transformers/examples/seq2seq/save_randomly_initialized_model.py
Suraj Patil eab5f59682
[s2s] add create student script (#7290)
Co-authored-by: Suraj Patil <surajp815@gmail.com>
Co-authored-by: Sam Shleifer <sshleifer@gmail.com>
2020-09-27 15:10:46 -04:00

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Python
Executable File

#!/usr/bin/env python
import fire
from transformers import AutoConfig, AutoModelForSeq2SeqLM, AutoTokenizer
def save_randomly_initialized_version(config_name: str, save_dir: str, **config_kwargs):
"""Save a randomly initialized version of a model using a pretrained config.
Args:
config_name: which config to use
save_dir: where to save the resulting model and tokenizer
config_kwargs: Passed to AutoConfig
Usage::
save_randomly_initialized_version("facebook/bart-large-cnn", "distilbart_random_cnn_6_3", encoder_layers=6, decoder_layers=3, num_beams=3)
"""
cfg = AutoConfig.from_pretrained(config_name, **config_kwargs)
model = AutoModelForSeq2SeqLM.from_config(cfg)
model.save_pretrained(save_dir)
AutoTokenizer.from_pretrained(config_name).save_pretrained(save_dir)
return model
if __name__ == "__main__":
fire.Fire(save_randomly_initialized_version)