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--- |
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license: apache-2.0 |
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language: |
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- en |
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pipeline_tag: text-generation |
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base_model: |
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- openai-community/gpt2 |
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library_name: transformers |
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datasets: |
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- CodeferSystem/GPT2-Hacker-password-generator-dataset |
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tags: |
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- cybersecurity |
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- passwords |
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--- |
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# GPT-2 Hacker password generator. |
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This model can generate hacker passwords. |
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# Fine-tuning results |
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Number of epochs: 5 |
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Number of steps: 3125 |
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Loss: 0.519600 |
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Fine-tuning time: almost 34:39 on Nvidia Geforce RTX 4060 8 GB GPU (laptop) |
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Fine-tuned on 20k examples of 128 tokens. |
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# Using the model |
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Use this code: |
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```python |
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from transformers import GPT2Tokenizer, GPT2LMHeadModel |
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import torch |
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model_name = "CodeferSystem/GPT2-Hacker-password-generator" |
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# Load the pre-trained GPT-2 model and tokenizer from the specified directory |
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tokenizer = GPT2Tokenizer.from_pretrained(model_name) # Load standard GPT-2 tokenizer |
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model = GPT2LMHeadModel.from_pretrained(model_name) # Load fine-tuned GPT-2 model |
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# Function to generate an answer based on a given question |
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def generate_answer(question): |
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# Create a prompt by formatting the question for the model |
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prompt = f"Question: {question}\nAnswer:" |
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# Encode the prompt into input token IDs suitable for the model |
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input_ids = tokenizer.encode(prompt, return_tensors="pt") |
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# Set the model to evaluation mode |
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model.eval() |
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# Generate the output without calculating gradients (for efficiency) |
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with torch.no_grad(): |
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output = model.generate( |
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input_ids, # Provide the input tokens |
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max_length=50, # Set the maximum length of the generated text |
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num_return_sequences=1, # Only return one sequence of text |
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no_repeat_ngram_size=2, # Prevent repeating n-grams (sequences of n words) |
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do_sample=True, # Enable sampling (randomized generation) |
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top_k=50, # Limit the model's choices to the top 50 probable words |
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top_p=0.95, # Use nucleus sampling (the cumulative probability distribution) |
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temperature=2.0, # Control the randomness/creativity of the output |
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pad_token_id=tokenizer.eos_token_id # Specify the padding token ID (EOS token in this case) |
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) |
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# Decode the generated token IDs back to a string and strip any special tokens |
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generated_text = tokenizer.decode(output[0], skip_special_tokens=True) |
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# Extract the part after "Answer:" to get the model's generated answer |
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answer = generated_text.split("Answer:")[-1].strip() |
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return answer |
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# Example usage |
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question = "generate password." |
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print(generate_answer(question)) # Print the generated password |
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``` |
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# Example passwords generation with this model: |
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### If you write a prompt like "Generate a hacker password." - the password will be something like this (5 examples): |
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- 0Qk=4CdPQQv0>n1K |
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- o4K*mQq9>Zu |
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- e5vx=KqE_j>kFj&* |
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- xD2PZ5@kz_hFq|W= |
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- h=rZ?^<Qp~7&z7XZ |
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## Fine-tuned data |
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The dataset on which the model was fine-tuned was uploaded to the public. |
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CodeferSystem/GPT2-Hacker-password-generator-dataset |
