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:
- 08204ee066ba7935072a034aa9e5e86c7d4729a04191dd67bd1564786fd3fe4e
- Size of remote file:
- 9.55 GB
- SHA256:
- 68eaf6d647056197965bdfbbfd8eaabf430977238f00102da6960de44a0c7955
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