alenphilip/Code_Review_Assistant_Model

1---2library_name: transformers3license: cc-by-nc-4.04tags:5- code-review6- security-analysis7- static-analysis8- python9- code-quality10- peft11- qlora12- fine-tuned13- sql-injection14- vulnerability-detection15- python-security16- code-optimization17pipeline_tag: text-generation18datasets:19- alenphilip/Code-Review-Assistant20- alenphilip/Code-Review-Assistant-Eval21language:22- en23metrics:24- rouge25- bleu26base_model:27- Qwen/Qwen2.5-7B-Instruct28---29 30# Code Review Assistant Model31 32<!-- Provide a quick summary of what the model is/does. -->33 34A 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.35 36## Model Details37 38### Model Description39 40This 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.41 42- **Developed by:** Alen Philip43- **Model type:** Causal Language Model44- **Language(s) (NLP):** English, with specialized Python code understanding45- **License:** cc-by-nc-4.046- **Finetuned from model:** Qwen/Qwen2.5-7B-Instruct47- **Supported Languages:** Python only48 49### Model Sources50 51- **Repository:** [Hugging Face Hub](https://huggingface.co/alenphilip/Code_Review_Assistant_Model)52- **Base Model:** [Qwen2.5-7B-Instruct](https://huggingface.co/Qwen/Qwen2.5-7B-Instruct)53- **Training Dataset:** [Code Review Dataset](https://huggingface.co/datasets/alenphilip/Code-Review-Assistant)54- **Evaluation Dataset** [Code Review(Eval) Dataset](https://huggingface.co/datasets/alenphilip/Code-Review-Assistant-Eval)55 56## Uses57 58### Direct Use59 60This model is specifically designed for:61- Automated Python code review in development pipelines62- Security vulnerability detection in Python code63- Python code quality assessment and improvement suggestions64- Performance optimization recommendations for Python applications65- Educational purposes for learning Python best practices66- Integration into Python IDEs and code editors67 68### Downstream Use69 70The model can be integrated into:71- CI/CD pipelines for automated Python code review72- Python code quality monitoring tools73- Security scanning platforms for Python applications74- Educational platforms for Python programming75- Code review assistance tools for Python developers76 77### Out-of-Scope Use78 79- Analysis of non-Python programming languages80- Non-code related text generation81- Legal or compliance advice82- Production deployment without human validation83- Real-time security monitoring without additional safeguards84 85## Bias, Risks, and Limitations86 87- **Language Specificity:** Only trained on Python code - will not perform well on other programming languages88- **False Positives/Negatives:** May occasionally miss edge cases or flag non-issues89- **Training Data Bias:** Reflects patterns and conventions present in the training dataset90- **Security Critical Systems:** Should not be sole security measure for critical systems91 92### Recommendations93 94Users should:95- Always validate model suggestions with human review96- Use as assistant tool rather than autonomous system97- Test suggested fixes thoroughly before deployment98- Combine with other security scanning tools for critical applications99 100## How to Get Started with the Model101 102```python103from transformers import AutoTokenizer, AutoModelForCausalLM104import torch105 106model_name = "alenphilip/Code_Review_Assistant_Model"107tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)108model = AutoModelForCausalLM.from_pretrained(109    model_name,110    torch_dtype=torch.bfloat16,111    device_map="auto",112    trust_remote_code=True113)114 115# Example usage for code review116def review_python_code(code_snippet):117    messages = [118        {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},119        {"role": "user", "content": f"Review this Python code and provide improvements with fixed code:\n\n```python\n{code_snippet}\n```"}120    ]121    122    text = tokenizer.apply_chat_template(123        messages,124        tokenize=False,125        add_generation_prompt=False126    )127    128    inputs = tokenizer(text, return_tensors="pt").to(model.device)129    outputs = model.generate(**inputs, max_new_tokens=512, temperature=0.1)130    response = tokenizer.decode(outputs[0], skip_special_tokens=True)131    132    return response133 134# Test with vulnerable code135vulnerable_code = '''136def get_user_by_email(email):137    query = "SELECT * FROM users WHERE email = '" + email + "'"138    cursor.execute(query)139    return cursor.fetchone()140'''141 142result = review_python_code(vulnerable_code)143print(result)144```145#### OR 146```python147# Use a pipeline as a high-level helper148from transformers import pipeline149pipe = pipeline("text-generation", model="alenphilip/Code_Review_Assistant_Model")150prompt = "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```"151messages = [152    {"role": "system", "content": "You are a helpful AI assistant specialized in code review and security analysis."},153    {"role": "user", "content": prompt},154]155result = pipe(messages)156conversation = result[0]['generated_text']157 158for message in conversation:159    print(f"\n{message['role'].upper()}:")160    print("-" * 50)161    print(message['content'])162    print()163 164print("=" * 70)165```166# Training Details167## Training Data168The model was trained on a comprehensive dataset of Python code review examples covering:169 170### 🔐 SECURITY171- SQL Injection Prevention172- XSS Prevention in Web Frameworks173- Authentication Bypass Vulnerabilities174- Insecure Deserialization175- Command Injection Prevention176- JWT Token Security177- Hardcoded Secrets Detection178- Input Validation & Sanitization179- Secure File Upload Handling180- Broken Access Control181- Password Hashing & Storage182 183### ⚡ PERFORMANCE184- Algorithm Complexity Optimization185- Database Query Optimization186- Memory Leak Detection187- I/O Bound Operations Optimization188- CPU Bound Operations Optimization189- Async/Await Performance190- Caching Strategies Implementation191- Loop Optimization Techniques192- Data Structure Selection193- Concurrent Execution Patterns194 195### 🐍 PYTHONIC CODE196 197- Type Hinting Implementation198- Mutable Default Arguments199- Context Manager Usage200- Decorator Best Practices201- List/Dict/Set Comprehensions202- Class Design Principles203- Dunder Method Implementation204- Property Decorator Usage205- Generator Expressions206- Class vs Static Methods207- Import Organization208- Exception Handling & Hierarchy209- EAFP vs LBYL Patterns210- Basic syntax validation211- Variable scope validation212- Type Operation Compatibility213 214### 🔧 PRODUCTION RELIABILITY215 216- Error Handling and Logging217 218## Training Procedure219[<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) 220### Training Hyperparameters221- **Training regime:** bf16 mixed precision with SFT & QLoRA222- **Base Model:** Qwen2.5-7B-Instruct223- **LoRA Rank:** 32224- **LoRA Alpha:** 64225- **LoRA Dropout:** 0.1226- **Learning Rate:** 2e-4227- **Batch Size:** 16 (with gradient accumulation 4)228- **Epochs:** 2229- **Max Sequence Length:** 2048 tokens230- **Optimizer:** Paged AdamW 8-bit231 232### Speeds, Sizes, Times233- **Base Model Size:** 7B parameters234- **Adapter Size:** ~45MB235- **Training Time:** ~68 minutes for 400 steps236- **Training Examples:** 13,670 training, 1,726 evaluation237 238## Evaluation239### Metrics240- **ROUGE-L:** 0.754  241- **BLEU:** 61.99  242- **Validation Loss:** 0.595  243 244## Results245The model achieved strong performance on code review tasks, particularly excelling at:246- Security vulnerability detection (SQL injection, XSS, etc.)247- Pythonic code improvements248- Performance optimization suggestions249- Providing corrected code examples250 251## Summary252The model demonstrates excellent capability in identifying and fixing common Python code issues, with particular strength in security vulnerability detection and code quality improvements.253 254## Environmental Impact255Carbon 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).256- Hardware Type: NVIDIA H100 80GB VRAM257- Hours used: ~1.5 hours258- Training Approach: QLoRA for efficient fine-tuning259 260## Technical Specifications261### Model Architecture and Objective262- **Architecture:** Transformer-based causal language model263- **Objective:** Supervised fine-tuning for code review tasks264- **Context Window:** 32K tokens (base model)265 266### Compute Infrastructure267**Hardware**268- Training performed on GPU cluster with NVIDIA H100 80GB VRAM269 270**Software**271- Transformers, PEFT, TRL, BitsAndBytes272- QLoRA for parameter-efficient fine-tuning273 274## Citation275```bibtex276@misc{alen_philip_george_2025,277  author       = {Alen Philip George},  278  title        = {Code_Review_Assistant_Model (Revision 233d438)},  279  year         = 2025,  280  url          = {https://huggingface.co/alenphilip/Code_Review_Assistant_Model},  281  doi          = {10.57967/hf/6836},  282  publisher    = {Hugging Face}  283}284```285## Model Card Authors286Alen Philip George287 288## Model Card Contact289Hugging Face: [alenphilip](https://huggingface.co/alenphilip)  290LinkedIn: [alenphilipgeorge](https://linkedin.com/in/alen-philip-george-130226254)  291Email: [alenphilipgeorge@gmail.com](mailto:alenphilipgeorge@gmail.com)292 293 294For questions about this model, please use the Hugging Face model repository discussions or contact via the above channels.