Update README.md

README.md

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----
-license: mit
----
+---
+license: mit
+language:
+- en
+---
+
+## Model Details
+**Model Name:** `Canstralian/pentest_ai`  
+**Base Model:** `WhiteRabbitNeo/WhiteRabbitNeo-13B-v1`  
+**Model Version:** `1.0.0`  
+
+## Intended Use
+The **Canstralian/pentest_ai** model is specifically designed for **penetration testing** applications. It assists security professionals and ethical hackers in automating and enhancing security assessment tasks. The model is well-suited for generating reconnaissance strategies, conducting vulnerability assessments, report generation, and automating scripting tasks related to penetration testing.
+
+## How to Use
+To utilize the **Canstralian/pentest_ai** model, ensure you have the `transformers` library installed, and load the model as follows:
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load the tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("Canstralian/pentest_ai")
+model = AutoModelForCausalLM.from_pretrained("Canstralian/pentest_ai")
+
+# Example usage
+input_text = "Generate a reconnaissance plan for the target network."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs)
+generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(generated_text)