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 chalyi/clitron-gh:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf chalyi/clitron-gh:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf chalyi/clitron-gh:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf chalyi/clitron-gh: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 chalyi/clitron-gh:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf chalyi/clitron-gh: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 chalyi/clitron-gh:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf chalyi/clitron-gh:Q4_K_M
Use Docker
docker model run hf.co/chalyi/clitron-gh:Q4_K_M
Quick Links

Clitron GH - CLI Interpreter Model

Fine-tuned Llama 3.2 1B model for interpreting natural language commands for GitHub CLI (gh).

Model Details

  • Base Model: meta-llama/Llama-3.2-1B-Instruct
  • Training: SFT with response-only loss masking + EOS token
  • Quantization: Q4_K_M (GGUF)
  • Size: 770MB

Usage

This model is designed to be used with the clitron library.

Input Format

<|system|>
You are a CLI command interpreter...
<|user|>
show my open pull requests
<|assistant|>

Output Format

{"command": "pr", "subcommand": "list", "args": {"author": "@me", "state": "open"}, "flags": [], "confidence": 0.95}

Performance

Metric Value
Valid JSON 94.3%
Command Accuracy 58.3%
Invalid JSON 5.7%

License

This model is based on Llama 3.2 and is subject to the Llama 3.2 Community License.

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GGUF
Model size
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Architecture
llama
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