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:
# pip install -U transformers accelerate # 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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download model.onnx_data from Sashavav/rag-resource-allocator: direct link, hf CLI and curl.
- Browser
- Download file 2.39 GB
-
https://huggingface.co/Sashavav/rag-resource-allocator/resolve/main/model.onnx_data
- Command line
-
hf download hf://Sashavav/rag-resource-allocator/model.onnx_data
-
curl -L -o model.onnx_data https://huggingface.co/Sashavav/rag-resource-allocator/resolve/main/model.onnx_data
2.39 GB
- Xet hash:
- 8b18321773bfd36804a5f8aafbbc87c704d00a9e49c0d38f08ba2b88f65ae6eb
- Size of remote file:
- 2.39 GB
- SHA256:
- f18b52c91a2e73d229fc18e57578125e52846db995832e8d4ff28ffa306bb6cd
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