devanshty
/

Code-Autopsy

@@ -1,209 +1,174 @@
----
-base_model: Qwen/Qwen2.5-Coder-7B-Instruct
-library_name: peft
-pipeline_tag: text-generation
-tags:
-- base_model:adapter:Qwen/Qwen2.5-Coder-7B-Instruct
-- lora
-- sft
-- transformers
-- trl
----
-# Model Card for Model ID
-<!-- Provide a quick summary of what the model is/does. -->
-## Model Details
-### Model Description
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-### Framework versions
-- PEFT 0.19.1

+---
+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`.