Instructions to use Sashavav/rag-resource-allocator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Sashavav/rag-resource-allocator with Transformers:
# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("Sashavav/rag-resource-allocator", trust_remote_code=True) model = AutoModel.from_pretrained("Sashavav/rag-resource-allocator", trust_remote_code=True) - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 85b8c5e45a4c9f1b206ca121cd8207247dd118672827196ad9fe9869b9fafe65
- Size of remote file:
- 11.4 MB
- SHA256:
- 4e95945ab0cef486709f760b81efcc7a6e75747f9165d13ead29159737455803
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