pegasus-large, whence the other checkpoints are fine-tuned:
Each checkpoint is 2.2 GB on disk and 568M parameters.
FP16 is not supported (help/ideas on this appreciated!).
Summarizing xsum in fp32 takes about 400ms/sample, with default parameters on a v100 GPU.
Full replication results and correctly pre-processed data can be found in this Issue.
Distilled checkpoints are described in this paper.
Examples
Script to fine-tune pegasus
  on the XSUM dataset. Data download instructions at examples/pytorch/summarization/.
FP16 is not supported (help/ideas on this appreciated!).
The adafactor optimizer is recommended for pegasus fine-tuning.
Implementation Notes
All models are transformer encoder-decoders with 16 layers in each component.
The implementation is completely inherited from [BartForConditionalGeneration]
