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
| library_name: transformers | |
| tags: | |
| - onnx | |
| - text-ranking | |
| # Jina reranker export | |
| This folder was exported from `/home/Sashavav/git/mef_models/models/jina_reranker`. | |
| ## Artifacts | |
| - Model weights and config in the Hugging Face format | |
| - Tokenizer files saved with `save_pretrained` | |
| - ONNX model at `model.onnx` | |
| ## Export details | |
| - ONNX opset: 18 | |
| - Exported at: 2026-05-19T08:56:08.644484+00:00 | |