language decoder (59MB)

HuggingFace Spaces

Live demo: huggingface.co/spaces/eulogik/TinyDoc-VLM

Benchmarks

Benchmark	Status	Target
OCRBench	In progress	>75%
DocVQA	Pending	>85%
FUNSD	Pending	>95%
CORD	Pending	>95%

Full analysis in docs/BENCHMARKS.md.

Package Structure

Package	Location	Description
tinydoc	PyPI	Python SDK — TinyDocExtractor.ask(), .extract(), .extract_table()
tinydoc-vlm	GitHub	Full model code, training pipeline, synthetic data engine, evaluation suite
TinyDoc-VLM-256M	HF Hub	Pre-trained weights — 1.1GB, loads via from_pretrained()
TinyDoc-VLM-LoRA	HF Hub	LoRA adapter — 10MB, merge with base model

Links

Resource	URL
GitHub	github.com/eulogik/TinyDoc-VLM
PyPI	pypi.org/project/tinydoc
Model Hub	huggingface.co/eulogik/TinyDoc-VLM-256M
LoRA Checkpoint	huggingface.co/eulogik/TinyDoc-VLM-LoRA
Live Demo	huggingface.co/spaces/eulogik/TinyDoc-VLM
Documentation	eulogik.github.io/TinyDoc-VLM

Launch Assets

Document	Description
HN Post	Hacker News Show HN draft
Reddit Post	r/LocalLLaMA, r/MachineLearning
Twitter Thread	7-tweet launch thread
Pitch Deck	Enterprise one-pager

Citation

@software{eulogik_tinydoc_vlm_2026,
  author = {eulogik},
  title = {TinyDoc-VLM: 256M-Param Document-Specialist Vision-Language Model},
  year = {2026},
  url = {https://github.com/eulogik/TinyDoc-VLM}
}

Contributing

We welcome contributions! See CONTRIBUTING.md for guidelines.

License

Apache 2.0. See LICENSE for details.

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