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