---
tags:
- text-generation
license: cc-by-nc-sa-4.0
language:
- ko
base_model: yanolja/KoSOLAR-10.7B-v0.1
pipeline_tag: text-generation
datasets:
- mncai/orca_dpo_pairs_ko
- Ja-ck/Orca-DPO-Pairs-KO
- We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs
---
# **DataVortexS-10.7B-dpo-v0.1**
## **Model Details**
### **Base Model**
[yanolja/KoSOLAR-10.7B-v0.1](https://huggingface.co/yanolja/KoSOLAR-10.7B-v0.1) _(Tokenizer Issue Fixed Version)_
### **Trained On**
- **OS**: Ubuntu 20.04
- **GPU**: H100 80GB 2ea
- **transformers**: v4.36.2
### **Dataset**
- [mncai/orca_dpo_pairs_ko](https://huggingface.co/datasets/mncai/orca_dpo_pairs_ko)
- [Ja-ck/Orca-DPO-Pairs-KO](https://huggingface.co/datasets/Ja-ck/Orca-DPO-Pairs-KO)
- [We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs](https://huggingface.co/datasets/We-Want-GPU/Yi-Ko-DPO-Orca-DPO-Pairs)
### **Instruction format**
It follows **Alpaca** format.
E.g.
```python
text = """\
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 Harnesss](https://github.com/Beomi/ko-lm-evaluation-harness)**
| Task | 0-shot | 5-shot | 10-shot | 50-shot |
| :--------------- | ------------: | -------------: | -----------: | -------------: |
| kobest_boolq | 0.334282 | 0.891367 | 0.896755 | 0.884441 |
| kobest_copa | 0.697763 | 0.716762 | 0.724769 | 0.751746 |
| kobest_hellaswag | 0.432047 | 0.458301 | 0.443993 | 0.458232 |
| kobest_sentineg | 0.49353 | 0.954657 | 0.964735 | 0.949606 |
| **Average** | **0.4894055** | **0.75527175** | **0.757563** | **0.76100625** |
### **[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-v0.1")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-dpo-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.
