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