Instructions to use modularStarEncoder/ModularStarEncoder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use modularStarEncoder/ModularStarEncoder with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="modularStarEncoder/ModularStarEncoder", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("modularStarEncoder/ModularStarEncoder", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- bb7c4db0fb15eec4822124d3e2f99c6ac6c33980d93f8134b193406724438f87
- Size of remote file:
- 2.21 GB
- SHA256:
- e4b0dccbcff22e5d1103d4850e0d42c224800d8ece45c243a436e2b013983110
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.