Instructions to use jienengchen/ViTamin-S with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use jienengchen/ViTamin-S with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="jienengchen/ViTamin-S", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("jienengchen/ViTamin-S", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 98df70e191f6404cc7d56fa88c1eebb7fd3da2f31f835b12670f207386ea1462
- Size of remote file:
- 250 MB
- SHA256:
- 3a1f0a47c185e3c718c999ac81054fb6c5556df6a5f203c1b5647ddaeb72d2c2
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.