PraneetNS/codesentinel-full

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

⚡ "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

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:

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!