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
- Atomic Chat
Download added_tokens.json from kernelpool/Kimi-K3-2bit-UVMAX: direct link, hf CLI and curl.
- Browser
- Download file 200 Bytes
-
https://huggingface.co/kernelpool/Kimi-K3-2bit-UVMAX/resolve/72d65b9bb27d36d6ee120eeb37a40a978245f749/added_tokens.json
- Command line
-
hf download hf://kernelpool/Kimi-K3-2bit-UVMAX@72d65b9bb27d36d6ee120eeb37a40a978245f749/added_tokens.json
-
curl -L -o added_tokens.json https://huggingface.co/kernelpool/Kimi-K3-2bit-UVMAX/resolve/72d65b9bb27d36d6ee120eeb37a40a978245f749/added_tokens.json
200 Bytes
| { | |
| "<|end_header_id|>": 163844, | |
| "<|im_assistant|>": 163842, | |
| "<|im_end|>": 163840, | |
| "<|im_middle|>": 163846, | |
| "<|im_system|>": 163845, | |
| "<|im_user|>": 163841, | |
| "<|start_header_id|>": 163843 | |
| } | |