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 LegoKeeper/Hexo-0.5-Mini:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LegoKeeper/Hexo-0.5-Mini:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf LegoKeeper/Hexo-0.5-Mini:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf LegoKeeper/Hexo-0.5-Mini: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 LegoKeeper/Hexo-0.5-Mini:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf LegoKeeper/Hexo-0.5-Mini: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 LegoKeeper/Hexo-0.5-Mini:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf LegoKeeper/Hexo-0.5-Mini:Q4_K_M
Use Docker
docker model run hf.co/LegoKeeper/Hexo-0.5-Mini:Q4_K_M
Quick Links

LegoKeeper/Hexo-0.5-Mini

  • Base Model: WeiboAI/VibeThinker-1.5B
  • Quantization: Q4_K_M

Usage

This model was fine-tuned via LoRA and merged.

  • Hexo-0.5-Mini-Q4_K_M.gguf: High-performance quantized version for llama.cpp / Ollama.
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Model size
2B params
Architecture
qwen2
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