import torch from transformers import GPT2Tokenizer, GPT2LMHeadModel tokenizer = GPT2Tokenizer.from_pretrained('gpt2') model = GPT2LMHeadModel.from_pretrained('gpt2') tokenizer.add_special_tokens({'pad_token': '[PAD]'}) tokenizer.add_special_tokens({'eos_token': '<|End|>'}) special_tokens = { "additional_special_tokens": ["<|USER|>", "<|SYSTEM|>", "<|ASSISTANT|>"] } tokenizer.add_special_tokens(special_tokens) model.resize_token_embeddings(len(tokenizer)) model.load_state_dict(torch.load("pytorch_model.bin")) device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device) def generate_text(model, tokenizer, prompt, max_length=1024): prompt = f'<|SYSTEM|> You are a helpful AI designed to answer questions <|USER|> {prompt} <|ASSISTANT|> ' input_ids = tokenizer.encode(prompt, add_special_tokens=True, return_tensors="pt").to(device) attention_mask = torch.ones_like(input_ids).to(device) output = model.generate(input_ids, max_length=max_length, do_sample=True, top_k=50, top_p=0.30, pad_token_id=tokenizer.pad_token_id, eos_token_id=tokenizer.eos_token_id, attention_mask=attention_mask) output_ids = tokenizer.decode(output[0], skip_special_tokens=False) assistant_token_index = output_ids.index('<|ASSISTANT|>') + len('<|ASSISTANT|>') next_token_index = output_ids.find('<|', assistant_token_index) output_ids = output_ids[assistant_token_index:next_token_index] return output_ids # Loop to interact with the model while True: prompt = input("Enter a prompt (or 'q' to quit): ") if prompt == "q": break output_text = generate_text(model, tokenizer, prompt) print(output_text)