GGUF
conversational
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 Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF: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 Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF: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 Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
Use Docker
docker model run hf.co/Liontix/Qwen3-4B-Advanced-Reasoning-Distill-GGUF:Q4_K_M
Quick Links

This is a fine-tuned version of Qwen3 4B using one reasoning and one non-reasoning dataset from closed-source LLMs (made available by reedmayhew, thanks!).

The total size of this training dataset is around 300 rows. This model was fine-tuned for 3000 steps.

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GGUF
Model size
4B params
Architecture
qwen3
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