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
File size: 477 Bytes
621808b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | {
"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
}
}
|