Instructions to use thetom-ai/PsyGuard-Qwopus-27B-alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="thetom-ai/PsyGuard-Qwopus-27B-alpha", filename="PsyGuard-Qwopus-27B-alpha-MTP-Q5_K_M.gguf", )
llm.create_chat_completion( messages = [ { "role": "user", "content": "What is the capital of France?" } ] ) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with 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
- LM Studio
- Jan
- vLLM
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "thetom-ai/PsyGuard-Qwopus-27B-alpha" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "thetom-ai/PsyGuard-Qwopus-27B-alpha", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
- Ollama
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Ollama:
ollama run hf.co/thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
- Unsloth Studio
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Unsloth Studio:
Install Unsloth Studio (macOS, Linux, WSL)
curl -fsSL https://unsloth.ai/install.sh | sh # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for thetom-ai/PsyGuard-Qwopus-27B-alpha to start chatting
Install Unsloth Studio (Windows)
irm https://unsloth.ai/install.ps1 | iex # Run unsloth studio unsloth studio -H 0.0.0.0 -p 8888 # Then open http://localhost:8888 in your browser # Search for thetom-ai/PsyGuard-Qwopus-27B-alpha to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for thetom-ai/PsyGuard-Qwopus-27B-alpha to start chatting
- Pi
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
Configure Hermes
# Install Hermes: curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash hermes setup # Point Hermes at the local server: hermes config set model.provider custom hermes config set model.base_url http://127.0.0.1:8080/v1 hermes config set model.default thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
Run Hermes
hermes
- Atomic Chat new
- OpenClaw new
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- Docker Model Runner
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Docker Model Runner:
docker model run hf.co/thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
- Lemonade
How to use thetom-ai/PsyGuard-Qwopus-27B-alpha with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull thetom-ai/PsyGuard-Qwopus-27B-alpha:Q5_K_M
Run and chat with the model
lemonade run user.PsyGuard-Qwopus-27B-alpha-Q5_K_M
List all available models
lemonade list
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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