Instructions to use mlx-community/SmolVLM2-500M-Video-Instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/SmolVLM2-500M-Video-Instruct-mlx with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mlx-community/SmolVLM2-500M-Video-Instruct-mlx") model = AutoModelForMultimodalLM.from_pretrained("mlx-community/SmolVLM2-500M-Video-Instruct-mlx", device_map="auto") - MLX
How to use mlx-community/SmolVLM2-500M-Video-Instruct-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SmolVLM2-500M-Video-Instruct-mlx mlx-community/SmolVLM2-500M-Video-Instruct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
metadata
library_name: transformers
license: apache-2.0
datasets:
- HuggingFaceM4/the_cauldron
- HuggingFaceM4/Docmatix
pipeline_tag: video-text-to-text
language:
- en
base_model:
- HuggingFaceTB/SmolLM2-360M-Instruct
- google/siglip-base-patch16-512
- HuggingFaceTB/SmolVLM2-500M-Video-Instruct
tags:
- mlx
HuggingFaceTB/SmolVLM2-500M-Video-Instruct-mlx
This model was converted to MLX format from HuggingFaceTB/SmolVLM2-500M-Video-Instruct using mlx-vlm version 0.1.13.
Refer to the original model card for more details on the model.
Use with mlx
pip install -U mlx-vlm
python -m mlx_vlm.generate --model mlx-community/SmolVLM2-500M-Video-Instruct-mlx --image https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/bee.jpg --prompt "Can you describe this image?"