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