README.md

---
tags:
    - text-generation
license: cc-by-nc-sa-4.0
language:
    - ko
base_model: hyeogi/SOLAR-10.7B-dpo-v0.1
pipeline_tag: text-generation
datasets:
    - nlpai-lab/kullm-v2
---

# **DataVortexS-10.7B-v0.1**

<img src="./DataVortex.png" alt="DataVortex" style="height: 8em;">

## Our Team

| Research & Engineering | Product Management |
| :--------------------: | :----------------: |
|     Kwangseok Yang     |   Seunghyun Choi   |
|     Jeongwon Choi      |    Hyoseok Choi    |

## **Model Details**

### **Base Model**

[hyeogi/SOLAR-10.7B-dpo-v0.1](https://huggingface.co./hyeogi/SOLAR-10.7B-dpo-v0.1)

### **Trained On**

-   **OS**: Ubuntu 20.04
-   **GPU**: H100 80GB 1ea
-   **transformers**: v4.36.2

### **Dataset**

-   [nlpai-lab/kullm-v2](https://huggingface.co./datasets/nlpai-lab/kullm-v2)

### **Instruction format**

It follows **Alpaca** format.

E.g.

```python
text = """\
당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다.

### Instruction:
대한민국의 수도는 어디야?

### Response:
대한민국의 수도는 서울입니다.

### Instruction:
서울 인구는 총 몇 명이야?
"""
```

## **Model Benchmark**

### **[Ko LM Eval Harness](https://github.com/Beomi/ko-lm-evaluation-harness)**

| Task             |         0-shot |       5-shot |       10-shot |      50-shot |
| :--------------- | -------------: | -----------: | ------------: | -----------: |
| kobest_boolq     |       0.334282 |     0.642861 |      0.691496 |     0.638754 |
| kobest_copa      |       0.584962 |     0.564325 |      0.570654 |     0.581035 |
| kobest_hellaswag |       0.340022 |     0.339401 |      0.341917 |     0.337713 |
| kobest_sentineg  |       0.328257 |     0.414905 |      0.464711 |     0.888914 |
| **Average**      | **0.39688075** | **0.490373** | **0.5171945** | **0.611604** |

### **[Ko-LLM-Leaderboard](https://huggingface.co./spaces/upstage/open-ko-llm-leaderboard)**

| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
|   35.39 |  28.48 |        39.79 |   35.98 |         44.72 |           27.63 |

## **Implementation Code**

This model contains the chat_template instruction format.  
You can use the code below.

```python
from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("Edentns/DataVortexS-10.7B-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.1")

messages = [
    {"role": "system", "content": "당신은 사람들이 정보를 찾을 수 있도록 도와주는 인공지능 비서입니다."},
    {"role": "user", "content": "대한민국의 수도는 어디야?"},
    {"role": "assistant", "content": "대한민국의 수도는 서울입니다."},
    {"role": "user", "content": "서울 인구는 총 몇 명이야?"}
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, max_new_tokens=1000, do_sample=True)
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])
```

## **License**

The model is licensed under the [cc-by-nc-sa-4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/) license, which allows others to copy, modify, and share the work non-commercially, as long as they give appropriate credit and distribute any derivative works under the same license.

<div align="center">
    <a href="https://edentns.com/">
        <img src="./Logo.png" alt="Logo" style="height: 3em;">
    </a>
</div>