Instructions to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("bsisduck/North-Mini-Code-1.0-MLX-MXFP8") prompt = "Write a story about Einstein" messages = [{"role": "user", "content": prompt}] prompt = tokenizer.apply_chat_template( messages, add_generation_prompt=True ) text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Pi
How to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with Pi:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bsisduck/North-Mini-Code-1.0-MLX-MXFP8"
Configure the model in Pi
# Install Pi: npm install -g @mariozechner/pi-coding-agent # Add to ~/.pi/agent/models.json: { "providers": { "mlx-lm": { "baseUrl": "http://localhost:8080/v1", "api": "openai-completions", "apiKey": "none", "models": [ { "id": "bsisduck/North-Mini-Code-1.0-MLX-MXFP8" } ] } } }Run Pi
# Start Pi in your project directory: pi
- Hermes Agent new
How to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with Hermes Agent:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bsisduck/North-Mini-Code-1.0-MLX-MXFP8"
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 bsisduck/North-Mini-Code-1.0-MLX-MXFP8
Run Hermes
hermes
- OpenClaw new
How to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with OpenClaw:
Start the MLX server
# Install MLX LM: uv tool install mlx-lm # Start a local OpenAI-compatible server: mlx_lm.server --model "bsisduck/North-Mini-Code-1.0-MLX-MXFP8"
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 "bsisduck/North-Mini-Code-1.0-MLX-MXFP8" \ --custom-provider-id mlx-lm \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- MLX LM
How to use bsisduck/North-Mini-Code-1.0-MLX-MXFP8 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Interactive chat REPL mlx_lm.chat --model "bsisduck/North-Mini-Code-1.0-MLX-MXFP8"
Run an OpenAI-compatible server
# Install MLX LM uv tool install mlx-lm # Start the server mlx_lm.server --model "bsisduck/North-Mini-Code-1.0-MLX-MXFP8" # Calling the OpenAI-compatible server with curl curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "bsisduck/North-Mini-Code-1.0-MLX-MXFP8", "messages": [ {"role": "user", "content": "Hello"} ] }'
Thanks and a few question
First of all, thanks for the conversion efforts. I downloaded this and gave it a try. It works with a custom build of mlx_lm basically, but due the model outputting custom thinking/tool markers and no parser support in most of the frontends, the usage is very limited. Opencode, Open Webui and most other tools lack support for that format, so thinking is not displayed properly and tool calling also doesn't work most of the time. I saw that there is support in vllm for Cohere's parsers and formats, but mlx_lm doesn't have anything like that. Any way this could get implemented for MLX? Some custom layer or a middleware for conversion of the markers? What would theoretically be the right way of adding the support? I'm rather new to the ecosystem, btw, which is why I'm asking.