---
license: other
base_model: LGAI-EXAONE/EXAONE-Deep-7.8B
tags:
- exaone
- lora
- civil-complaint
- korean
- on-device-ai
model_type: exaone
language:
- ko
---

# Civil Complaint Analysis & Processing System (LoRA Adapter)

This model is a LoRA adapter for **LGAI-EXAONE/EXAONE-Deep-7.8B**, fine-tuned on the Korean Civil Complaint dataset (AI Hub 71852/71844). 
It is optimized for classifying civil complaints and generating high-quality response drafts in a structured format.

## Model Details

- **Base Model**: [LGAI-EXAONE/EXAONE-Deep-7.8B](https://huggingface.co/LGAI-EXAONE/EXAONE-Deep-7.8B)
- **Fine-tuning Method**: QLoRA (4-bit)
- **Dataset**: AI Hub Public and Private Civil Complaint Datasets (~10,000 samples)
- **Max Sequence Length**: 2048
- **Language**: Korean

## Training Results

The model achieved stable convergence during fine-tuning:
- **Best Eval Loss**: 1.0179 (at step 700)
- **Epochs**: 1
- **Learning Rate**: 2e-4
- **LoRA Rank (r)**: 16
- **LoRA Alpha**: 32

## License

This model is released under the **EXAONE AI Model License Agreement 1.1-NC**, inherited from the base model. 
Usage is permitted for non-commercial research and educational purposes. For commercial use, please refer to the original license terms provided by LG AI Research.

## How to use

You can load this adapter using the `peft` library with the base model:

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

model_id = "LGAI-EXAONE/EXAONE-Deep-7.8B"
adapter_id = "umyunsang/civil-complaint-exaone-lora"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    torch_dtype=torch.bfloat16, 
    device_map="auto",
    trust_remote_code=True
)
model = PeftModel.from_pretrained(model, adapter_id)
```

## Acknowledgments
This project was supported by the SW Central University Project at Dong-A University.
Special thanks to Mentor Professor Sejin Chun (sjchun@dau.ac.kr).