Feature Extraction
Transformers
Safetensors
English
penguinvl_vision_encoder
multi-modal
large-language-model
vision-language-model
vision-encoder
custom_code
Instructions to use tencent/Penguin-Encoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use tencent/Penguin-Encoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="tencent/Penguin-Encoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("tencent/Penguin-Encoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
File size: 467 Bytes
dda5e62 | 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 | {
"auto_map": {
"AutoImageProcessor": "image_processing_penguinvl.PenguinVLImageProcessor"
},
"do_convert_rgb": true,
"do_normalize": true,
"do_rescale": true,
"do_resize": true,
"image_mean": [
0.5,
0.5,
0.5
],
"image_processor_type": "PenguinVLImageProcessor",
"image_std": [
0.5,
0.5,
0.5
],
"max_tokens": 16384,
"min_tokens": 16,
"patch_size": 14,
"resample": 3,
"rescale_factor": 0.00392156862745098
}
|