Instructions to use mlx-community/idefics2-8b-chatty-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use mlx-community/idefics2-8b-chatty-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("mlx-community/idefics2-8b-chatty-4bit") config = load_config("mlx-community/idefics2-8b-chatty-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
File size: 460 Bytes
4499481 6a69aad 4499481 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"do_convert_rgb": true,
"do_image_splitting": true,
"do_normalize": true,
"do_pad": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "Idefics2ImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"processor_class": "Idefics2Processor",
"resample": 2,
"rescale_factor": 0.00392156862745098,
"size": {
"longest_edge": 980,
"shortest_edge": 378
}
}
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