---
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:
- nlpai-lab/kullm-v2
---
# **DataVortexS-10.7B-v0.1**
## **Model Details**
### **Base Model**
[yanolja/KoSOLAR-10.7B-v0.1](https://huggingface.co/yanolja/KoSOLAR-10.7B-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) - 152k rows
### **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-LLM-Leaderboard**
On Benchmarking ...
| Model | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ------------------------------------ | ------- | ------ | ------------ | ------- | ------------- | --------------- |
| Edentns/DataVortexM-7B-Instruct-v0.1 | 39.81 | 34.13 | 42.35 | 38.73 | 45.46 | 38.37 |
| **Edentns/DataVortexS-10.7B-v0.1** | **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-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.
