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 zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
Install from WinGet (Windows)
winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
llama cli -hf zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_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 zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./llama-cli -hf zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_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 zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
Use Docker
docker model run hf.co/zaydiscold/Qwopus3.6-27B-v2-Q5_K_M-GGUF:Q5_K_M
Quick Links

Qwopus3.6-27B-v2 Q5_K_M GGUF

Q5_K_M quantization of Jackrong/Qwopus3.6-27B-v2 via stock llama.cpp (ggml-org/llama.cpp), built locally with Metal support on Apple Silicon. Preserves MTP draft heads from the source.

Use

llama-cli -m Qwopus3.6-27B-v2-Q5_K_M.gguf -p "Hi"

Author's temperature recommendation: 0.75–1.0.

Credits


Mix brought by the NOTORIOUS MLX.

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Model size
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Architecture
qwen35
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