| import torch | |
| from transformers import GPT2Tokenizer, GPT2LMHeadModel | |
| model_dir = "./nova1_model" | |
| tokenizer = GPT2Tokenizer.from_pretrained(model_dir) | |
| model = GPT2LMHeadModel.from_pretrained(model_dir) | |
| model.eval() | |
| def generate_text(prompt, max_length=100): | |
| inputs = tokenizer(prompt, return_tensors="pt") | |
| with torch.no_grad(): | |
| outputs = model.generate( | |
| **inputs, | |
| max_length=max_length, | |
| do_sample=True, | |
| top_p=0.95, | |
| top_k=50, | |
| temperature=0.8, | |
| pad_token_id=tokenizer.eos_token_id, | |
| ) | |
| return tokenizer.decode(outputs[0], skip_special_tokens=True) | |
| if __name__ == "__main__": | |
| print("Chat NOVA ready. Type your prompt (type 'exit' to quit):") | |
| while True: | |
| prompt = input(">> ") | |
| if prompt.lower() == "exit": | |
| break | |
| response = generate_text(prompt) | |
| print(response) | |