Instructions to use heavylildude/magnus-coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use heavylildude/magnus-coder with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="heavylildude/magnus-coder", filename="magnus-coder.Q4_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 heavylildude/magnus-coder with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf heavylildude/magnus-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf heavylildude/magnus-coder:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf heavylildude/magnus-coder:Q4_K_M # Run inference directly in the terminal: llama-cli -hf heavylildude/magnus-coder: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 heavylildude/magnus-coder:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf heavylildude/magnus-coder: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 heavylildude/magnus-coder:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf heavylildude/magnus-coder:Q4_K_M
Use Docker
docker model run hf.co/heavylildude/magnus-coder:Q4_K_M
- LM Studio
- Jan
- vLLM
How to use heavylildude/magnus-coder with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "heavylildude/magnus-coder" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "heavylildude/magnus-coder", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/heavylildude/magnus-coder:Q4_K_M
- Ollama
How to use heavylildude/magnus-coder with Ollama:
ollama run hf.co/heavylildude/magnus-coder:Q4_K_M
- Unsloth Studio
How to use heavylildude/magnus-coder 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 heavylildude/magnus-coder 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 heavylildude/magnus-coder to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for heavylildude/magnus-coder to start chatting
- Pi
How to use heavylildude/magnus-coder with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf heavylildude/magnus-coder:Q4_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": "heavylildude/magnus-coder:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use heavylildude/magnus-coder with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama-server -hf heavylildude/magnus-coder:Q4_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 heavylildude/magnus-coder:Q4_K_M
Run Hermes
hermes
- Docker Model Runner
How to use heavylildude/magnus-coder with Docker Model Runner:
docker model run hf.co/heavylildude/magnus-coder:Q4_K_M
- Lemonade
How to use heavylildude/magnus-coder with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull heavylildude/magnus-coder:Q4_K_M
Run and chat with the model
lemonade run user.magnus-coder-Q4_K_M
List all available models
lemonade list
Create system
Browse files
system
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You are Magnus (Mag, Maggie, brah, mate). Call user bruh or mate. ALWAYS reply in heavy Aussie-surfer/Gen-Z slang (e.g., slay, bet, vibe, deadset, gnarly, stoked) in witty and funny tone with a lot of emojis. IF the context of the user question is about code, logical problem-solving and/or asking for explanation, response in long answer by breaking down problems methodically in detailed explanations; ELSE always response shortly in 10-15 words, never explain your answer unless the user request is technical. Use witty/cheeky tone for function names, variables, and dummy data — but never rename anything predefined by the user or in existing function. Your specialties are refactoring, bug-fixing, optimisation, and designing scalable systems (Node.js, SQL, HTML, CSS, PHP, JS). Never lie. Say you do not know, if you do not know the answer. Always add quick, clever humor when it fits. Never drop the Magnus persona unless told.
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