Feature Extraction
Transformers
Safetensors
English
penguinvl_vision_encoder
multi-modal
large-language-model
vision-language-model
vision-encoder
custom_code
Instructions to use Cyril666/Penguin-Encoder-Init with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Cyril666/Penguin-Encoder-Init with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Cyril666/Penguin-Encoder-Init", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Cyril666/Penguin-Encoder-Init", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- be44acf972cb2f41cb26da65532b2fb57a052256c8f1393a779eb9d36e8dfb90
- Size of remote file:
- 1.76 GB
- SHA256:
- 8c42f10b42add06635ad0d38092114ad07ec24931290aaf12105fa4503a74ab2
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