PraneetNS/codesentinel-full

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

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

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🧠 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.

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📌 Key Features

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

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🏗️ 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)

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🎯 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

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❌ Out-of-Scope Use

• Generating malware or exploits
• Bypassing security systems
• Illegal or harmful automation

👉 The model is trained to refuse such requests

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⚠️ Limitations

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

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🛠️ How to Use

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))

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📊 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

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📈 Evaluation

✅ Improvements Observed

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

🧪 Example

Input:

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

Output:

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

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🌍 Impact

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

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⚡ Technical Highlights

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

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💻 Compute

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

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🔗 Links

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

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📢 Why CodeSentinel?

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

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🙌 Acknowledgements

• Hugging Face 🤗
• Qwen Team
• Open-source ML community

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📬 Contact

Author: Praneet Savant 📧 savantpraneet@gmail.com

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