Instructions to use google/long-t5-local-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use google/long-t5-local-base with Transformers:
# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("google/long-t5-local-base") model = AutoModelForSeq2SeqLM.from_pretrained("google/long-t5-local-base", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
Adding `safetensors` variant of this model
This is an automated PR created with https://huggingface.co/spaces/safetensors/convert This new file is equivalent to `pytorch_model.bin` but safe in the sense that no arbitrary code can be put into it. These files also happen to load much faster than their pytorch counterpart: https://colab.research.google.com/github/huggingface/notebooks/blob/main/safetensors_doc/en/speed.ipynb The widgets on your model page will run using this model even if this is not merged making sure the file actually works. If you find any issues: please report here: https://huggingface.co/spaces/safetensors/convert/discussions Feel free to ignore this PR.9258113
-
Adding generation config file(s)
8484523
-
Fix code example
e040d65
-
Update README.md
e6218cc
-
Copy tokenizer files from t5-base
6121e6d
-
Fix tie_word_embeddings
a0ed3a3
-
Add PT checkpoint
920f64d -
Update n_positions
Daniel Stancl1253f09 -
Specify encoder attn type
Daniel Stancl0d11a24 -
Add config and flax ckpt
Daniel Stancl5261045 -
Update README.md
00e56fe -
Create README.md
94846bf -
initial commit
c261246