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README.md

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+---
+license: mit
+tags:
+  - peft
+  - lora
+  - qwen2
+  - code-review
+  - safetensors
+  - code-generation
+base_model: Qwen/Qwen2.5-Coder-7B-Instruct
+---
+
+# Code Autopsy
+
+## Model Description
+Code Autopsy is a QLoRA adapter fine-tuned on top of Qwen2.5-Coder-7B-Instruct for automated code review. It analyzes code for bugs, security vulnerabilities, style issues, and best practice violations — providing detailed, actionable review comments similar to a senior engineer's review.
+
+## Model Architecture
+- **Base Model**: `Qwen/Qwen2.5-Coder-7B-Instruct`
+- **Fine-tuning Method**: QLoRA (Quantized Low-Rank Adaptation) via PEFT
+- **Checkpoint**: `checkpoint-809` (best checkpoint)
+- **Task**: Code Review / Code Analysis
+
+## Training Details
+- **Framework**: HuggingFace PEFT + Transformers + BitsAndBytes
+- **Training Steps**: 809 (best checkpoint selected)
+- **Dataset**: Curated code review dataset with paired code + review comment examples
+- **Quantization**: 4-bit NF4 quantization during training
+
+## Files
+| File | Description |
+|------|-------------|
+| `adapter_model.safetensors` | LoRA adapter weights |
+| `adapter_config.json` | PEFT adapter configuration |
+| `tokenizer.json` | Tokenizer vocabulary |
+| `tokenizer_config.json` | Tokenizer configuration |
+
+## Usage
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig
+from peft import PeftModel
+import torch
+from huggingface_hub import snapshot_download
+
+# Download adapter
+adapter_dir = snapshot_download(repo_id='devanshty/Code-Autopsy')
+
+# Load base model with 4-bit quantization
+bnb_config = BitsAndBytesConfig(load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16)
+base_model = AutoModelForCausalLM.from_pretrained(
+    "Qwen/Qwen2.5-Coder-7B-Instruct",
+    quantization_config=bnb_config,
+    device_map="auto"
+)
+tokenizer = AutoTokenizer.from_pretrained(adapter_dir)
+
+# Load LoRA adapter
+model = PeftModel.from_pretrained(base_model, adapter_dir)
+model.eval()
+
+# Review code
+code =