Code Review Assistant Model

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
Base Model: Qwen2.5-7B-Instruct
Training Dataset: Code Review Dataset
Evaluation Dataset Code Review(Eval) Dataset

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

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

# 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

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 presented in Lacoste et al. (2019).

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

@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
LinkedIn: alenphilipgeorge
Email: alenphilipgeorge@gmail.com

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

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Safetensors

Model size

8B params

Tensor type

BF16

Model tree for alenphilip/Code_Review_Assistant_Model

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct