Create G

G

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+import torch
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+# Load the pre-trained models and tokenizers
+wormgpt_model = GPT2LMHeadModel.from_pretrained("wormgpt")
+wormgpt_tokenizer = GPT2Tokenizer.from_pretrained("wormgpt")
+fraudgpt_model = GPT2LMHeadModel.from_pretrained("fraudgpt")
+fraudgpt_tokenizer = GPT2Tokenizer.from_pretrained("fraudgpt")
+xxxgpt_model = GPT2LMHeadModel.from_pretrained("xxxgpt")
+xxxgpt_tokenizer = GPT2Tokenizer.from_pretrained("xxxgpt")
+evilgpt_model = GPT2LMHeadModel.from_pretrained("evilgpt")
+evilgpt_tokenizer = GPT2Tokenizer.from_pretrained("evilgpt")
+# Function to generate text from a given prompt using the specified model
+def generate_text(prompt, model, tokenizer, max_length=50):
+ input_ids = tokenizer.encode(prompt, return_tensors="pt")
+ output = model.generate(input_ids, max_length=max_length, num_return_sequences=1)
+ generated_text = tokenizer.decode(output[0], skip_special_tokens=True)
+ return generated_text
+# Function to generate text from a given prompt using all four models
+def generate_uncensored_text(prompt, max_length=50):
+ wormgpt_text = generate_text(prompt, wormgpt_model, wormgpt_tokenizer, max_length)
+ fraudgpt_text = generate_text(prompt, fraudgpt_model, fraudgpt_tokenizer, max_length)
+ xxxgpt_text = generate_text(prompt, xxxgpt_model, xxxgpt_tokenizer, max_length)
+ evilgpt_text = generate_text(prompt, evilgpt_model, evilgpt_tokenizer, max_length)
+ return wormgpt_text + "\n" + fraudgpt_text + "\n" + xxxgpt_text + "\n" + evilgpt_text
+# Example usage
+prompt = "I want to generate some uncensored text."
+uncensored_text = generate_uncensored_text(prompt)
+print(uncensored_text)