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:
- 7788e4797a2287f020c361721d4d61ff9b1beeb67e7008dcf4974ed0bc4ec261
- Size of remote file:
- 3.65 GB
- SHA256:
- 33bc7438ada6bd129b2968e73ce8a44857096980b213a6d2f3284c1b2c691787
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