Create README.md

README.md

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+---
+license: apache-2.0
+language:
+- en
+pipeline_tag: text-generation
+---
+# 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 3674.21 examples of 128 tokens.
+
+# Using the model
+Use this code:
+
+```python
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+import torch
+
+model_name = "Codefer/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