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:
- 06752f724294a49e0e205a2def4c388a3143bc38f47467f2ee1925ac45bf9bed
- Size of remote file:
- 1.42 MB
- SHA256:
- 898fbc0acf6454f7ccfeff61f07e37b9efd469ad5b52f62ff61085f9a8cd08e8
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