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
File size: 402 Bytes
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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
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