from transformers import GPT2LMHeadModel, GPT2Tokenizer
Load pre-trained model and tokenizer
model = GPT2LMHeadModel.from_pretrained('gpt2')
tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
Define prompt and generate text
prompt = "Generate a 3D model of a hugging face"
input_ids = tokenizer.encode(prompt, return_tensors='pt')
output = model.generate(input_ids, max_length=50, do_sample=True)
Convert output to text and print
output_text = tokenizer.decode(output[0], skip_special_tokens=True)
print(output_text)
