Instructions to use alenphilip/Code_Review_Assistant_Model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use alenphilip/Code_Review_Assistant_Model with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="alenphilip/Code_Review_Assistant_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("alenphilip/Code_Review_Assistant_Model")
model = AutoModelForCausalLM.from_pretrained("alenphilip/Code_Review_Assistant_Model")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

PEFT
How to use alenphilip/Code_Review_Assistant_Model with PEFT:
```
Task type is invalid.
```
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use alenphilip/Code_Review_Assistant_Model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "alenphilip/Code_Review_Assistant_Model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alenphilip/Code_Review_Assistant_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/alenphilip/Code_Review_Assistant_Model

SGLang

How to use alenphilip/Code_Review_Assistant_Model with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "alenphilip/Code_Review_Assistant_Model" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alenphilip/Code_Review_Assistant_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "alenphilip/Code_Review_Assistant_Model" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "alenphilip/Code_Review_Assistant_Model",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use alenphilip/Code_Review_Assistant_Model with Docker Model Runner:
```
docker model run hf.co/alenphilip/Code_Review_Assistant_Model
```

Code_Review_Assistant_Model / README.md

alenphilip

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	---
	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

	<!-- Provide a quick summary of what the model is/does. -->

	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
	[<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)
	### 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.