README.md

---
library_name: transformers
license: cc-by-nc-4.0
tags:
- code-review
- security-analysis
- static-analysis
- python
- code-quality
- peft
- qlora
- fine-tuned
- sql-injection
- vulnerability-detection
- python-security
- code-optimization
pipeline_tag: text-generation
datasets:
- alenphilip/Code-Review-Assistant
- alenphilip/Code-Review-Assistant-Eval
language:
- en
metrics:
- rouge
- bleu
base_model:
- Qwen/Qwen2.5-7B-Instruct
---

# Code Review Assistant Model

<!-- Provide a quick summary of what the model is/does. -->

A specialized Python code review assistant fine-tuned for security analysis, performance optimization, and Pythonic code quality. The model identifies security vulnerabilities, performance issues, and provides corrected code examples with detailed explanations specifically for Python codebases.

## Model Details

### Model Description

This model is a fine-tuned version of Qwen2.5-7B-Instruct, specifically optimized for Python code analysis. It excels at detecting security vulnerabilities, performance bottlenecks, and code quality issues while providing actionable fixes with corrected code examples.

- **Developed by:** Alen Philip
- **Model type:** Causal Language Model
- **Language(s) (NLP):** English, with specialized Python code understanding
- **License:** cc-by-nc-4.0
- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct
- **Supported Languages:** Python only

### Model Sources

- **Repository:** [Hugging Face Hub](https://huggingface.co/alenphilip/Code_Review_Assistant_Model)
- **Base Model:** [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)
- **Training Dataset:** [Code Review Dataset](https://huggingface.co/datasets/alenphilip/Code-Review-Assistant)
- **Evaluation Dataset** [Code Review(Eval) Dataset](https://huggingface.co/datasets/alenphilip/Code-Review-Assistant-Eval)

## Uses

### Direct Use

This model is specifically designed for:
- Automated Python code review in development pipelines
- Security vulnerability detection in Python code
- Python code quality assessment and improvement suggestions
- Performance optimization recommendations for Python applications
- Educational purposes for learning Python best practices
- Integration into Python IDEs and code editors

### Downstream Use

The model can be integrated into:
- CI/CD pipelines for automated Python code review
- Python code quality monitoring tools
- Security scanning platforms for Python applications
- Educational platforms for Python programming
- Code review assistance tools for Python developers

### Out-of-Scope Use

- Analysis of non-Python programming languages
- Non-code related text generation
- Legal or compliance advice
- Production deployment without human validation
- Real-time security monitoring without additional safeguards

## Bias, Risks, and Limitations

- **Language Specificity:** Only trained on Python code - will not perform well on other programming languages
- **False Positives/Negatives:** May occasionally miss edge cases or flag non-issues
- **Training Data Bias:** Reflects patterns and conventions present in the training dataset
- **Security Critical Systems:** Should not be sole security measure for critical systems

### Recommendations

Users should:
- Always validate model suggestions with human review
- Use as assistant tool rather than autonomous system
- Test suggested fixes thoroughly before deployment
- Combine with other security scanning tools for critical applications

## How to Get Started with the Model

```python
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch

model_name = "alenphilip/Code_Review_Assistant_Model"
tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype=torch.bfloat16,
    device_map="auto",
    trust_remote_code=True
)

# Example usage for code review
def review_python_code(code_snippet):
    messages = [
        {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},
        {"role": "user", "content": f"Review this Python code and provide improvements with fixed code:\n\n```python\n{code_snippet}\n```"}
    ]
    
    text = tokenizer.apply_chat_template(
        messages,
        tokenize=False,
        add_generation_prompt=False
    )
    
    inputs = tokenizer(text, return_tensors="pt").to(model.device)
    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)
    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
    
    return response

# Test with vulnerable code
vulnerable_code = '''
def get_user_by_email(email):
    query = "SELECT * FROM users WHERE email = '" + email + "'"
    cursor.execute(query)
    return cursor.fetchone()
'''

result = review_python_code(vulnerable_code)
print(result)
```
#### OR 
```python
# Use a pipeline as a high-level helper
from transformers import pipeline
pipe = pipeline("text-generation", model="alenphilip/Code_Review_Assistant_Model")
prompt = "Review this Python code and provide improvements with fixed code:\n\n```python\nclass LockManager:\n    def __init__(self, lock1, lock2):\n        self.lock1 = lock1\n        self.lock2 = lock2\n\n    def acquire_both(self):\n        self.lock1.acquire()\n        self.lock2.acquire() # This might fail\n\n    def release_both(self):\n        self.lock1.release()\n        self.lock2.release()\n```"
messages = [
    {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},
    {"role": "user", "content": prompt},
]
result = pipe(messages)
conversation = result[0]['generated_text']

for message in conversation:
    print(f"\n{message['role'].upper()}:")
    print("-" * 50)
    print(message['content'])
    print()

print("=" * 70)
```
# Training Details
## Training Data
The model was trained on a comprehensive dataset of Python code review examples covering:

### 🔐 SECURITY
- SQL Injection Prevention
- XSS Prevention in Web Frameworks
- Authentication Bypass Vulnerabilities
- Insecure Deserialization
- Command Injection Prevention
- JWT Token Security
- Hardcoded Secrets Detection
- Input Validation & Sanitization
- Secure File Upload Handling
- Broken Access Control
- Password Hashing & Storage

### ⚡ PERFORMANCE
- Algorithm Complexity Optimization
- Database Query Optimization
- Memory Leak Detection
- I/O Bound Operations Optimization
- CPU Bound Operations Optimization
- Async/Await Performance
- Caching Strategies Implementation
- Loop Optimization Techniques
- Data Structure Selection
- Concurrent Execution Patterns

### 🐍 PYTHONIC CODE

- Type Hinting Implementation
- Mutable Default Arguments
- Context Manager Usage
- Decorator Best Practices
- List/Dict/Set Comprehensions
- Class Design Principles
- Dunder Method Implementation
- Property Decorator Usage
- Generator Expressions
- Class vs Static Methods
- Import Organization
- Exception Handling & Hierarchy
- EAFP vs LBYL Patterns
- Basic syntax validation
- Variable scope validation
- Type Operation Compatibility

### 🔧 PRODUCTION RELIABILITY

- Error Handling and Logging

## Training Procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alenphilip2071-google/huggingface/runs/d27nrifd) 
### Training Hyperparameters
- **Training regime:** bf16 mixed precision with SFT & QLoRA
- **Base Model:** Qwen2.5-7B-Instruct
- **LoRA Rank:** 32
- **LoRA Alpha:** 64
- **LoRA Dropout:** 0.1
- **Learning Rate:** 2e-4
- **Batch Size:** 16 (with gradient accumulation 4)
- **Epochs:** 2
- **Max Sequence Length:** 2048 tokens
- **Optimizer:** Paged AdamW 8-bit

### Speeds, Sizes, Times
- **Base Model Size:** 7B parameters
- **Adapter Size:** ~45MB
- **Training Time:** ~68 minutes for 400 steps
- **Training Examples:** 13,670 training, 1,726 evaluation

## Evaluation
### Metrics
- **ROUGE-L:** 0.754  
- **BLEU:** 61.99  
- **Validation Loss:** 0.595  

## Results
The model achieved strong performance on code review tasks, particularly excelling at:
- Security vulnerability detection (SQL injection, XSS, etc.)
- Pythonic code improvements
- Performance optimization suggestions
- Providing corrected code examples

## Summary
The model demonstrates excellent capability in identifying and fixing common Python code issues, with particular strength in security vulnerability detection and code quality improvements.

## Environmental Impact
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact/#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- Hardware Type: NVIDIA H100 80GB VRAM
- Hours used: ~1.5 hours
- Training Approach: QLoRA for efficient fine-tuning

## Technical Specifications
### Model Architecture and Objective
- **Architecture:** Transformer-based causal language model
- **Objective:** Supervised fine-tuning for code review tasks
- **Context Window:** 32K tokens (base model)

### Compute Infrastructure
**Hardware**
- Training performed on GPU cluster with NVIDIA H100 80GB VRAM

**Software**
- Transformers, PEFT, TRL, BitsAndBytes
- QLoRA for parameter-efficient fine-tuning

## Citation
```bibtex
@misc{alen_philip_george_2025,
  author       = {Alen Philip George},  
  title        = {Code_Review_Assistant_Model (Revision 233d438)},  
  year         = 2025,  
  url          = {https://huggingface.co/alenphilip/Code_Review_Assistant_Model},  
  doi          = {10.57967/hf/6836},  
  publisher    = {Hugging Face}  
}
```
## Model Card Authors
Alen Philip George

## Model Card Contact
Hugging Face: [alenphilip](https://huggingface.co/alenphilip)  
LinkedIn: [alenphilipgeorge](https://linkedin.com/in/alen-philip-george-130226254)  
Email: [alenphilipgeorge@gmail.com](mailto:alenphilipgeorge@gmail.com)


For questions about this model, please use the Hugging Face model repository discussions or contact via the above channels.