Instructions to use mmrech/Minimalism with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mmrech/Minimalism with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("mmrech/Minimalism") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- MLX LM
How to use mmrech/Minimalism with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "mmrech/Minimalism" --prompt "Once upon a time"
| # Minimalism Usage | |
| ## Quick Start | |
| ### 1. Install dependencies | |
| ```bash | |
| pip install mlx-lm | |
| ``` | |
| ### 2. Start the server | |
| ```bash | |
| # Using the base model with this adapter | |
| python -m mlx_lm.server \ | |
| --model mlx-community/Qwen2.5-Coder-0.5B-Instruct-4bit \ | |
| --adapter-path . \ | |
| --host 127.0.0.1 \ | |
| --port 8080 | |
| ``` | |
| ### 3. Test with curl | |
| ```bash | |
| curl http://127.0.0.1:8080/v1/chat/completions \ | |
| -H 'Content-Type: application/json' \ | |
| -d '{ | |
| "model": "Minimalism", | |
| "messages": [ | |
| {"role": "user", "content": "Write a Python function to add two numbers"} | |
| ], | |
| "max_tokens": 256 | |
| }' | |
| ``` | |
| ## Response Format | |
| Minimalism provides runnable-first responses with these sections: | |
| - **Solution**: Main implementation | |
| - **Usage**: Smallest runnable example | |
| - **Sanity test**: Tiny test snippet (when appropriate) | |