Load the pre-trained WormGPT model and tokenizer

model = GPT2LMHeadModel.from_pretrained("wormgpt") tokenizer = GPT2Tokenizer.from_pretrained("wormgpt")

def generate_text(prompt, max_length=50): input_ids = tokenizer.encode(prompt, return_tensors="pt") output = model.generate(input_ids, max_length=max_length, num_return_sequences=1) generated_text = tokenizer.decode(output[0], skip_special_tokens=True) return generated_text