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
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:
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:
cminst/Llama-3.2-11B-VisionEncoder
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Model tree for cminst/Llama-3.2-11B-VisionEncoder
Base model
meta-llama/Llama-3.2-11B-Vision Finetuned
unsloth/Llama-3.2-11B-Vision