Instructions to use Vivek/gptneo_hellaswag with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Vivek/gptneo_hellaswag with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForMultipleChoice tokenizer = AutoTokenizer.from_pretrained("Vivek/gptneo_hellaswag") model = AutoModelForMultipleChoice.from_pretrained("Vivek/gptneo_hellaswag", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Vivek/gptneo_hellaswag: direct link, hf CLI and curl.
- Browser
- Download file 5.26 GB
-
https://huggingface.co/Vivek/gptneo_hellaswag/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Vivek/gptneo_hellaswag/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Vivek/gptneo_hellaswag/resolve/main/flax_model.msgpack
5.26 GB
- Xet hash:
- 215d17ef6c7fe93c0a8775d239b0470a64dd2b03299e931ccbd4b77a0aeea2db
- Size of remote file:
- 5.26 GB
- SHA256:
- cb28e9c02878b25c9e9b89780245951c2fcdc361f75fef67e8f45f2d1aea93b0
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.