Instructions to use Youssofal/Gemma4-MTPLX-Optimized-Quality with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Youssofal/Gemma4-MTPLX-Optimized-Quality 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-Quality") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use Youssofal/Gemma4-MTPLX-Optimized-Quality 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-Quality" --prompt "Once upon a time"
- Xet hash:
- 2605e799ebf1b97007de31749ccd92d828fa85e9ee9fedce2e92d0557be2997a
- Size of remote file:
- 5.27 GB
- SHA256:
- 7a6cf32b03f6953bb42d863a4cc28e07289b6ea9fd501dfa9882b19e5539b048
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