Some key configuration differences:
static, sinusoidal position embeddings
the model starts generating with pad_token_id (which has 0 token_embedding) as the prefix.
more beams are used (num_beams=8)
All pretrained pegasus checkpoints are the same besides three attributes: tokenizer.model_max_length (maximum
  input size), max_length (the maximum number of tokens to generate) and length_penalty.
The code to convert checkpoints trained in the author's repo can be
  found in convert_pegasus_tf_to_pytorch.py.
Usage Example
```python
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
import torch
src_text = [
...     """ PG&E stated it scheduled the blackouts in response to forecasts for high winds amid dry conditions. The aim is to reduce the risk of wildfires. Nearly 800 thousand customers were scheduled to be affected by the shutoffs which were expected to last through at least midday tomorrow."""
