Update 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"
+---
+language: en
+tags:
+  - causal-lm
+  - secure-model
+  - merged-lora
+license: apache-2.0
+---
+
+# Llama-3.1-8b-Instruct-Secure
+
+This model is fine-tuned with LoRA adapters for secure behavior and low ASR (Attack Success Rate).
+
+## Model Details
+
+- **Base Model**: Llama-3.1-8b-Instruct
+- **Fine-tuning**: LoRA method
+- **Purpose**: Secure language model with defenses against jailbreaking.
+
+## Training Details
+
+- **Dataset**: Custom synthetic data
+- **Framework**: PyTorch
+- **Sharding**: Model is saved in shards of 100MB to ensure compatibility.
+
+## Usage
+
+Load the model and tokenizer as follows:
+
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+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
+inputs = tokenizer("Your input prompt here", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.decode(outputs[0]))