| 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. |