Instructions to use ssweens/Kimi-VL-A3B-Thinking-2506-mlx-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use ssweens/Kimi-VL-A3B-Thinking-2506-mlx-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("ssweens/Kimi-VL-A3B-Thinking-2506-mlx-4bit") config = load_config("ssweens/Kimi-VL-A3B-Thinking-2506-mlx-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
- Atomic Chat
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README.md
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---
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base_model: moonshotai/Kimi-VL-A3B-Thinking-2506
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license: mit
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pipeline_tag: text-
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library_name: mlx
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tags:
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- mlx
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2504.07491},
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}
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```
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---
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base_model: moonshotai/Kimi-VL-A3B-Thinking-2506
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license: mit
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pipeline_tag: image-text-to-text
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library_name: mlx
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tags:
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- mlx
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2504.07491},
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}
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```
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