Instructions to use cminst/Llama-3.2-11B-VisionEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use cminst/Llama-3.2-11B-VisionEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("image-feature-extraction", model="cminst/Llama-3.2-11B-VisionEncoder")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("cminst/Llama-3.2-11B-VisionEncoder") model = AutoModel.from_pretrained("cminst/Llama-3.2-11B-VisionEncoder") - Notebooks
- Google Colab
- Kaggle
| { | |
| "do_convert_rgb": true, | |
| "do_normalize": true, | |
| "do_pad": true, | |
| "do_rescale": true, | |
| "do_resize": true, | |
| "image_mean": [ | |
| 0.48145466, | |
| 0.4578275, | |
| 0.40821073 | |
| ], | |
| "image_processor_type": "MllamaImageProcessor", | |
| "image_std": [ | |
| 0.26862954, | |
| 0.26130258, | |
| 0.27577711 | |
| ], | |
| "max_image_tiles": 4, | |
| "processor_class": "MllamaProcessor", | |
| "resample": 2, | |
| "rescale_factor": 0.00392156862745098, | |
| "size": { | |
| "height": 448, | |
| "width": 448 | |
| } | |
| } | |