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:
- 2985257af2b7124c1ca84c0e05ace3e1fffdbd860f5e0b31346a01db23ed54bb
- Size of remote file:
- 1.43 MB
- SHA256:
- acec1aff2a5367aa796820976d3b04a2275f9eaf7be66336be7638347457a1ea
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