Instructions to use nov3630/RLRetriever with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use nov3630/RLRetriever with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="nov3630/RLRetriever")# Load model directly from transformers import AutoTokenizer, AutoModel tokenizer = AutoTokenizer.from_pretrained("nov3630/RLRetriever") model = AutoModel.from_pretrained("nov3630/RLRetriever") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 65bbfb60229f4ef3ffba1d4a4a4d4cf6805ea6d5f46c024e63889f61c53d2c7d
- Size of remote file:
- 504 MB
- SHA256:
- 018cff5df7d64f97fed4893fe59c9bea2800f8b61596cf9f9f5303339b9df1cf
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.