---
license: llama3
language:
- en
- id
- ta
- th
- vi
---
# LLaMA3 8B SEA-LIONv2

SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
This model is continued pre-trained from the (Meta-Llama-3-8B-Instruct)[https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct] model.
This is the card for the LLaMA3 8B SEA-LIONv2 base model.

SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.


## Model Details

### Model Description

The SEA-LION model is a significant leap forward in the field of Natural Language Processing,
specifically trained to understand the SEA regional context.

For tokenization, the model employs the default tokenizer used in Meta-Llama-3-8B-Instruct.

The continued pre-training data for LLaMA3 8B SEA-LIONv2 base model encompasses approximately 48B tokens.

- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages:** English, Indonesian, Thai, Vietnamese, Tamil
- **License:** LLaMA3 Community License

### Performance Benchmarks

SEA-LION has an average performance on general tasks in English (as measured by Hugging Face's LLM Leaderboard):

| Model                | ARC   | BBH   | HellaSwag | MMLU  | GSM8k  | Average |
|----------------------|:-----:|:-----:|:---------:|:-----:|:------:|:-------:|
| LLaMA3 8B SEA-LIONv2 | 58.87 | 47.70 |   81.14   | 63.11 |  50.49 | 60.26   |

## Training Details

### Data

LLaMA3 8B SEA-LIONv2 base model was continued pre-trained on 48B tokens of the following data:

| Data Source               | Unique Tokens | Multiplier | Total Tokens | Percentage |
|---------------------------|:-------------:|:----------:|:------------:|:----------:|
| Dolma RefinedWeb - English|        7.650B |          1 |       7.650B |     15.90% |
| Dolma C4 - English        |        1.160B |          1 |           1B |      9.21% |
| Dolma Reddit - English    |        1.339B |          1 |        14.7B |      2.42% |
| Dolma Semantic Scholar    |        0.959B |          1 |         2.9B |      2.79% |
| Dolma arXiv               |        0.469B |          1 |         5.3B |      1.99% |
| Dolma StarCoder           |        4.422B |          1 |         4.9B |      0.98% |
| SEA-LION Pile - Indonesian|          3.4B |          1 |         6.8B |     14.17% |
| Wiki* - Indonesian        |          0.3B |          4 |         1.2B |      2.50% |
| SEA-LION Pile - Tamil     |          5.6B |          1 |         5.6B |     11.67% |
| Wiki* + News - Tamil      |          0.6B |          4 |         2.4B |      5.00% |
| SEA-LION Pile - Thai      |         2.28B |          1 |        2.28B |      4.75% |
| WangChanBERTa - Thai      |            5B |          1 |           5B |     10.42% |
| Wiki* - Thai              |         0.18B |          4 |        0.72B |      1.50% |
| SEA-LION Pile - Vietnamese|         6.76B |          1 |        6.76B |     14.08% |
| Wiki* - Vietnamese        |         0.31B |          4 |        1.24B |      2.58% |

Note: 
- All token counts are counted using LLaMA3 tokenizer
- wiki* sources includes Wikipedia, Wiki Books, Wiki Source and Wiki Voyage
- Source of Tamil news is source with permission from (Seithi)[https://seithi.mediacorp.sg/]

### Infrastructure

SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
on the following hardware:

| Training Details     | LLaMA3 8B SEA-LIONv2 |
|----------------------|:--------------------:|
| AWS EC2 p5d.24xlarge |          8 instances |
| Nvidia H100 80GB GPU |          64          |
| Training Duration    |          2 days      |


### Configuration

| HyperParameter    | LLaMA3 8B SEA-LIONv2 |
|-------------------|:--------------------:|
| Precision         | bfloat16             |
| Optimizer         | decoupled_adamw      |
| Scheduler         | weight_stable_decay  |
| Learning Rate     | 1.0e-5               |
| Global Batch Size | 512                  |
| Micro Batch Size  | 2                    |


## The Team

Brandon Ong<br>
Bryan Siow<br>
Esther Choa<br>
Huang Yuli<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Li Yier<br>
Liu Bing Jie Darius<br>
Lovenia Holy<br>
Montalan Jann Railey<br>
Ng Boon Cheong Raymond<br>
Ngui Jian Gang<br>
Nguyen Thanh Ngan<br>
Nicholas Cheng<br>
Ong Tat-Wee David<br>
Ong Zhi Hao<br>
Rengarajan Hamsawardhini<br>
Susanto Yosephine<br>
Tai Ngee Chia<br>
Tan Choon Meng<br>
Teo Jin Howe<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Walter Teng<br>
Wayne Lau<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>

## Acknowledgements

AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.

## Contact

For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)

[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)


## Disclaimer

This the repository for the base model.
The model has _not_ been aligned for safety.
Developers and users should perform their own safety fine-tuning and related security measures.
In no event shall the authors be held liable for any claim, damages, or other liability
arising from the use of the released weights and codes.


## References

```bibtex
@misc{lowphansirikul2021wangchanberta,
    title={WangchanBERTa: Pretraining transformer-based Thai Language Models},
    author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong},
    year={2021},
    eprint={2101.09635},
    archivePrefix={arXiv},
    primaryClass={cs.CL}
}
```