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:
- 2ba6ca8279c9f4bb731041770be3634fa72bdadf61be36d53059785b1d722ef3
- Size of remote file:
- 2.39 GB
- SHA256:
- e3a059d63c7f00ba4ecbd097fc6c32246fd605e41a22ec957fe9eec7fc293936
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