Update README.md

README.md

@@ -132,3 +132,208 @@ 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
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+Hardcoded Secrets Detection
+
+Input Validation & Sanitization
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+Secure File Upload Handling
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+Broken Access Control
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+Password Hashing & Storage
+
+⚡ PERFORMANCE
+
+Algorithm Complexity Optimization
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+Database Query Optimization
+
+Memory Leak Detection
+
+I/O Bound Operations Optimization
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+CPU Bound Operations Optimization
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+Async/Await Performance
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+Caching Strategies Implementation
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+Loop Optimization Techniques
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+Data Structure Selection
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+Concurrent Execution Patterns
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+🐍 PYTHONIC CODE
+
+Type Hinting Implementation
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+Mutable Default Arguments
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+Context Manager Usage
+
+Decorator Best Practices
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+List/Dict/Set Comprehensions
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+Class Design Principles
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+Dunder Method Implementation
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+Property Decorator Usage
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+Generator Expressions
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+Class vs Static Methods
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+Import Organization
+
+Exception Handling & Hierarchy
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+EAFP vs LBYL Patterns
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+Basic syntax validation
+
+Variable scope validation
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+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
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+LoRA Dropout: 0.1
+
+Learning Rate: 2e-4
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+Batch Size: 16 (with gradient accumulation 4)
+
+Epochs: 2
+
+Max Sequence Length: 2048 tokens
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+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
+
+
+For questions about this model, please use the Hugging Face model repository discussions or contact via the above channels.