Image-Text-to-Text
MLX
Safetensors
cohere_compass
mlx-vlm
openmed
openmedkit
apple-silicon
on-device
vision
multimodal
clinical
medical
privacy
native-resolution
conversational
4-bit precision
Instructions to use OpenMed/North-Micro-Vision-Instruct-4bit-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use OpenMed/North-Micro-Vision-Instruct-4bit-mlx 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("OpenMed/North-Micro-Vision-Instruct-4bit-mlx") config = load_config("OpenMed/North-Micro-Vision-Instruct-4bit-mlx") # 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
- Xet hash:
- d92e116c7c5e603f925d7b85464d32b6d0254745d16c188a8e341fb56b345ccb
- Size of remote file:
- 2.17 GB
- SHA256:
- ce3248aa6f570f742cd8d8f13bf08d90f05a26796c98459627aa015586351fd2
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