Instructions to use sshleifer/distilbart-xsum-9-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-9-6 with Transformers:
# Use a pipeline as a high-level helper # Warning: Pipeline type "summarization" is no longer supported in transformers v5. # You must load the model directly (see below) or downgrade to v4.x with: # pip install "transformers<5.0.0" from transformers import pipeline pipe = pipeline("summarization", model="sshleifer/distilbart-xsum-9-6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-9-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-9-6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Commit History
-
add flax model
28dfbe9
-
Update config.json
1d02002
-
Update README.md
6cb86ba
-
Update README.md
3bb3c27 -
Update README.md
3245d03 -
Update README.md
7119493
-
Update README.md
201e392
-
Update config.json
846ad59 -
Update merges.txt
1ad23dc -
Update pytorch_model.bin
d859fc2 -
Update tokenizer_config.json
e73bd62 -
Update vocab.json
a06af7b -
initial commit
b8d5fb9