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:
# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx") model = AutoModelForMultimodalLM.from_pretrained("mlx-community/SmolVLM2-256M-Video-Instruct-mlx", device_map="auto") - 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] hf download mlx-community/SmolVLM2-256M-Video-Instruct-mlx --local-dir SmolVLM2-256M-Video-Instruct-mlx
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- Atomic Chat
Download processor_config.json from mlx-community/SmolVLM2-256M-Video-Instruct-mlx: direct link, hf CLI and curl.
- Browser
- Download file 67 Bytes
-
https://huggingface.co/mlx-community/SmolVLM2-256M-Video-Instruct-mlx/resolve/refs%2Fpr%2F3/processor_config.json
- Command line
-
hf download hf://mlx-community/SmolVLM2-256M-Video-Instruct-mlx@refs/pr/3/processor_config.json
-
curl -L -o processor_config.json https://huggingface.co/mlx-community/SmolVLM2-256M-Video-Instruct-mlx/resolve/refs%2Fpr%2F3/processor_config.json
67 Bytes
| { | |
| "image_seq_len": 64, | |
| "processor_class": "SmolVLMProcessor" | |
| } | |