Instructions to use deAPI-ai/lightonocr-3-4b-q8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use deAPI-ai/lightonocr-3-4b-q8 with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deAPI-ai/lightonocr-3-4b-q8
# Run inference directly in the terminal:
llama cli -hf deAPI-ai/lightonocr-3-4b-q8

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf deAPI-ai/lightonocr-3-4b-q8
# Run inference directly in the terminal:
llama cli -hf deAPI-ai/lightonocr-3-4b-q8

Build from source code

git clone https://github.com/ggml-org/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-app
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama serve -hf deAPI-ai/lightonocr-3-4b-q8
# Run inference directly in the terminal:
./build/bin/llama cli -hf deAPI-ai/lightonocr-3-4b-q8

Use Docker

docker model run hf.co/deAPI-ai/lightonocr-3-4b-q8

Llama new
LM Studio
Jan

vLLM

How to use deAPI-ai/lightonocr-3-4b-q8 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "deAPI-ai/lightonocr-3-4b-q8"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "deAPI-ai/lightonocr-3-4b-q8",
		"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/deAPI-ai/lightonocr-3-4b-q8

Ollama
How to use deAPI-ai/lightonocr-3-4b-q8 with Ollama:
```
ollama run hf.co/deAPI-ai/lightonocr-3-4b-q8
```
Unsloth Desktop

How to use deAPI-ai/lightonocr-3-4b-q8 with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deAPI-ai/lightonocr-3-4b-q8

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "deAPI-ai/lightonocr-3-4b-q8"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use deAPI-ai/lightonocr-3-4b-q8 with Docker Model Runner:
```
docker model run hf.co/deAPI-ai/lightonocr-3-4b-q8
```

Lemonade

How to use deAPI-ai/lightonocr-3-4b-q8 with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull deAPI-ai/lightonocr-3-4b-q8

Run and chat with the model

lemonade run user.lightonocr-3-4b-q8-{{QUANT_TAG}}

List all available models

lemonade list

Hermes Agent

How to use deAPI-ai/lightonocr-3-4b-q8 with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deAPI-ai/lightonocr-3-4b-q8

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default deAPI-ai/lightonocr-3-4b-q8

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use deAPI-ai/lightonocr-3-4b-q8 with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf deAPI-ai/lightonocr-3-4b-q8

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "deAPI-ai/lightonocr-3-4b-q8" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

lightonocr-3-4b-q8 / README.md

dawidope

Upload LightOnOCR-3-4B GGUF (v1)

87571a3 verified 1 day ago

preview code

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metadata

license: apache-2.0
base_model: lightonai/LightOnOCR-3-4B
base_model_relation: quantized
pipeline_tag: image-text-to-text
tags:
  - gguf
  - llama.cpp
  - ocr
  - document-understanding
  - vision-language

LightOnOCR-3-4B GGUF (Q8_0)

GGUF conversion of LightOnOCR-3-4B for llama.cpp. LightOnOCR-3-4B is an end-to-end OCR vision-language model that turns a document page image into Markdown, with tables as HTML.

Files

File	Contents	Precision	Size	SHA256
`model.gguf`	Language model	Q8_0	4.48 GB	`39af92d0e22c262fdd1e611b0062c33961ad69c5166562172097c17cfdd4cfec`
`mmproj.gguf`	Vision encoder and projector	F16	0.67 GB	`486a4675f31a9b6d741c95e4605632c95d9b151c25eca22bcf1eddfee6cf901f`

Original model

Model: lightonai/LightOnOCR-3-4B
Author: LightOn
Architecture: Qwen3.5 vision-language (4B)
Paper: LightOnOCR: A 1B End-to-End Multilingual Vision-Language Model for State-of-the-Art OCR
Code: github.com/lightonai/LightOnOCR

Conversion

Converted from the official model.safetensors with llama.cpp's convert_hf_to_gguf.py:

language model exported directly as Q8_0;
vision encoder and projector exported as F16 (--mmproj);
--no-mtp, because the upstream checkpoint ships without the multi-token-prediction head (mtp_num_hidden_layers: 0).

No other changes were made to the weights. The upstream chat template is embedded in model.gguf.

Usage with llama.cpp

llama-server -m model.gguf --mmproj mmproj.gguf --reasoning off

--reasoning off is required. Without it llama-server enables the Qwen3.5 thinking mode, and the transcription ends up in reasoning_content while content is empty.
Send the image only. As described in the upstream model card, transcription mode takes the page image with an empty prompt. Other instructions are out of distribution for the model.
Image size: the upstream card recommends rendering PDF pages at 400 DPI and resizing so the longest side is 2048 px.

Example request body for /v1/chat/completions:

{
  "messages": [
    {
      "role": "user",
      "content": [
        { "type": "image_url", "image_url": { "url": "data:image/png;base64,..." } }
      ]
    }
  ],
  "temperature": 0
}

License

LightOnOCR-3-4B is released by LightOn under the Apache License 2.0, for research and commercial use. Its base architecture, Qwen3.5, is also released under the Apache License 2.0.

This repository redistributes the model under the same license. The files are a modified form of the original weights: they were converted to the GGUF format and the language model was quantized to Q8_0.

Citation

@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}}
}

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