Instructions to use Vishva007/LightOnOCR-3-4B-W4A16-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Vishva007/LightOnOCR-3-4B-W4A16-AutoRound with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="Vishva007/LightOnOCR-3-4B-W4A16-AutoRound")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("Vishva007/LightOnOCR-3-4B-W4A16-AutoRound")
model = AutoModelForMultimodalLM.from_pretrained("Vishva007/LightOnOCR-3-4B-W4A16-AutoRound", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Vishva007/LightOnOCR-3-4B-W4A16-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/Vishva007/LightOnOCR-3-4B-W4A16-AutoRound

SGLang

How to use Vishva007/LightOnOCR-3-4B-W4A16-AutoRound with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use Vishva007/LightOnOCR-3-4B-W4A16-AutoRound with Docker Model Runner:
```
docker model run hf.co/Vishva007/LightOnOCR-3-4B-W4A16-AutoRound
```

LightOnOCR-3-4B — W4A16 Quantized (AutoRound)

This repository provides an INT4 quantized version of lightonai/LightOnOCR-3-4B using Intel AutoRound with high-fidelity production settings.

LightOnOCR-3-4B is an end-to-end vision-language model based on the Qwen3.5 architecture tailored for high-accuracy document transcription, layout understanding, visual grounding, and structured figure extraction.

Quantization Details

Parameter	Configuration	Details
Quantization Scheme	`W4A16`	Weights INT4, Activations BF16
Group Size	`32`	Enhanced accuracy per weight block
Symmetry	`Symmetric` (`sym=True`)	Standard high-throughput configuration
Iterations	`1200`	Production-grade fine-grained optimization
Calibration Samples	`512` samples (`seqlen=4096`)	Broad domain calibration coverage
Vision Preservation	`quant_nontext_module=False`	Vision tower & multimodal projector kept in BF16 to prevent OCR/grounding degradation

Sibling Repositories & Formats

Choose the backend format suited to your inference engine:

AutoRound Native / Transformers: Vishva007/LightOnOCR-3-4B-W4A16-AutoRound
AutoGPTQ / Marlin: Vishva007/LightOnOCR-3-4B-W4A16-AutoRound-GPTQ
vLLM / Compressed Tensors: Vishva007/LightOnOCR-3-4B-W4A16-AutoRound-LLM-Compressor

Operating Modes

LightOnOCR-3 operates in two primary modes:

Plain Transcription Mode (prompt = ""): Transcribes text directly to Markdown layout without bounding boxes.
Grounding Mode (prompt = "grounding"): Outputs normalized bounding boxes [0, 1000] and semantic labels (text, title, table, image, chart, etc.) for each document component.

Quickstart

1. Python Inference via Transformers

pip install --upgrade "transformers>=5.5.4" auto-round pillow torchvision

import torch
from transformers import AutoProcessor, AutoModelForImageTextToText

# Swap repo_id with your target variant:
model_id = "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound"

processor = AutoProcessor.from_pretrained(model_id)
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)

# Set image URL or local path
image_url = "[https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg](https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg)"

# Use "grounding" or "" (for transcription only)
prompt = "grounding"

content = [{"type": "image", "url": image_url}]
if prompt:
    content.append({"type": "text", "text": prompt})

conversation = [{"role": "user", "content": content}]

inputs = processor.apply_chat_template(
    conversation,
    add_generation_prompt=True,
    tokenize=True,
    return_dict=True,
    return_tensors="pt",
    enable_thinking=False,  # LightOnOCR is optimized with thinking disabled
).to(model.device)

with torch.inference_mode():
    output_ids = model.generate(**inputs, max_new_tokens=2048, do_sample=False)

generated_ids = output_ids[0, inputs["input_ids"].shape[1]:]
print(processor.decode(generated_ids, skip_special_tokens=True))

2. High-Throughput Serving with vLLM

(Recommended using Vishva007/LightOnOCR-3-4B-W4A16-AutoRound-LLM-Compressor or Vishva007/LightOnOCR-3-4B-W4A16-AutoRound-GPTQ)

Start Server

vllm serve Vishva007/Vishva007/LightOnOCR-3-4B-W4A16-AutoRound \
  --trust-remote-code \
  --dtype bfloat16 \
  --max-model-len 8192 \
  --default-chat-template-kwargs '{"enable_thinking": false}'

Query Server

import requests

url = "http://localhost:8000/v1/chat/completions"
image_url = "[https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg](https://huggingface.co/datasets/hf-internal-testing/fixtures_ocr/resolve/main/SROIE-receipt.jpeg)"

payload = {
    "model": "Vishva007/LightOnOCR-3-4B-W4A16-AutoRound",
    "messages": [
        {
            "role": "user",
            "content": [
                {"type": "image_url", "image_url": {"url": image_url}},
                {"type": "text", "text": "grounding"}
            ]
        }
    ],
    "max_tokens": 2048,
    "temperature": 0.0
}

response = requests.post(url, json=payload)
print(response.json()["choices"][0]["message"]["content"])

Best Practices for Document Preprocessing

Resolution: Render scanned documents and PDFs at 400 DPI using a target longest dimension of 2048px.
Aspect Ratio: Maintain the native aspect ratio when resizing; do not stretch images, as bounding box coordinate accuracy depends on geometric integrity.
Thinking Tokens: Ensure enable_thinking=False is preserved in chat templates during generation.

🚀 Deploy on RunPod

One-click launch environments pre-configured with PyTorch, CUDA, and dependencies for fine-tuning or quantization.

🎁 Need GPU compute? Sign up via RunPod and get $5–$500 in free credits when you add your first $10.

PyTorch 2.14

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.14 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.14-runpod`	`d7lxsa4w9m`
PyTorch 2.14 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.14-runpod`	`yk0y6j6rpg`
PyTorch 2.14 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.14-runpod`	`gsp4gwx0nw`

PyTorch 2.13

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.13 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.13-runpod`	`gmlupxnxfk`
PyTorch 2.13 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.13-runpod`	`y3j8xvk4f4`
PyTorch 2.13 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.13-runpod`	`vigpissn5w`

PyTorch 2.12

Template	CUDA Version	Docker Image	Template ID
PyTorch 2.12 (CUDA 12.6)	12.6	`vishva123/cuda-12.6-pytorch-2.12-runpod`	`ctmz86zmf0`
PyTorch 2.12 (CUDA 13.0)	13.0	`vishva123/cuda-13.0-pytorch-2.12-runpod`	`qjko5yiwzi`
PyTorch 2.12 (CUDA 13.2)	13.2	`vishva123/cuda-13.2-pytorch-2.12-runpod`	`ifg6xmye0f`

Citation & Acknowledgements

Quantized using Intel AutoRound. Base model created and released by LightOn AI.

@misc{lightonocr2_2026,
  title        = {LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR},
  author       = {Said Taghadouini and Adrien Cavaill\`{e}s and Baptiste Aubertin},
  year         = {2026},
  howpublished = {\url{[https://arxiv.org/abs/2601.14251](https://arxiv.org/abs/2601.14251)}}
}

@article{cheng2023optimize,
  title={Optimize Weight Rounding via Signed Gradient Descent for Large Language Model Quantization},
  author={Cheng, Wenhua and Hai, Wei and Shen, Haihao and others},
  journal={arXiv preprint arXiv:2309.05516},
  year={2023}
}