Instructions to use smdesai/SmolVLM2-2.2B-Instruct-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use smdesai/SmolVLM2-2.2B-Instruct-4bit with Transformers:
# Load model directly from transformers import AutoProcessor, AutoModelForMultimodalLM processor = AutoProcessor.from_pretrained("smdesai/SmolVLM2-2.2B-Instruct-4bit") model = AutoModelForMultimodalLM.from_pretrained("smdesai/SmolVLM2-2.2B-Instruct-4bit", device_map="auto") - MLX
How to use smdesai/SmolVLM2-2.2B-Instruct-4bit with MLX:
# Download the model from the Hub pip install huggingface_hub[hf_xet] huggingface-cli download --local-dir SmolVLM2-2.2B-Instruct-4bit smdesai/SmolVLM2-2.2B-Instruct-4bit
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- 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": 384 | |
| }, | |
| "processor_class": "SmolVLMProcessor", | |
| "resample": 1, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "longest_edge": 1536 | |
| }, | |
| "video_sampling": { | |
| "fps": 1, | |
| "max_frames": 64, | |
| "video_size": { | |
| "longest_edge": 384 | |
| } | |
| } | |
| } | |