Instructions to use Riyagarg01/Practice1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Riyagarg01/Practice1 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Riyagarg01/Practice1") model = AutoModelForSeq2SeqLM.from_pretrained("Riyagarg01/Practice1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download new_model/checkpoint-2000/optimizer.pt from Riyagarg01/Practice1: direct link, hf CLI and curl.
- Browser
- Download file 1.12 GB
-
https://huggingface.co/Riyagarg01/Practice1/resolve/main/new_model/checkpoint-2000/optimizer.pt
- Command line
-
hf download hf://Riyagarg01/Practice1/new_model/checkpoint-2000/optimizer.pt
-
curl -L -o optimizer.pt https://huggingface.co/Riyagarg01/Practice1/resolve/main/new_model/checkpoint-2000/optimizer.pt
1.12 GB
- Xet hash:
- f2d6c1f4dea718980163623d976c02f5293555154973f40ec8a87843d0f590f1
- Size of remote file:
- 1.12 GB
- SHA256:
- 25f1e5af314928c84becf5df52745acc03fb709b2ca197e6d6349e5eaa9beaf9
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.