Instructions to use Jeevesh8/t5-small-cogs_10 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Jeevesh8/t5-small-cogs_10 with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("Jeevesh8/t5-small-cogs_10") model = AutoModelForSeq2SeqLM.from_pretrained("Jeevesh8/t5-small-cogs_10", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from Jeevesh8/t5-small-cogs_10: direct link, hf CLI and curl.
- Browser
- Download file 242 MB
-
https://huggingface.co/Jeevesh8/t5-small-cogs_10/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://Jeevesh8/t5-small-cogs_10/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/Jeevesh8/t5-small-cogs_10/resolve/main/flax_model.msgpack
242 MB
- Xet hash:
- 906b7b7e9983c5ac1aaaa39258e8af1581db7d525d383d09f1678b7d2cd3426c
- Size of remote file:
- 242 MB
- SHA256:
- 69a7e3e2311a7f6460c9517a0c057d035caf7992733b64a4d92c1eb704d9696b
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.