metadata
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
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 =