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
| { | |
| "_attn_implementation_autoset": false, | |
| "architectures": [ | |
| "MllamaVisionModel" | |
| ], | |
| "attention_heads": 16, | |
| "dtype": "float32", | |
| "hidden_act": "gelu", | |
| "hidden_size": 1280, | |
| "image_size": 448, | |
| "initializer_range": 0.02, | |
| "intermediate_layers_indices": [ | |
| 3, | |
| 7, | |
| 15, | |
| 23, | |
| 30 | |
| ], | |
| "intermediate_size": 5120, | |
| "max_num_tiles": 4, | |
| "model_type": "mllama_vision_model", | |
| "norm_eps": 1e-05, | |
| "num_channels": 3, | |
| "num_global_layers": 8, | |
| "num_hidden_layers": 32, | |
| "patch_size": 14, | |
| "supported_aspect_ratios": [ | |
| [ | |
| 1, | |
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| ], | |
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| ] | |
| ], | |
| "transformers_version": "4.57.6", | |
| "vision_output_dim": 7680 | |
| } | |