Instructions to use sshleifer/distilbart-xsum-12-6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use sshleifer/distilbart-xsum-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-xsum-12-6")# pip install -U transformers accelerate # Load model directly from transformers import AutoTokenizer, AutoModelForSeq2SeqLM tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-xsum-12-6") model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-xsum-12-6", device_map="auto") - Inference
- Notebooks
- Google Colab
- Kaggle
Download flax_model.msgpack from sshleifer/distilbart-xsum-12-6: direct link, hf CLI and curl.
- Browser
- Download file 611 MB
-
https://huggingface.co/sshleifer/distilbart-xsum-12-6/resolve/main/flax_model.msgpack
- Command line
-
hf download hf://sshleifer/distilbart-xsum-12-6/flax_model.msgpack
-
curl -L -o flax_model.msgpack https://huggingface.co/sshleifer/distilbart-xsum-12-6/resolve/main/flax_model.msgpack
611 MB
- Xet hash:
- 1a8bd9ff488cef374d80922f08483e05d713f9d4dd96a30d8fc56f6400012a97
- Size of remote file:
- 611 MB
- SHA256:
- 1b27661f9737ed209e969fa2f0cd36eba40248c3f528070ab911085e9bf07a02
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.