Create README.md

README.md

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+Llama-3.1-8B-Instruct-Secure
+Repository: SanjanaCodes/Llama-3.1-8B-Instruct-Secure
+License: Add License Here
+Languages: English (or specify other supported languages)
+Base Model: Llama-3.1-8B (or specify if different)
+Library Name: transformers, PyTorch (Add library used)
+Pipeline Tag: text-generation
+
+Model Description
+The Llama-3.1-8B-Instruct-Secure is a fine-tuned variant of the Llama-3.1-8B model designed to address LLM security vulnerabilities while maintaining strong performance for instruction-based tasks. It is optimized to handle:
+
+Secure Prompt Handling: Resistant to common jailbreak and adversarial attacks.
+Instruction Following: Retains instruction-based generation accuracy.
+Safety and Robustness: Improved safeguards against harmful or unsafe outputs.
+Key Features:
+Fine-tuned for secure instruction-based generation tasks.
+Includes defense mechanisms against adversarial and jailbreaking prompts.
+Pre-trained on a mixture of secure and adversarial datasets to generalize against threats.
+Usage
+Installation
+bash
+Copy code
+pip install transformers torch
+Example
+python
+Copy code
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_name = "SanjanaCodes/Llama-3.1-8B-Instruct-Secure"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name)
+
+# Example Input
+input_text = "Explain the importance of cybersecurity in simple terms."
+inputs = tokenizer(input_text, return_tensors="pt")
+
+# Generate Response
+output = model.generate(**inputs, max_length=150)
+print(tokenizer.decode(output[0], skip_special_tokens=True))
+Training Details
+Dataset
+Fine-tuned on a curated dataset with:
+Instruction-following data.
+Security-focused prompts.
+Adversarial prompts for robustness.
+Training Procedure
+Framework: PyTorch
+Hardware: GPU-enabled nodes
+Optimization Techniques:
+Mixed Precision Training
+Gradient Checkpointing
+Evaluation Metrics: Attack Success Rate (ASR), Robustness Score