---
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-7B-Instruct
library_name: peft
pipeline_tag: text-generation
tags:
- code
- code-review
- bug-fixing
- qwen
- qwen2.5-coder
- qlora
- peft
- trl
- static-analysis
model-index:
- name: Code-Autopsy
  results:
  - task:
      type: text-generation
      name: Code Bug Diagnosis & Refactoring
    metrics:
    - name: Validation Loss
      type: loss
      value: 0.2442
    - name: Token Accuracy
      type: accuracy
      value: 93.20%
---

<div align="center">

# 🔬 Code-Autopsy (QLoRA)
### Deep Structural Bug Diagnosis & Remediation Model

[![Base Model](https://img.shields.io/badge/Base_Model-Qwen2.5--Coder--7B--Instruct-blue.svg)](https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct)
[![PEFT](https://img.shields.io/badge/Fine--Tuning-QLoRA_(4--bit_NF4)-purple.svg)](https://github.com/huggingface/peft)
[![W&B Cloud Run](https://img.shields.io/badge/Weights_&_Biases-Live_Dashboard-gold.svg)](https://wandb.ai/devanshtyagi1903-innothoughts/code-autopsy/runs/gc70q2q2)
[![License](https://img.shields.io/badge/License-Apache_2.0-green.svg)](LICENSE)

</div>

---

## 📌 Model Summary

**Code-Autopsy** is a specialized code intelligence model fine-tuned on top of **Qwen2.5-Coder-7B-Instruct** using 4-bit QLoRA. It operates like an autonomous forensic compiler: given buggy, defective, or vulnerable code snippets across Python, JavaScript, and other languages, it outputs a clean, structured diagnostic report:

1. **Bug Identified:** Exact forensic analysis of the flaw (e.g. mutable default arguments, ZeroDivisionError, unawaited asynchronous promises, race conditions).
2. **Root Cause:** In-depth explanation of *why* the defect occurs at the runtime/memory level.
3. **Fixed Code:** Corrected, refactored, and production-ready implementation.

---

## 📊 Training Metrics & Cloud Logs

The model was trained for **3 full epochs (246 steps)** on a curated dataset of code bugs and algorithmic repairs.

| Metric | Initial (Epoch 0.06) | Final (Epoch 3.0) | Delta |
| :--- | :---: | :---: | :---: |
| **Training Loss** | `2.162` | **`0.255`** | **-88.2%** 📉 |
| **Validation Loss (`eval_loss`)** | `1.397` | **`0.2442`** | **-82.5%** 📉 |
| **Token Accuracy** | `60.29%` | **`93.20%`** | **+32.91%** 📈 |
| **Gradient Norm** | `0.27` | `0.39` | Stable |

> 🌐 **Interactive Training Logs & Loss Curves:**  
> View the live dashboard, loss charts, and hardware telemetry on [Weights & Biases](https://wandb.ai/devanshtyagi1903-innothoughts/code-autopsy/runs/gc70q2q2).

---

## ⚙️ Hyperparameters & Hardware Configuration

* **Base Model:** `Qwen/Qwen2.5-Coder-7B-Instruct`
* **Quantization:** 4-bit NF4 (`bitsandbytes` double quant)
* **Compute Dtype:** `bfloat16`
* **LoRA Rank ($r$):** `16`
* **LoRA Alpha ($lpha$):** `32`
* **LoRA Target Modules:** `q_proj`, `v_proj`
* **Optimizer:** `adamw_8bit`
* **Peak Learning Rate:** `2e-4` (with Cosine Decay and 5% Warmup)
* **Effective Batch Size:** `8` (Per-device `1`, Gradient Accumulation `8`)
* **Hardware:** NVIDIA GeForce RTX 5060 (8GB VRAM)

---

## 🚀 Quickstart: Running Inference

```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
from peft import PeftModel

BASE_MODEL = "Qwen/Qwen2.5-Coder-7B-Instruct"
ADAPTER_REPO = "devanshty/Code-Autopsy"

# 1. Load Tokenizer & 4-bit Base Model
tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)
bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16,
    bnb_4bit_use_double_quant=True
)

base_model = AutoModelForCausalLM.from_pretrained(
    BASE_MODEL,
    quantization_config=bnb_config,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    trust_remote_code=True
)

# 2. Load Fine-Tuned Code-Autopsy Adapter
model = PeftModel.from_pretrained(base_model, ADAPTER_REPO)
model.eval()

# 3. Format Diagnostic Prompt
code_snippet = '''def append_item(val, lst=[]):
    lst.append(val)
    return lst'''

prompt = f"""<|im_start|>system
You are a code review expert. Analyze the provided code, identify any bugs or issues, explain the root cause, and provide a corrected version.<|im_end|>
<|im_start|>user
Language: python

```python
{code_snippet}
```<|im_end|>
<|im_start|>assistant
"""

inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
    outputs = model.generate(
        **inputs,
        max_new_tokens=512,
        do_sample=False,
        pad_token_id=tokenizer.eos_token_id
    )

print(tokenizer.decode(outputs[0][inputs.input_ids.shape[1]:], skip_special_tokens=True))
```

---

## 🔍 Diagnostic Output Format

The model generates responses structured in Markdown:

```markdown
## Bug Identified
Mutable default argument `lst=[]` used in function definition.

## Root Cause
In Python, default arguments are evaluated once when the function is defined, not each time it is called. Modifying `lst` mutates the single shared list object across subsequent calls.

## Fixed Code
```python
def append_item(val, lst=None):
    if lst is None:
        lst = []
    lst.append(val)
    return lst
```
```

---

## 📜 Citation & Credits

* **Author:** Devansh Tyagi ([devanshty](https://huggingface.co/devanshty))
* **Base Architecture:** Alibaba Cloud Qwen Team (`Qwen2.5-Coder-7B-Instruct`)
* **Frameworks:** 🤗 Hugging Face `transformers`, `peft`, `trl`, and Weights & Biases `wandb`.