How to use from
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 noctrex/LightOnOCR-2-1B-GGUF:
# Run inference directly in the terminal:
llama cli -hf noctrex/LightOnOCR-2-1B-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf noctrex/LightOnOCR-2-1B-GGUF:
# Run inference directly in the terminal:
llama cli -hf noctrex/LightOnOCR-2-1B-GGUF:
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 noctrex/LightOnOCR-2-1B-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf noctrex/LightOnOCR-2-1B-GGUF:
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 noctrex/LightOnOCR-2-1B-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf noctrex/LightOnOCR-2-1B-GGUF:
Use Docker
docker model run hf.co/noctrex/LightOnOCR-2-1B-GGUF:
Quick Links

This are the quantizations of the model LightOnOCR-2-1B

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For the mmproj, try to use the F32 version as it will produce the best results.
Quality order: F32 > BF16 > F16

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