Instructions to use sshleifer/distilbart-cnn-12-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-cnn-12-6 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-12-6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download pytorch_model.bin from sshleifer/distilbart-cnn-12-6: direct link, hf CLI and curl.
- Browser
- Download file 1.22 GB
-
https://huggingface.co/sshleifer/distilbart-cnn-12-6/resolve/main/pytorch_model.bin
- Command line
-
hf download hf://sshleifer/distilbart-cnn-12-6/pytorch_model.bin
-
curl -L -o pytorch_model.bin https://huggingface.co/sshleifer/distilbart-cnn-12-6/resolve/main/pytorch_model.bin
1.22 GB
- Xet hash:
- 12fafd1567394dd10c30d963d02ebf70568c2cd509bad3e0a6af52183d984b11
- Size of remote file:
- 1.22 GB
- SHA256:
- 3bac65d18c99463302d12ca75c2220ea714f9c81ce235f205fa818efe71df6ea
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