README.md

---
license: apache-2.0
library_name: peft
tags:
  - security
  - cybersecurity
  - lora
  - mistral
  - fine-tuned
  - instruction-tuned
  - peft
  - text-generation
language:
  - en
pipeline_tag: text-generation
base_model: mistralai/Mistral-7B-Instruct-v0.1
---

# 🔒 Security-Focused Mistral 7B LoRA

A fine-tuned [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) model optimized for cybersecurity questions and answers using LoRA (Low-Rank Adaptation).

This model is specialized in providing detailed, accurate responses to security-related queries including vulnerabilities, attack vectors, defense mechanisms, and best practices.

## 📋 Model Details

| Property | Value |
|----------|-------|
| **Base Model** | [mistralai/Mistral-7B-Instruct-v0.1](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) |
| **Fine-tuning Method** | LoRA (r=8, α=16) |
| **Training Data** | 24 security Q&A pairs (JSONL format) |
| **Model Size** | 7B parameters (base) |
| **LoRA Adapter Size** | ~50-100 MB |
| **Framework** | Transformers + PEFT |
| **License** | Same as Mistral (Apache 2.0) |

---

## 🎯 Use Cases

This model is designed for:
- **Security Education** - Learning about vulnerabilities and defenses
- **Vulnerability Assessment** - Understanding attack vectors
- **Security Best Practices** - Implementation recommendations
- **Threat Analysis** - Explaining security concepts
- **Compliance Questions** - Security-related compliance topics

### ✅ What It Does Well
- Explains common security vulnerabilities (SQL injection, XSS, CSRF, etc.)
- Provides defense mechanisms and mitigation strategies
- Discusses security best practices and standards
- Analyzes threat models and attack scenarios
- Recommends secure coding practices

### ⚠️ Limitations
- Trained on limited dataset (24 examples) for demonstration purposes
- May not cover all specialized security topics
- Should be used as educational supplement, not primary security advisor
- Responses should be validated against official security documentation

---

## 🚀 Quick Start

### Installation

```bash
# Install required packages
pip install transformers peft torch

# (Optional) For GPU support
pip install torch --index-url https://download.pytorch.org/whl/cu118
```

### Basic Usage

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer

# Load the model
model = AutoPeftModelForCausalLM.from_pretrained(
    "debashis2007/security-mistral-lora",
    device_map="auto",
    torch_dtype=torch.float16,
)

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

# Prepare input (Mistral format)
prompt = "[INST] What is SQL injection and how do you prevent it? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt")

# Generate response
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_length=256,
        temperature=0.7,
        top_p=0.9,
    )

# Decode and print
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(response)
```

### Advanced Usage with Custom Settings

```python
from peft import AutoPeftModelForCausalLM
from transformers import AutoTokenizer
import torch

# Load model with specific settings
model = AutoPeftModelForCausalLM.from_pretrained(
    "debashis2007/security-mistral-lora",
    device_map="auto",
    torch_dtype=torch.float16,
    load_in_8bit=True,  # Optional: 8-bit quantization for memory efficiency
)

tokenizer = AutoTokenizer.from_pretrained("mistralai/Mistral-7B-Instruct-v0.1")

# Multiple questions
questions = [
    "What are the main types of web application attacks?",
    "How do you implement CSRF protection?",
    "Explain the principle of least privilege",
]

for question in questions:
    prompt = f"[INST] {question} [/INST]"
    inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
    
    with torch.no_grad():
        outputs = model.generate(
            **inputs,
            max_length=512,
            temperature=0.7,
            top_p=0.95,
            do_sample=True,
        )
    
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    print(f"Q: {question}\nA: {response}\n" + "="*60 + "\n")
```

---

## 📊 Training Details

### Training Configuration

| Parameter | Value |
|-----------|-------|
| **Learning Rate** | 2e-4 |
| **Epochs** | 1 |
| **Batch Size** | 1 |
| **Gradient Accumulation** | 4 |
| **Max Token Length** | 256 |
| **Optimizer** | paged_adamw_8bit |
| **Precision** | FP16 |
| **LoRA Rank (r)** | 8 |
| **LoRA Alpha** | 16 |
| **LoRA Dropout** | 0.05 |
| **Target Modules** | ["q_proj", "v_proj"] |

### Training Environment

- **Platform**: Google Colab
- **GPU**: NVIDIA T4 (16GB VRAM)
- **Training Time**: ~10-12 minutes
- **Framework**: Transformers 4.36.2 + PEFT 0.7.1
- **Memory Optimization**: 4-bit quantization + gradient checkpointing

### Dataset

- **Format**: JSONL (JSON Lines)
- **Size**: 24 security Q&A pairs
- **Topics**: 
  - SQL Injection
  - Cross-Site Scripting (XSS)
  - Cross-Site Request Forgery (CSRF)
  - Authentication & Authorization
  - Encryption & Hashing
  - Security Best Practices
  - Vulnerability Assessment
  - Threat Modeling

Example data point:
```json
{
  "instruction": "What is SQL injection and how do you prevent it?",
  "response": "SQL injection is a security vulnerability that occurs when an attacker inserts malicious SQL code into input fields. It exploits improperly validated or unescaped user input. Prevention methods include: 1) Using parameterized queries, 2) Input validation and sanitization, 3) Principle of least privilege for database accounts, 4) Web application firewalls, 5) Security testing and code reviews."
}
```

---

## 💡 Usage Examples

### Example 1: Security Vulnerability Explanation

```python
prompt = "[INST] What is a buffer overflow vulnerability? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

**Expected Output**: Explanation of buffer overflow, its consequences, and prevention methods.

### Example 2: Best Practice Recommendation

```python
prompt = "[INST] What are the best practices for password storage? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

**Expected Output**: Recommendations including hashing, salting, key derivation functions, etc.

### Example 3: Attack Scenario Analysis

```python
prompt = "[INST] How would an attacker exploit an unpatched software vulnerability? [/INST]"
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_length=256, temperature=0.7)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
```

**Expected Output**: Explanation of exploitation methods and defense strategies.

---

## ⚙️ Model Architecture

The model uses:
- **Base**: Mistral 7B Instruct v0.1
- **Adaptation**: LoRA (Low-Rank Adaptation)
- **Quantization**: 4-bit (during training)
- **Key Modifications**:
  - Q and V projections adapted with LoRA
  - Gradient checkpointing for memory efficiency
  - Flash Attention 2 for faster inference (when available)

### LoRA Details

```python
LoraConfig(
    r=8,                          # Rank
    lora_alpha=16,                # Scaling factor
    lora_dropout=0.05,            # Dropout probability
    bias="none",                  # Don't train bias
    task_type="CAUSAL_LM",        # Causal language modeling
    target_modules=["q_proj", "v_proj"],  # Adapted modules
    inference_mode=False,         # Training mode
)
```

---

## 🔍 Evaluation

### Model Performance

The model was evaluated on:
- **Accuracy**: Factual correctness of security information
- **Relevance**: Appropriateness of responses to queries
- **Clarity**: Comprehensibility of explanations
- **Completeness**: Coverage of important security concepts

### Known Issues

- Limited training data may result in incomplete responses for edge cases
- Responses should be verified against official security documentation
- Not suitable as primary security advisory tool
- May require fine-tuning with domain-specific data for production use

---

## 🛠️ Fine-tuning This Model

To fine-tune this model further on your own data:

```python
from peft import LoraConfig, get_peft_model
from transformers import AutoModelForCausalLM, TrainingArguments, Trainer
from datasets import load_dataset

# Load base model with adapter
model = AutoPeftModelForCausalLM.from_pretrained("debashis2007/security-mistral-lora")

# Merge with base model if you want to continue training
model = model.merge_and_unload()

# Or create new LoRA config for additional training
lora_config = LoraConfig(
    r=8,
    lora_alpha=16,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
    task_type="CAUSAL_LM",
)

model = get_peft_model(model, lora_config)

# Define training arguments
training_args = TrainingArguments(
    output_dir="./security-mistral-lora-v2",
    num_train_epochs=3,
    per_device_train_batch_size=1,
    gradient_accumulation_steps=4,
    learning_rate=2e-4,
    fp16=True,
    save_steps=10,
    logging_steps=5,
)

# Create trainer
trainer = Trainer(
    model=model,
    args=training_args,
    train_dataset=dataset,
)

# Train
trainer.train()
```

---

## 📚 Resources

### Documentation
- [PEFT Documentation](https://huggingface.co/docs/peft/)
- [Transformers Documentation](https://huggingface.co/docs/transformers/)
- [LoRA Paper](https://arxiv.org/abs/2106.09685)
- [Mistral Model Card](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1)

### Related Models
- [Mistral 7B](https://huggingface.co/mistralai/Mistral-7B-v0.1) - Base model
- [Mistral 7B Instruct](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.1) - Instruction-tuned base
- [LLaMA 2 7B](https://huggingface.co/meta-llama/Llama-2-7b-hf) - Alternative base model
- [Phi-2](https://huggingface.co/microsoft/phi-2) - Smaller alternative

---

## ⚖️ License & Attribution

This model is based on:
- **Mistral 7B**: Licensed under [Mistral AI Research License Agreement](https://huggingface.co/mistralai/Mistral-7B-v0.1)

Modifications using LoRA are provided as-is. Please comply with the original Mistral license.

### Citation

If you use this model, please cite:

```bibtex
@misc{security-mistral-lora,
  title={Security-Focused Mistral 7B LoRA},
  author={debashis2007},
  year={2024},
  howpublished={\url{https://huggingface.co/debashis2007/security-mistral-lora}}
}
```

---

## 🤝 Contributing

Found an issue or have suggestions? Feel free to open an issue on the model repository.

### Ways to Contribute
- Report bugs or issues
- Suggest improvements to prompts or responses
- Provide additional training data
- Contribute fine-tuning scripts
- Help with documentation

---

## ⚠️ Disclaimer

**This model is for educational and research purposes only.**

- Responses should not be used as the sole basis for security decisions
- Always validate against official security documentation
- Consult with security professionals for production systems
- The developers assume no liability for misuse or harmful outputs

---

## 📧 Contact

For questions about this model:
- **HuggingFace**: [@debashis2007](https://huggingface.co/debashis2007)
- **Model**: [security-mistral-lora](https://huggingface.co/debashis2007/security-mistral-lora)

---

## 📈 Version History

| Version | Date | Changes |
|---------|------|---------|
| v1.0 | 2024-12 | Initial release with 24 security examples |

---

## 🎓 Educational Use

This model is part of a security-focused AI training project. It demonstrates:
- LoRA fine-tuning on domain-specific data
- Memory-efficient training on consumer GPUs
- Deploying custom LLMs on HuggingFace Hub
- Building security-focused AI applications

---

**Last Updated**: December 2024  
**Model Status**: Active  
**Maintained By**: [debashis2007](https://huggingface.co/debashis2007)