TinyDoc-VLM
256MApache 2.0CPU-Runnable Document Vision Language Model
ABOUT THIS MODEL
A 256M-parameter vision-language model for document understanding. Handles OCR, layout analysis, table extraction, and visual QA — all without a GPU. Built for regulated industries that process high volumes of documents privately.
KEY HIGHLIGHTS
- ✓Runs entirely on CPU — no GPU costs
- ✓Processes invoices, receipts, forms, and contracts
- ✓698+ downloads on HuggingFace
- ✓Designed for air-gapped and regulated environments
SPECIFICATIONS
Parameters256M
ArchitectureSigLIP + SmolLM2
TasksOCR · Layout · VQA · Table Extraction
HardwareCPU-runnable · No GPU required
FormatHuggingFace Transformers · ONNX
LicenseApache 2.0
Downloads698+
QUICK START
Get started in minutes
Install
pip install transformers torch pillowUsage
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
processor = AutoProcessor.from_pretrained("eulogik/TinyDoc-VLM-256M")
model = AutoModelForVision2Seq.from_pretrained("eulogik/TinyDoc-VLM-256M")
image = Image.open("document.png")
inputs = processor(images=image, text="Extract all text", return_tensors="pt")
output = model.generate(**inputs, max_new_tokens=512)
print(processor.decode(output[0], skip_special_tokens=True))Need help deploying TinyDoc-VLM?
We offer consulting, custom fine-tuning, and on-premise deployment for all our models.