

from transformers import AutoModelForSeq2SeqLM, AutoTokenizer, GenerationConfig
tokenizer = AutoTokenizer.from_pretrained("google-t5/t5-small")
model = AutoModelForSeq2SeqLM.from_pretrained("google-t5/t5-small")
translation_generation_config = GenerationConfig(
     num_beams=4,
     early_stopping=True,
     decoder_start_token_id=0,
     eos_token_id=model.config.eos_token_id,
     pad_token=model.config.pad_token_id,
 )
Tip: add push_to_hub=True to push to the Hub
translation_generation_config.save_pretrained("/tmp", "translation_generation_config.json")
You could then use the named generation config file to parameterize generation
generation_config = GenerationConfig.from_pretrained("/tmp", "translation_generation_config.json")
inputs = tokenizer("translate English to French: Configuration files are easy to use!", return_tensors="pt")
outputs = model.generate(**inputs, generation_config=generation_config)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
['Les fichiers de configuration sont faciles à utiliser!']