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"
File size: 576 Bytes
cc1cd06 | 1 2 3 4 5 6 7 8 9 10 11 12 | {
"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"
} |