Instructions to use Lin-Chen/ShareCaptioner with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Lin-Chen/ShareCaptioner with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="Lin-Chen/ShareCaptioner", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("Lin-Chen/ShareCaptioner", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 2126dae449b703f9cd3b8b414df9c9aa4d97eac9d3fa845ee06b305543e4ba74
- Size of remote file:
- 9.99 GB
- SHA256:
- 313bb6ad80bbec562ccd0f01baa0ce7b239b6f68c76d223657499169e04d98b7
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.