Instructions to use mlx-community/gemma-4-31b-6bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/gemma-4-31b-6bit with MLX:
# Make sure mlx-vlm is installed # pip install --upgrade mlx-vlm from mlx_vlm import load, generate from mlx_vlm.prompt_utils import apply_chat_template from mlx_vlm.utils import load_config # Load the model model, processor = load("mlx-community/gemma-4-31b-6bit") config = load_config("mlx-community/gemma-4-31b-6bit") # Prepare input image = ["http://images.cocodataset.org/val2017/000000039769.jpg"] prompt = "Describe this image." # Apply chat template formatted_prompt = apply_chat_template( processor, config, prompt, num_images=1 ) # Generate output output = generate(model, processor, formatted_prompt, image) print(output) - Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
- Xet hash:
- 2e7ad99fe28ec40cd28867fbe6c65c1ec92ce18051c4fdb8eccad32e16989ada
- Size of remote file:
- 32.2 MB
- SHA256:
- 12bac982b793c44b03d52a250a9f0d0b666813da566b910c24a6da0695fd11e6
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