Instructions to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForImageTextToText processor = AutoProcessor.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx") model = AutoModelForImageTextToText.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx") - MLX
How to use mlx-community/SmolVLM2-256M-Video-Instruct-mlx with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SmolVLM2-256M-Video-Instruct-mlx mlx-community/SmolVLM2-256M-Video-Instruct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- LM Studio
| { | |
| "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": "SmolVLMImageProcessor", | |
| "image_std": [ | |
| 0.5, | |
| 0.5, | |
| 0.5 | |
| ], | |
| "max_image_size": { | |
| "longest_edge": 512 | |
| }, | |
| "processor_class": "SmolVLMProcessor", | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 2048 | |
| }, | |
| "video_sampling": { | |
| "fps": 1, | |
| "max_frames": 64, | |
| "video_size": { | |
| "longest_edge": 512 | |
| } | |
| } | |
| } | |