README.md

---
license: llama2
base_model: beomi/llama-2-ko-7b
inference: false
datasets:
- Ash-Hun/Welfare-QA
library_name: peft
pipeline_tag: text-generation
tags:
- torch
- llama2
- domain-specific-lm
---

<div align='center'>
  <img src="https://cdn-uploads.huggingface.co/production/uploads/6370a4e53d1bd47a4ebc2120/TQSWE0e3dAO_Ksbb8b5Xd.png" width='45%'/>
  <h1>"WelSSiSKo : Welfare Domain Specific Model"</h1>
</div>

---  


# Github ▼
  > If you want to get how to use this model, please check my github repository :)  
👉 [Github Repo](https://github.com/ash-hun/WelSSISKo)


[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ash-hun/WelSSISKo/blob/main/WelSSiSKo_Inference.ipynb)


# What is BaseModel ▼
> 👉 [beomi/llama-2-ko-7b](https://huggingface.co/beomi/llama-2-ko-7b)


# Training procedure ▼
The following `bitsandbytes` quantization config was used during training:
- **load_in_4bit**: True
- **bnb_4bit_quant_type**: nf4
- **bnb_4bit_use_double_quant**: False
- **bnb_4bit_compute_dtype**: float16
  
# Framework versions ▼
- PEFT 0.8.2.


# Evaluate Score
- 적절한 Domain Benchmark Set이 없기때문에 정성평가를 진행하였고 그에 따른 **AVG Score는 0.74** 입니다.

![image/png](https://cdn-uploads.huggingface.co/production/uploads/6370a4e53d1bd47a4ebc2120/HwIKWCJb3bT2pk_tP70e0.png)