Instructions to use mlx-community/llava-v1.6-mistral-7b-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/llava-v1.6-mistral-7b-4bit 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/llava-v1.6-mistral-7b-4bit") config = load_config("mlx-community/llava-v1.6-mistral-7b-4bit") # 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 Settings
- LM Studio
- Xet hash:
- d00044502caf31853875b8bf7cbf154666775ed3dac1c7938b134d4d3bfa8f9e
- Size of remote file:
- 4.26 GB
- SHA256:
- 8c067c64c4cc2ab04af36a9c0a2cb528efc89ed0828a4a5df1332522ef5d3f62
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