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:
- f04bc4da16f0400bf32be2e4d201af67c0dd410227dc4541073ff26311220d32
- Size of remote file:
- 2.39 GB
- SHA256:
- 5bec928eb91ab17e4b8cf17a71a3e21070bb647b3ed4cc185a0288b745c9b9f6
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