Instructions to use kernelpool/Kimi-K3-2bit-UVMAX with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use kernelpool/Kimi-K3-2bit-UVMAX 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("kernelpool/Kimi-K3-2bit-UVMAX") config = load_config("kernelpool/Kimi-K3-2bit-UVMAX") # 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:
- 27c1cf5d3d6234b4f78439b6e982135f40e8dada4d58a23aa7d9a64ec0bee53c
- Size of remote file:
- 3.65 GB
- SHA256:
- 8897788b3c956daf875fac4ccdc1f127eed1d717b360dad37f705c394851812f
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