thon

from transformers import AutoModelForCausalLM, AutoTokenizer
prompt = "I look forward to"
checkpoint = "distilbert/distilgpt2"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
inputs = tokenizer(prompt, return_tensors="pt")
model = AutoModelForCausalLM.from_pretrained(checkpoint)
outputs = model.generate(**inputs)
tokenizer.batch_decode(outputs, skip_special_tokens=True)
['I look forward to seeing you all again!\n\n\n\n\n\n\n\n\n\n\n']

Contrastive search
The contrastive search decoding strategy was proposed in the 2022 paper A Contrastive Framework for Neural Text Generation.