Instructions to use hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4 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("hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4") config = load_config("hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4") # 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 tokenizer.json from hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4: direct link, hf CLI and curl.
- Browser
- Download file 17.1 MB
-
https://huggingface.co/hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4/resolve/main/tokenizer.json
- Command line
-
hf download hf://hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4/tokenizer.json
-
curl -L -o tokenizer.json https://huggingface.co/hehua2008/Mistral-Small-3.2-24B-Instruct-2506-MLX-mixed_3_4/resolve/main/tokenizer.json
17.1 MB
- Xet hash:
- a99b20a27d0573b29f9ccd84d97f346991ac2e8e306b7e06bcd072051c71c525
- Size of remote file:
- 17.1 MB
- SHA256:
- b76085f9923309d873994d444989f7eb6ec074b06f25b58f1e8d7b7741070949
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