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 flax_model.msgpack 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/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/distilbart-xsum-12-1/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/distilbart-xsum-12-1/resolve/main/flax_model.msgpack
443 MB
- Xet hash:
- 94828306c1ba2e5ff97434ed416e7a63f96edf3cda6572f9260455ceb04f1efe
- Size of remote file:
- 443 MB
- SHA256:
- daf00ac44c66c8618480a2c8203311153566d8219a6e4b7a365d813be7e6167d
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