README.md

---
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:
    - Edentns/data_go_kr-PublicDoc
    - Edentns/aihub-TL_unanswerable_output
    - Edentns/aihub-TL_span_extraction_how_output
    - Edentns/aihub-TL_multiple_choice_output
    - Edentns/aihub-TL_text_entailment_output
    - jojo0217/korean_rlhf_dataset
    - kyujinpy/KOR-OpenOrca-Platypus-v3
    - beomi/KoAlpaca-v1.1a
    - HumanF-MarkrAI/WIKI_QA_Near_dedup
---

# **DataVortexS-10.7B-v0.4**

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

## **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**

-   Edentns/data_go_kr-PublicDoc - private
-   Edentns/aihub-TL_unanswerable_output - private
-   Edentns/aihub-TL_span_extraction_how_output - private
-   Edentns/aihub-TL_multiple_choice_output - private
-   Edentns/aihub-TL_text_entailment_output - private
-   [jojo0217/korean_rlhf_dataset](https://huggingface.co/datasets/jojo0217/korean_rlhf_dataset)
-   [kyujinpy/KOR-OpenOrca-Platypus-v3](https://huggingface.co/datasets/kyujinpy/KOR-OpenOrca-Platypus-v3)
-   [beomi/KoAlpaca-v1.1a](https://huggingface.co/datasets/beomi/KoAlpaca-v1.1a)
-   [HumanF-MarkrAI/WIKI_QA_Near_dedup](https://huggingface.co/datasets/HumanF-MarkrAI/WIKI_QA_Near_dedup)

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

On Benchmarking ...

| Model                        | Average | Ko-ARC | Ko-HellaSwag | Ko-MMLU | Ko-TruthfulQA | Ko-CommonGen V2 |
| ---------------------------- | ------- | ------ | ------------ | ------- | ------------- | --------------- |
| DataVortexM-7B-Instruct-v0.1 | 39.81   | 34.13  | 42.35        | 38.73   | 45.46         | 38.37           |
| DataVortexS-10.7B-v0.1       | 0       | 0      | 0            | 0       | 0             | 0               |
| DataVortexS-10.7B-v0.2       | 0       | 0      | 0            | 0       | 0             | 0               |
| DataVortexS-10.7B-v0.3       | 0       | 0      | 0            | 0       | 0             | 0               |
| **DataVortexS-10.7B-v0.4**   | **0**   | **0**  | **0**        | **0**   | **0**         | **0**           |
| DataVortexS-10.7B-v0.5       | 0       | 0      | 0            | 0       | 0             | 0               |
| DataVortexTL-1.1B-v0.1       | 0       | 0      | 0            | 0       | 0             | 0               |
| DataVortexS-10.7B-dpo-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-v0.4")
tokenizer = AutoTokenizer.from_pretrained("Edentns/DataVortexS-10.7B-v0.4")

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>