Instructions to use sshleifer/distilbart-xsum-1-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-1-1 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-1-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-1-1") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-1-1", device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/distilbart-xsum-1-1: direct link, hf CLI and curl.
- Browser
- Download file 332 MB
-
https://huggingface.co/sshleifer/distilbart-xsum-1-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/distilbart-xsum-1-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/distilbart-xsum-1-1/resolve/main/pytorch_model.bin
332 MB
- Xet hash:
- dfbcad22f621410db2a50459ee603dc1792b2fd8e5b75372cd19a59176a44e23
- Size of remote file:
- 332 MB
- SHA256:
- 717ff3539c66188763cd053f1f5f6de6236d3f988d65455e9fa668a6a7603a9d
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