--- 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 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 [Visualize in Weights & Biases](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.