Instructions to use sshleifer/distilbart-xsum-6-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-6-6 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-6-6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-6-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-6-6", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/distilbart-xsum-6-6: direct link, hf CLI and curl.
- Browser
- Download file 460 MB
-
https://huggingface.co/sshleifer/distilbart-xsum-6-6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/distilbart-xsum-6-6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/distilbart-xsum-6-6/resolve/main/pytorch_model.bin
460 MB
- Xet hash:
- c79ba921931043ee8372b115e4cf4e64d08ad893319cb3ba768bc328c477330f
- Size of remote file:
- 460 MB
- SHA256:
- 45b52e2ae678b6b76c5d5c05bf6d7e3d8b682af6ea66316f5b425d555fe424fa
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