Image-Text-to-Text
MLX
Safetensors
English
qwen2_vl
vision-language
image-to-text
qwen2-vl
apple-silicon
conversational
8-bit precision
Instructions to use Munchit/dam-vision-v3-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use Munchit/dam-vision-v3-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("Munchit/dam-vision-v3-mlx") config = load_config("Munchit/dam-vision-v3-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
- Xet hash:
- 664f337c546274ab57b2cb2d564f355caab2328fd9e8588a711f755ea6a67cce
- Size of remote file:
- 11.4 MB
- SHA256:
- ff8cce547abc110590d19c6b5b6e0c6a7b4c8d1012d78b9c42131bae7f494a02
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