Instructions to use Youssofal/Gemma4-MTPLX-Optimized-Speed with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/Gemma4-MTPLX-Optimized-Speed 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("Youssofal/Gemma4-MTPLX-Optimized-Speed") 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 Youssofal/Gemma4-MTPLX-Optimized-Speed with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "Youssofal/Gemma4-MTPLX-Optimized-Speed" --prompt "Once upon a time"
| { | |
| "note": "Converted locally with MTPLX Gemma4 assistant classes because stock mlx_lm.convert does not support model_type=gemma4_assistant.", | |
| "precision_policy": "Q6 affine G64 for all quantizable assistant modules, including tied embedding/LM-head path and projections.", | |
| "quantization": { | |
| "bits": 6, | |
| "group_size": 64, | |
| "mode": "affine" | |
| }, | |
| "source_path": "/Users/youssof/Documents/MTPLX/models/gemma-4-31B-it-assistant-google-bf16-mlx", | |
| "source_repo": "google/gemma-4-31B-it-assistant", | |
| "source_revision": "cffbbd2cea41ea56a0fa5b0487e0d445121fd204" | |
| } |