Instructions to use SoLID/t5_tod_large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use SoLID/t5_tod_large with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("SoLID/t5_tod_large") model = AutoModelForSeq2SeqLM.from_pretrained("SoLID/t5_tod_large", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from SoLID/t5_tod_large: direct link, hf CLI and curl.
- Browser
- Download file 3.13 GB
-
https://huggingface.co/SoLID/t5_tod_large/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://SoLID/t5_tod_large/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/SoLID/t5_tod_large/resolve/main/pytorch_model.bin
3.13 GB
- Xet hash:
- 5af23c97aad478dbc441df98fde5cdf68cd48af28063c89833e8a873e031fb04
- Size of remote file:
- 3.13 GB
- SHA256:
- 83d4eb5d54e831a6e0b36cd06b9c4f23d5d4ff722c5cb7f2d8326877a0b97cdb
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.