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:
- 55c22b0d6969026c57344f0d23b4e2bf4a2dc93f179f62cd06274eea41552fd0
- Size of remote file:
- 5.26 GB
- SHA256:
- c3fe7c584ab9fb211c3071ac035628ccc6861c4a7e1b59c200f58b33ddb81435
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