Instructions to use lightonai/LightOnOCR-2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lightonai/LightOnOCR-2-1B with Transformers:

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

pipe = pipeline("image-text-to-text", model="lightonai/LightOnOCR-2-1B")
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)

# Load model directly
from transformers import AutoProcessor, AutoModelForSeq2SeqLM

processor = AutoProcessor.from_pretrained("lightonai/LightOnOCR-2-1B")
model = AutoModelForSeq2SeqLM.from_pretrained("lightonai/LightOnOCR-2-1B")
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=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use lightonai/LightOnOCR-2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lightonai/LightOnOCR-2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lightonai/LightOnOCR-2-1B",
		"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/lightonai/LightOnOCR-2-1B

SGLang

How to use lightonai/LightOnOCR-2-1B 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 "lightonai/LightOnOCR-2-1B" \
    --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": "lightonai/LightOnOCR-2-1B",
		"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 "lightonai/LightOnOCR-2-1B" \
        --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": "lightonai/LightOnOCR-2-1B",
		"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 lightonai/LightOnOCR-2-1B with Docker Model Runner:
```
docker model run hf.co/lightonai/LightOnOCR-2-1B
```

ameliechatelain commited on Feb 2

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@@ -23,25 +23,21 @@ tags:
 - forms
 ---
-# LightOnOCR-2-1B
-[![Speed](https://img.shields.io/badge/Speed-5.71%20pages%2Fs-brightgreen)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
-[![Model Size](https://img.shields.io/badge/Model%20Size-1B%20params-orange)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
-[![Demo](https://img.shields.io/badge/🚀%20Demo-Spaces-orange)](https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo)
-[![License](https://img.shields.io/badge/License-Apache%202.0-green.svg)](https://opensource.org/licenses/Apache-2.0)
-[![SOTA](https://img.shields.io/badge/OlmOCR--Bench-SOTA-gold)](https://huggingface.co/lightonai/LightOnOCR-2-1B)
-[![Transformers](https://img.shields.io/badge/🤗%20Transformers-supported-yellow)](https://github.com/huggingface/transformers)
-[![vLLM](https://img.shields.io/badge/vLLM-supported-purple)](https://github.com/vllm-project/vllm)
-[![Blog](https://img.shields.io/badge/📝%20Blog-HuggingFace-yellow)](https://huggingface.co/blog/lightonai/lightonocr-2)
-[![Dataset](https://img.shields.io/badge/📊%20Dataset-LightOnOCR--mix-blue)](https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126)
-[![BBox Dataset](https://img.shields.io/badge/📊%20BBox%20Dataset-LightOnOCR--bbox--mix-blue)](https://huggingface.co/datasets/lightonai/LightOnOCR-bbox-mix-0126)
-[![Colab](https://img.shields.io/badge/Finetuning-Notebook-F9AB00?logo=googlecolab)](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)
-[![Website](https://img.shields.io/badge/LightOn-Website-blue?logo=google-chrome)](https://lighton.ai)
-[![LinkedIn](https://img.shields.io/badge/LightOn-LinkedIn-0A66C2?logo=linkedin)](https://www.linkedin.com/company/lighton/)
-[![X](https://img.shields.io/badge/@LightOnIO-X-black?logo=x)](https://x.com/LightOnIO)
 <div align="center">
   <img src="lightonocr-banner.png" alt="LightOnOCR-2-1B Banner" width="600"/>
 </div>
 # LightOnOCR-2-1B
 **Best OCR model .** LightOnOCR-2-1B is **[LightOn's](https://lighton.ai)** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks.

 - forms
 ---
 <div align="center">
   <img src="lightonocr-banner.png" alt="LightOnOCR-2-1B Banner" width="600"/>
 </div>
+---
+<div align="center">
+[![Website](https://img.shields.io/badge/LightOn-Website-blue?logo=google-chrome)](https://lighton.ai)
+[![LinkedIn](https://img.shields.io/badge/LightOn-LinkedIn-0A66C2?logo=linkedin)](https://www.linkedin.com/company/lighton/)
+[![X](https://img.shields.io/badge/@LightOnIO-X-black?logo=x)](https://x.com/LightOnIO)
+📄 [Paper](https://arxiv.org/pdf/2601.14251) | 📝 [Blog](https://huggingface.co/blog/lightonai/lightonocr-2) | 🚀 [Demo](https://huggingface.co/spaces/lightonai/LightOnOCR-2-1B-Demo) | 📊 [Dataset](https://huggingface.co/datasets/lightonai/LightOnOCR-mix-0126) | 📓 [Finetuning](https://colab.research.google.com/drive/1WjbsFJZ4vOAAlKtcCauFLn_evo5UBRNa?usp=sharing)
+</div>
 # LightOnOCR-2-1B
 **Best OCR model .** LightOnOCR-2-1B is **[LightOn's](https://lighton.ai)** flagship OCR model, refined with RLVR training for maximum accuracy. We recommend this variant for most OCR tasks.