| --- |
| 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 = |
