Feature Extraction
Transformers
PyTorch
English
clip
vision-language
probabilistic
uncertainty
custom_code
Instructions to use aalto-ml/BayesVLM-Large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aalto-ml/BayesVLM-Large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aalto-ml/BayesVLM-Large", trust_remote_code=True)# Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("aalto-ml/BayesVLM-Large", trust_remote_code=True) model = AutoModel.from_pretrained("aalto-ml/BayesVLM-Large", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 8c7d9caed43287f7c532d46a78ecdf29933e5bd17aed8d1f45d13534ef824ebe
- Size of remote file:
- 1.72 GB
- SHA256:
- 0960f79969a7a738afc3783e6b6bdf515924112942b4fc6191665984778ffd46
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