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
| library_name: transformers | |
| base_model: unsloth/Llama-3.2-11B-Vision | |
| tags: | |
| - mllama | |
| - vision | |
| - vision-encoder | |
| # Llama-3.2-11B Vision Encoder | |
| This repository contains only the `MllamaVisionModel` weights extracted from | |
| `unsloth/Llama-3.2-11B-Vision`. It intentionally excludes the text decoder and language | |
| model weights. | |
| Load it with: | |
| ```python | |
| from transformers import MllamaVisionModel | |
| vision = MllamaVisionModel.from_pretrained("cminst/Llama-3.2-11B-VisionEncoder") | |
| ``` | |
| For LEGATO configs, set `encoder_pretrained_model_name_or_path` to: | |
| ```text | |
| cminst/Llama-3.2-11B-VisionEncoder | |
| ``` | |