Feature Extraction
Transformers
PyTorch
English
clip
vision-language
probabilistic
uncertainty
custom_code
Instructions to use aalto-ml/BayesVLM-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use aalto-ml/BayesVLM-Base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="aalto-ml/BayesVLM-Base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModel processor = AutoProcessor.from_pretrained("aalto-ml/BayesVLM-Base", trust_remote_code=True) model = AutoModel.from_pretrained("aalto-ml/BayesVLM-Base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download tokenizer.json from aalto-ml/BayesVLM-Base: direct link, hf CLI and curl.
- Browser
- Download file 2.22 MB
-
https://huggingface.co/aalto-ml/BayesVLM-Base/resolve/main/tokenizer.json
- Command line
-
hf download hf://aalto-ml/BayesVLM-Base/tokenizer.json
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curl -L -o tokenizer.json https://huggingface.co/aalto-ml/BayesVLM-Base/resolve/main/tokenizer.json
2.22 MB
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