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🚀 CodeSentinel — Smart Code Assistant for Bug Detection & Safe Generation

Name: codesentinel-full
Availability: InStock

⚡ "Write better code. Catch bugs early. Avoid unsafe outputs."

🧠 Model Overview

CodeSentinel is a fine-tuned Large Language Model built on top of Qwen2.5-Coder-3B-Instruct, designed specifically for:

🐞 Detecting and fixing bugs in code
🧠 Reducing hallucinations in generated code
🔒 Enforcing safe and responsible code generation
⚡ Assisting developers with clean, reliable solutions

This model is optimized for real-world developer workflows, especially for students and engineers in Tier-2/3 ecosystems.

📌 Key Features

✅ Bug Detection & Fixing
✅ Hallucination Reduction
✅ Safe Code Refusal (malicious prompts blocked)
✅ Structured & Clean Outputs
✅ Lightweight LoRA Fine-tuning (efficient)

🏗️ Model Details

Base Model: Qwen/Qwen2.5-Coder-3B-Instruct
Fine-tuning Method: LoRA (PEFT)
Training Stages:
- Stage 1 → Supervised Fine-Tuning (SFT)
- Stage 2 → Direct Preference Optimization (DPO)
Framework: Hugging Face Transformers + PEFT
Precision: FP16
Hardware: Tesla T4 (Kaggle)

🎯 Intended Use

💡 Direct Use

Debugging Python / general code
Improving code quality
Generating safe code snippets

🔧 Downstream Use

IDE plugins
Code review assistants
Educational tools
Developer copilots

❌ Out-of-Scope Use

Generating malware or exploits
Bypassing security systems
Illegal or harmful automation

👉 The model is trained to refuse such requests

⚠️ Limitations

May still hallucinate in rare edge cases
Limited to training distribution (mostly Python-centric)
Not a replacement for production-level code review

🛠️ How to Use

python

from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel

base_model = AutoModelForCausalLM.from_pretrained(
    "Qwen/Qwen2.5-Coder-3B-Instruct",
    device_map="auto",
    torch_dtype="auto"
)

model = PeftModel.from_pretrained(base_model, "PraneetNS/codesentinel-adapter")
tokenizer = AutoTokenizer.from_pretrained("PraneetNS/codesentinel-adapter")

prompt = "Fix this bug: KeyError in dictionary access"

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=200)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

📊 Training Details

📚 Dataset

Custom curated dataset including:

Buggy vs correct code pairs
Hallucination traps (e.g., fake functions)
Safe vs unsafe response pairs

⚙️ Training Setup

Batch size: 2 (effective 16 via accumulation)
Learning rate: 2e-4 (SFT), 5e-6 (DPO)
Epochs: 1–2
Optimizer: AdamW

📈 Evaluation

✅ Improvements Observed

Reduced incorrect API usage
Better structured responses
Improved refusal for unsafe prompts

🧪 Example

Input:

python

users = {'alice': {'score': 80}}
print(users['bob']['score'])

Output:

Detects KeyError
Suggests safe access (dict.get())

🌍 Impact

Helps beginner developers avoid common mistakes
Encourages safe AI usage
Reduces debugging time

⚡ Technical Highlights

LoRA-based fine-tuning (memory efficient)
Runs on low-cost GPUs (T4)
Easily deployable

💻 Compute

GPU: Tesla T4
Platform: Kaggle
Training Time: ~1–2 hours

🔗 Links

Model: https://huggingface.co/PraneetNS/codesentinel-adapter

📢 Why CodeSentinel?

Most code models generate answers. CodeSentinel understands correctness and safety.

🙌 Acknowledgements

Hugging Face 🤗
Qwen Team
Open-source ML community

📬 Contact

Author: Praneet Savant 📧 savantpraneet@gmail.com

⭐ If you find this useful, consider starring the repo!

PraneetNS/codesentinel-full