Instructions to use prithivMLmods/LightOnOCR-3-4B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/LightOnOCR-3-4B-GGUF")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("prithivMLmods/LightOnOCR-3-4B-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use prithivMLmods/LightOnOCR-3-4B-GGUF 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 prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: llama cli -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Use pre-built binary
# Download pre-built binary from: # https://github.com/ggerganov/llama.cpp/releases # Start a local OpenAI-compatible server with a web UI: ./llama-server -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Build from source code
git clone https://github.com/ggerganov/llama.cpp.git cd llama.cpp cmake -B build cmake --build build -j --target llama-server llama-cli # Start a local OpenAI-compatible server with a web UI: ./build/bin/llama-server -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Use Docker
docker model run hf.co/prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "prithivMLmods/LightOnOCR-3-4B-GGUF" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "prithivMLmods/LightOnOCR-3-4B-GGUF", "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/prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
- SGLang
How to use prithivMLmods/LightOnOCR-3-4B-GGUF 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 "prithivMLmods/LightOnOCR-3-4B-GGUF" \ --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": "prithivMLmods/LightOnOCR-3-4B-GGUF", "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 "prithivMLmods/LightOnOCR-3-4B-GGUF" \ --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": "prithivMLmods/LightOnOCR-3-4B-GGUF", "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" } } ] } ] }' - Ollama
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Ollama:
ollama run hf.co/prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
- Unsloth Desktop
- Pi
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
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": "prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Docker Model Runner:
docker model run hf.co/prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
- Lemonade
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Run and chat with the model
lemonade run user.LightOnOCR-3-4B-GGUF-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
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 prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use prithivMLmods/LightOnOCR-3-4B-GGUF with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M
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 "prithivMLmods/LightOnOCR-3-4B-GGUF:Q4_K_M" \ --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"
# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("prithivMLmods/LightOnOCR-3-4B-GGUF", device_map="auto")LightOnOCR-3-4B-GGUF
LightOnOCR-3-4B, developed by lightonai, is the largest and most accurate model in the LightOnOCR-3 family of end-to-end OCR vision-language models, built on the Qwen3.5-4B architecture and released under the Apache 2.0 license. Recommended for most OCR tasks, it serves as a powerful, single-model alternative to complex document understanding pipelines by combining high-quality text transcription (triggered by an empty prompt) with advanced visual understanding features. When used with the
groundingprompt, the model outputs labeled bounding boxes for all document elements, generates short descriptions for images, and extracts numerical data from charts into structured HTML tables. Highly versatile and optimized for seamless integration with Transformers and vLLM, it excels at processing complex layouts—including tables, receipts, forms, multi-column documents, and math notation—and performs best when documents are preprocessed at 400 DPI with a target longest dimension of 2048px.
- Speculative Decoding — No
Model Files
| File Name | Quant Type | File Size | File Link | Description |
|---|---|---|---|---|
| LightOnOCR-3-4B.BF16.gguf | BF16 | 8.42 GB | Link | Full BF16 weights. Highest quality, largest file size. |
| LightOnOCR-3-4B.Q3_K_L.gguf | Q3_K_L | 2.42 GB | Link | Lower quality but usable, good for low RAM availability. |
| LightOnOCR-3-4B.Q3_K_M.gguf | Q3_K_M | 2.26 GB | Link | Low quality. |
| LightOnOCR-3-4B.Q4_K_M.gguf | Q4_K_M | 2.71 GB | Link | Good quality, default size for most use cases, recommended. |
| LightOnOCR-3-4B.Q4_K_S.gguf | Q4_K_S | 2.56 GB | Link | Slightly lower quality with more space savings, recommended. |
| LightOnOCR-3-4B.Q5_K_M.gguf | Q5_K_M | 3.07 GB | Link | High quality, recommended. |
| LightOnOCR-3-4B.Q5_K_S.gguf | Q5_K_S | 2.99 GB | Link | High quality, recommended. |
| LightOnOCR-3-4B.Q6_K.gguf | Q6_K | 3.46 GB | Link | Very high quality, near perfect, recommended. |
| LightOnOCR-3-4B.Q8_0.gguf | Q8_0 | 4.48 GB | Link | Extremely high quality, generally unneeded but max available quant. |
| LightOnOCR-3-4B.mmproj-bf16.gguf | mmproj-bf16 | 676 MB | Link | Multimodal projection file in BF16 format. Used for vision/language models. |
| LightOnOCR-3-4B.mmproj-q8_0.gguf | mmproj-q8_0 | 367 MB | Link | Multimodal projection file in Q8_0 quantization. Smaller size for vision capabilities. |
llama.cpp
LLM inference in C/C++ — https://github.com/ggml-org/llama.cpp
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Model tree for prithivMLmods/LightOnOCR-3-4B-GGUF
Base model
lightonai/LightOnOCR-3-4B
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-text-to-text", model="prithivMLmods/LightOnOCR-3-4B-GGUF")