Decoding
strategies like greedy search and contrastive search return a single output sequence.

Save a custom decoding strategy with your model
If you would like to share your fine-tuned model with a specific generation configuration, you can:
* Create a [GenerationConfig] class instance
* Specify the decoding strategy parameters
* Save your generation configuration with [GenerationConfig.save_pretrained], making sure to leave its config_file_name argument empty
* Set push_to_hub to True to upload your config to the model's repo
thon

from transformers import AutoModelForCausalLM, GenerationConfig
model = AutoModelForCausalLM.from_pretrained("my_account/my_model")  # doctest: +SKIP
generation_config = GenerationConfig(
     max_new_tokens=50, do_sample=True, top_k=50, eos_token_id=model.config.eos_token_id
 )
generation_config.save_pretrained("my_account/my_model", push_to_hub=True)  # doctest: +SKIP

You can also store several generation configurations in a single directory, making use of the config_file_name
argument in [GenerationConfig.save_pretrained].