Instructions to use ShynBui/s3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ShynBui/s3 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("question-answering", model="ShynBui/s3")# Load model directly from transformers import AutoTokenizer, AutoModelForQuestionAnswering tokenizer = AutoTokenizer.from_pretrained("ShynBui/s3") model = AutoModelForQuestionAnswering.from_pretrained("ShynBui/s3", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from ShynBui/s3: direct link, hf CLI and curl.
- Browser
- Download file 431 MB
-
https://huggingface.co/ShynBui/s3/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://ShynBui/s3/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/ShynBui/s3/resolve/main/pytorch_model.bin
431 MB
- Xet hash:
- 838345340c65a36536420e794b14bc3c0506332327597d9d309e43a3f07ece2d
- Size of remote file:
- 431 MB
- SHA256:
- 1756c58976511386edf097534ceb271fec9d0b74c68d42809f36b27e565c264b
路
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.