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: 293 Bytes
45b3a8c 4c70d7b 45b3a8c 4c70d7b 45b3a8c | 1 2 3 4 5 6 7 8 9 10 | {
"source_model_dir": "/home/Sashavav/git/mef_models/models/jina_reranker",
"hf_model_id": null,
"tokenizer_source": null,
"output_dir": "outputs/base/final",
"onnx_path": "outputs/base/final/model.onnx",
"opset": 18,
"model_class": "JinaForRanking",
"loaded_from_hub": false
} |