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 Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
# Run inference directly in the terminal:
llama-cli -hf Jackrong/Qwen3.5-9B-Python-Coder-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 Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
# Run inference directly in the terminal:
./llama-cli -hf Jackrong/Qwen3.5-9B-Python-Coder-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 Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
Use Docker
docker model run hf.co/Jackrong/Qwen3.5-9B-Python-Coder-GGUF:
Quick Links

Qwen3.5-9B-Python-Coder-GGUF

⚠️ Preview Status

This repository is currently a preview release.

The model is still under active testing, and I am continuing to explore more suitable training settings and optimization strategies. Because of this, the current version should be considered experimental.

Downloading or using this model for serious evaluation is not recommended yet. A more stable version will be released after further testing and parameter tuning.

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Architecture
qwen35
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