---
library_name: transformers
tags:
- code-generation
- bug-fixing
- ai-safety
- llm
- qwen
- fine-tuned
- peft
- developer-tools
license: mit
language:
- en
- hi
base_model:
- Qwen/Qwen2.5-Coder-3B-Instruct
pipeline_tag: question-answering
---

---

# 🚀 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

```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

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