alenphilip
/

Code_Review_Assistant_Model

@@ -130,209 +130,134 @@ def get_user_by_email(email):
 result = review_python_code(vulnerable_code)
 print(result)
-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 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
-Testing Data, Factors & Metrics
 Testing Data
 Evaluation performed on held-out Python code examples from the same dataset distribution.
-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 A100 or equivalent
-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 A100/A6000 class hardware
-Software
-Transformers, PEFT, TRL, BitsAndBytes
-QLoRA for parameter-efficient fine-tuning
-Citation
-BibTeX:
-bibtex
-@misc{code_review_assistant_2024,
-  title={Code Review Assistant: A Fine-tuned Model for Python Code Analysis},
-  author={Philip, Alen},
-  year={2024},
-  publisher={Hugging Face},
-  howpublished={\url{https://huggingface.co/alenphilip/Code_Review_Assistant_Model}}
-}
-DOI:
-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
-Model Card Contact
 Hugging Face: alenphilip
 LinkedIn: linkedin.com/in/alen-philip-george-130226254
 Email: alenphilipgeorge@gmail.com

 result = review_python_code(vulnerable_code)
 print(result)
+```
+# 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 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
+### Testing Data, Factors & Metrics
 Testing Data
 Evaluation performed on held-out Python code examples from the same dataset distribution.
+### 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 A100 or equivalent
+- 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 A100/A6000 class hardware
+**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: linkedin.com/in/alen-philip-george-130226254
 Email: alenphilipgeorge@gmail.com