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

PsyGuard-Qwopus-27B-alpha

Experimental coding model. Fine-tune of Jackrong/Qwopus3.6-27B-Coder.

alpha — experimental. Internal evals only. Dogfood at your own risk.

Format: Q5_K_M GGUF (llama.cpp / LM Studio / Ollama).

The GGUF bundles an MTP draft head — runs as a normal model, or ~1.8× faster with speculative decode in llama.cpp: --spec-type draft-mtp --spec-draft-n-max 2

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Model size
27B params
Architecture
qwen35
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