Instructions to use vsty/weights with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use vsty/weights with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("fill-mask", model="vsty/weights")# Load model directly from transformers import AutoTokenizer, AutoModelForMaskedLM tokenizer = AutoTokenizer.from_pretrained("vsty/weights") model = AutoModelForMaskedLM.from_pretrained("vsty/weights", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 7d92404efb8f2cc03be096de880079815dce0c882fff52974eac022a45a72429
- Size of remote file:
- 1.04 kB
- SHA256:
- 943fc684d36d23f6a5065d3fb419b60839f8240f7629af946be096e56c074991
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.