Image-Text-to-Text
Safetensors
MLX
openmed
cohere_compass
openmedkit
apple-silicon
ios
on-device
vision
multimodal
clinical
medical
privacy
native-resolution
conversational
8-bit precision
Instructions to use OpenMed/North-Micro-Vision-Instruct-8bit-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-8bit-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-8bit-mlx") config = load_config("OpenMed/North-Micro-Vision-Instruct-8bit-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:
- 5e61b905a12085106daef49a6343a8c0b2ba42f4393122294c7e22318cd7688c
- Size of remote file:
- 3.15 GB
- SHA256:
- 912eb8d8463c14539790711b4516beee01510963b34e1d892ecfaa4838906018
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.