README.md

---
tags:
    - text-generation
license: cc-by-nc-4.0
language:
    - ko
base_model: LDCC/LDCC-SOLAR-10.7B
pipeline_tag: text-generation
---

# **DataVortexS-10.7B-dpo-v1.11**

<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**

[LDCC/LDCC-SOLAR-10.7B](https://huggingface.co./LDCC/LDCC-SOLAR-10.7B)

### **Trained On**

-   **OS**: Ubuntu 22.04
-   **GPU**: H100 80GB 4ea
-   **transformers**: v4.36.2

### **Instruction format**

It follows **Alpaca (Chat)** format.

E.g.

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

### User:
대한민국의 수도는 어디야?

### Assistant:
대한민국의 수도는 서울입니다.

### User:
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     |     0.920101 |     0.928018 |     0.933025 |     0.928754 |
| kobest_copa      |     0.721782 |     0.801936 |     0.817737 |      0.84093 |
| kobest_hellaswag |      0.44502 |     0.482783 |     0.483978 |      0.48978 |
| kobest_sentineg  |      0.51398 |     0.931928 |     0.944556 |     0.934475 |
| **Average**      | **0.650221** | **0.786166** | **0.794824** | **0.798485** |

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

On Benchmarking ...

| Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------: | -----: | -----------: | ------: | ------------: | --------------: |
|       0 |      0 |            0 |       0 |             0 |               0 |

## **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-dpo-v1.11")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-v1.11")

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**

This model is licensed under the [cc-by-nc-4.0](https://creativecommons.org/licenses/by-nc/4.0/). which allows others to share and adapt the model for non-commercial purposes.

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