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:
- 8b18321773bfd36804a5f8aafbbc87c704d00a9e49c0d38f08ba2b88f65ae6eb
- Size of remote file:
- 2.39 GB
- SHA256:
- f18b52c91a2e73d229fc18e57578125e52846db995832e8d4ff28ffa306bb6cd
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