Instructions to use winglian/t5-large-flan-cot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use winglian/t5-large-flan-cot with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("winglian/t5-large-flan-cot") model = AutoModelForSeq2SeqLM.from_pretrained("winglian/t5-large-flan-cot", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from winglian/t5-large-flan-cot: direct link, hf CLI and curl.
- Browser
- Download file 1.48 GB
-
https://huggingface.co/winglian/t5-large-flan-cot/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://winglian/t5-large-flan-cot/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/winglian/t5-large-flan-cot/resolve/main/pytorch_model.bin
1.48 GB
- Xet hash:
- 0dc16f74c943107e07e3139379674b7a4bbea8bef54694fa782a1835e4665b92
- Size of remote file:
- 1.48 GB
- SHA256:
- 945fcac48261aea62dec911c7f811a4a879d3f5b532c15b6f71c35d066f60204
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.