Instructions to use sshleifer/distilbart-cnn-6-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-cnn-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-cnn-6-6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-6-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-6-6", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/distilbart-cnn-6-6: direct link, hf CLI and curl.
- Browser
- Download file 460 MB
-
https://huggingface.co/sshleifer/distilbart-cnn-6-6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/distilbart-cnn-6-6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/distilbart-cnn-6-6/resolve/main/pytorch_model.bin
460 MB
- Xet hash:
- f0c878c43ba3c07ca734c75b172a8d4a70da5f9e77d9f3755a32f322ec89af72
- Size of remote file:
- 460 MB
- SHA256:
- 68f994703e838fff13b9e54ab72d5ae44f184cb55300a7f65f46a7d282e8da23
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