Update README.md

README.md

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 license: mit
+language:
+- en
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+
+# Model Card for AI-Driven Exploit Generation
+
+## Model Details
+
+### Model Description
+The **AI-Driven Exploit Generation** model is designed to assist cybersecurity researchers and penetration testers in simulating exploit generation and analysis. The model leverages state-of-the-art natural language processing (NLP) techniques to understand vulnerabilities and create theoretical exploit scenarios in a controlled and ethical environment. It aids in improving vulnerability management by providing insights into potential exploit paths, fostering proactive defense strategies.
+
+- **Developed by:** Canstralian  
+- **Funded by:** Self-funded  
+- **Shared by:** Canstralian  
+- **Model type:** Transformer-based language model for cybersecurity tasks  
+- **Language(s) (NLP):** English  
+- **License:** MIT License  
+- **Finetuned from model:** [Base model or framework, e.g., GPT-based or similar]
+
+### Model Sources
+- **Repository:** [Insert GitHub or Hugging Face link]  
+- **Demo:** [Insert Space or demo link]  
+
+## Uses
+
+### Direct Use
+The model is intended for controlled environments and ethical cybersecurity research, including:  
+- Exploit simulation and vulnerability testing  
+- Educational tools for security professionals and students  
+- Generating synthetic exploit datasets for training purposes  
+
+### Downstream Use
+- Integration into cybersecurity tools for enhancing penetration testing capabilities  
+- Fine-tuning for specific exploit scenarios in different sectors (e.g., IoT, cloud security)  
+
+### Out-of-Scope Use
+- Malicious use for real-world exploitation or harm  
+- Unauthorized generation of exploits outside ethical and legal standards  
+
+## Bias, Risks, and Limitations
+
+This model comes with risks of misuse due to its potential in simulating exploits. Measures should be taken to limit its access to authorized and trained professionals. It may also have biases based on the dataset it was trained on, focusing more on certain vulnerability types over others.
+
+### Recommendations
+Users should:
+- Ensure the model is used ethically and in compliance with local cybersecurity laws.  
+- Regularly audit the outputs to prevent accidental misuse.  
+- Avoid use cases that could lead to real-world harm.
+
+## How to Get Started with the Model
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+# Load model and tokenizer
+model = AutoModelForCausalLM.from_pretrained("Canstralian/AI-Driven-Exploit-Generation")
+tokenizer = AutoTokenizer.from_pretrained("Canstralian/AI-Driven-Exploit-Generation")
+
+# Generate a sample exploit description
+input_text = "Generate an exploit for a buffer overflow vulnerability in C."
+inputs = tokenizer(input_text, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=150)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
+
+## Training Details
+
+### Training Data
+The model was trained on a curated dataset comprising publicly available vulnerability descriptions, exploit code samples, and cybersecurity research papers.  
+
+### Training Procedure
+The training involved:  
+- Preprocessing the data to remove sensitive or harmful exploit examples  
+- Applying supervised fine-tuning on a base language model  
+- Using ethical guidelines to filter outputs during training  
+
+#### Training Hyperparameters
+- **Learning Rate:** 5e-5  
+- **Batch Size:** 16  
+- **Optimizer:** AdamW  
+- **Precision:** Mixed FP16  
+
+## Evaluation
+
+### Testing Data, Factors & Metrics
+
+#### Testing Data
+The evaluation dataset included synthetic exploit scenarios, vulnerability reports, and sanitized exploit examples.
+
+#### Metrics
+- **Accuracy:** Matching generated exploit descriptions to vulnerability patterns  
+- **Usefulness:** Relevance of generated outputs for vulnerability management  
+- **Ethical Safeguards:** Effectiveness of filters in preventing harmful output  
+
+### Results
+- High accuracy in generating theoretical exploit examples for educational use.  
+- Ethical filters successfully minimized harmful outputs.  
+
+## Environmental Impact
+
+- **Hardware Type:** NVIDIA A100 GPUs  
+- **Hours Used:** 40 hours  
+- **Compute Region:** [Insert region]  
+- **Carbon Emitted:** Calculated using [ML Impact Calculator](https://mlco2.github.io/impact#compute)  
+
+## Citation
+
+**BibTeX:**
+```bibtex
+@misc{ai_exploit_generation,
+  author = {Canstralian},
+  title = {AI-Driven Exploit Generation},
+  year = {2025},
+  howpublished = {Hugging Face},
+  license = {MIT}
+}
+```