How to use from
llama.cpp
Install from brew
brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wfwrigh/unity-coder-30b-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf wfwrigh/unity-coder-30b-GGUF:F16
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wfwrigh/unity-coder-30b-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf wfwrigh/unity-coder-30b-GGUF:F16
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 wfwrigh/unity-coder-30b-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf wfwrigh/unity-coder-30b-GGUF:F16
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 wfwrigh/unity-coder-30b-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wfwrigh/unity-coder-30b-GGUF:F16
Use Docker
docker model run hf.co/wfwrigh/unity-coder-30b-GGUF:F16
Quick Links

Unity Coder 30B - Q4_K_M + F16 GGUF

Quantized GGUF version of vishnuOI/unity-coder-30b for local inference.

  • Base model: vishnuOI/unity-coder-30b
  • Quantization: Q4_K_M + F16
  • Format: GGUF (llama.cpp)

Usage

Load directly in LM Studio, Ollama, or llama.cpp.

Full Disclaimer:

Needs testing and tweaks- as it stands, the q4_k_m version with base LM Studio settings performs worse than some newer models without Unity Lora training- consider it more of an experiment. If someone has better experience with the f16 or has a good configuration, let me know!

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