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: 373 Bytes
45b3a8c | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | ---
library_name: transformers
tags:
- onnx
- text-ranking
---
# Jina reranker export
This folder was exported from `outputs/checkpoint-50`.
## 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: 17
- Exported at: 2026-05-18T10:27:02.000729+00:00
|