Instructions to use sshleifer/distilbart-xsum-12-1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-12-1 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-12-1")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-1") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-12-1", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/distilbart-xsum-12-1: direct link, hf CLI and curl.
- Browser
- Download file 443 MB
-
https://huggingface.co/sshleifer/distilbart-xsum-12-1/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/distilbart-xsum-12-1/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/distilbart-xsum-12-1/resolve/main/pytorch_model.bin
443 MB
- Xet hash:
- f38d5fd686ab321d731bad99808d4d8cbb035b708906ceb18df947c05a22caca
- Size of remote file:
- 443 MB
- SHA256:
- 0a8dcdab3e8a565dfac21237399c9e5f8a3bbfb404b0b7996be0420cfbb5f447
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