Instructions to use apparelsgoat/medcode with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use apparelsgoat/medcode 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 apparelsgoat/medcode:Q4_K_M # Run inference directly in the terminal: llama cli -hf apparelsgoat/medcode:Q4_K_M
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf apparelsgoat/medcode:Q4_K_M # Run inference directly in the terminal: llama cli -hf apparelsgoat/medcode: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 apparelsgoat/medcode:Q4_K_M # Run inference directly in the terminal: ./llama-cli -hf apparelsgoat/medcode: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 apparelsgoat/medcode:Q4_K_M # Run inference directly in the terminal: ./build/bin/llama-cli -hf apparelsgoat/medcode:Q4_K_M
Use Docker
docker model run hf.co/apparelsgoat/medcode:Q4_K_M
- LM Studio
- Jan
- Ollama
How to use apparelsgoat/medcode with Ollama:
ollama run hf.co/apparelsgoat/medcode:Q4_K_M
- Unsloth Desktop
- Pi
How to use apparelsgoat/medcode with Pi:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf apparelsgoat/medcode:Q4_K_M
Configure the model in Pi
# Install Pi: npm install -g @earendil-works/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "llama-cpp": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "apparelsgoat/medcode:Q4_K_M" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Docker Model Runner
How to use apparelsgoat/medcode with Docker Model Runner:
docker model run hf.co/apparelsgoat/medcode:Q4_K_M
- Lemonade
How to use apparelsgoat/medcode with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull apparelsgoat/medcode:Q4_K_M
Run and chat with the model
lemonade run user.medcode-Q4_K_M
List all available models
lemonade list
- Hermes Agent
How to use apparelsgoat/medcode with Hermes Agent:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf apparelsgoat/medcode: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 apparelsgoat/medcode:Q4_K_M
Run Hermes
hermes
- Atomic Chat
- OpenClaw
How to use apparelsgoat/medcode with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf apparelsgoat/medcode:Q4_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 "apparelsgoat/medcode:Q4_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"
medcode : GGUF
This model was finetuned and converted to GGUF format using Unsloth.
Example usage:
- For text only LLMs:
llama-cli -hf apparelsgoat/medcode --jinja - For multimodal models:
llama-mtmd-cli -hf apparelsgoat/medcode --jinja
Available Model files:
gemma-4-e4b-it.BF16.ggufgemma-4-e4b-it.BF16-mmproj.gguf
⚠️ Ollama Note for Vision Models
Important: Ollama currently does not support separate mmproj files for vision models.
To create an Ollama model from this vision model:
- Place the
Modelfilein the same directory as the finetuned bf16 merged model - Run:
ollama create model_name -f ./Modelfile(Replacemodel_namewith your desired name)
This will create a unified bf16 model that Ollama can use.
This was trained 2x faster with Unsloth

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