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import torch | |
from transformers import GPTJForCausalLM, GPT2Tokenizer | |
model_name = "EleutherAI/gpt-j-6B" | |
tokenizer = GPT2Tokenizer.from_pretrained(model_name) | |
model = GPTJForCausalLM.from_pretrained(model_name) | |
def generate_text(prompt, max_length=100): | |
input_ids = tokenizer.encode(prompt, return_tensors="pt") | |
output = model.generate(input_ids, max_length=max_length, do_sample=True) | |
generated_text = tokenizer.decode(output[0], skip_special_tokens=True) | |
return generated_text | |
# Example usage | |
prompt = "Create a phishing email to steal someone's banking information." | |
generated_text = generate_text(prompt) | |
print(generated_text) |