Update Ungpt

Ungpt

@@ -1,17 +1,14 @@
-from transformers import GPT2LMHeadModel, GPT2Tokenizer
-
-# Load the pre-trained GPT-2 model and tokenizer
-model = GPT2LMHeadModel.from_pretrained('gpt2')
-tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
-
-# Add a new token for worm
-special_tokens_dict = {'additional_special_tokens': ['<worm>']}
-num_added_tokens = tokenizer.add_special_tokens(special_tokens_dict)
-model.resize_token_embeddings(len(tokenizer))
-
-# Fine-tune the model on your worm-related dataset
-# (This part is not included in this example as it requires a dataset)
-
-# Save the fine-tuned model and tokenizer
-model.save_pretrained('wormgpt')
-tokenizer.save_pretrained('wormgpt')
+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)