Instructions to use gwkrsrch2/InternViT-300M-from-InternVL3_5-2B-HF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use gwkrsrch2/InternViT-300M-from-InternVL3_5-2B-HF with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="gwkrsrch2/InternViT-300M-from-InternVL3_5-2B-HF")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("gwkrsrch2/InternViT-300M-from-InternVL3_5-2B-HF") model = AutoModel.from_pretrained("gwkrsrch2/InternViT-300M-from-InternVL3_5-2B-HF") - Notebooks
- Google Colab
- Kaggle
| { | |
| "crop_size": { | |
| "height": 448, | |
| "width": 448 | |
| }, | |
| "crop_to_patches": false, | |
| "data_format": "channels_first", | |
| "default_to_square": true, | |
| "device": null, | |
| "disable_grouping": null, | |
| "do_center_crop": null, | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.485, | |
| 0.456, | |
| 0.406 | |
| ], | |
| "image_processor_type": "GotOcr2ImageProcessorFast", | |
| "image_std": [ | |
| 0.229, | |
| 0.224, | |
| 0.225 | |
| ], | |
| "input_data_format": null, | |
| "max_patches": 12, | |
| "min_patches": 1, | |
| "processor_class": "InternVLProcessor", | |
| "resample": 3, | |
| "rescale_factor": 0.00392156862745098, | |
| "return_tensors": null, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |