transformers safetensors llama text-generation en id ta th vi arxiv:2309.06085 arxiv:2101.09635 base_model:meta-llama/Meta-Llama-3-8B-Instruct base_model:finetune:meta-llama/Meta-Llama-3-8B-Instruct license:llama3 autotrain_compatible text-generation-inference endpoints_compatible region:us
---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
language:
- en
- id
- ta
- th
- vi
license: llama3
new_version: aisingapore/llama3.1-8b-cpt-sea-lionv3-base
---
# Llama3 8B CPT 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.

Llama3 8B CPT SEA-LIONv2 Base is a multilingual model which has undergone continued pre-training on approximately **48B** tokens across 5 SEA languages: English, Indonesia, Tamil, Thai and Vietnamese.

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

- **Developed by:** Products Pillar, AI Singapore
- **Funded by:** Singapore NRF
- **Model type:** Decoder
- **Languages supported:** English, Indonesian, Thai, Vietnamese, Tamil
- **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)

## Model Details
### Model Description
We performed continued pre-training in English and SEA languages on [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), a decoder model using the Llama 3 architecture, to create Llama3 8B CPT SEA-LIONv2 Base.

For tokenisation, the model employs the default tokenizer used in Llama 3 8B Instruct.

### Benchmark Performance
We evaluated Llama3 8B CPT SEA-LIONv2 base model on general language capabilities.

#### General Language Capabilities
For the evaluation of general language capabilities in SEA languages, we employed the [BHASA evaluation benchmark](https://arxiv.org/abs/2309.06085v2) across a variety of tasks.
These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).

The evaluation was done **five-shot** with native prompts and only a sample of 100-1000 instances for each dataset was used as per the setting described in the paper.

For more details on Llama3 8B CPT SEA-LIONv2 base benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/

## Training Details
### Infrastructure
Llama3 8B CPT SEA-LIONv2 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
on the following hardware:

| Training Details     | Llama3 8B CPT SEA-LIONv2 |
|----------------------|:--------------------:|
| AWS EC2 p5d.24xlarge |          8 instances |
| Nvidia H100 80GB GPU |          64          |
| Training Duration    |          2 days      |

### Configuration
| HyperParameter    | Llama3 8B CPT SEA-LIONv2 |
|-------------------|:--------------------:|
| Precision         | bfloat16             |
| Optimizer         | decoupled_adamw      |
| Scheduler         | weight_stable_decay  |
| Learning Rate     | 1.0e-5               |
| Global Batch Size | 512                  |
| Micro Batch Size  | 2                    |

## Data
Llama3 8B CPT SEA-LIONv2 base model was continued pre-trained on 48B tokens of the following data:

| Data Source               | Unique Tokens (B) | Multiplier | Total Tokens (B) | Percentage (%) |
|---------------------------|:-----------------:|:----------:|:----------------:|:--------------:|
| Dolma RefinedWeb - English|        7.650      |          1 |       7.650      |     15.90      |
| Dolma C4 - English        |        1.160      |          1 |        1.16      |      9.21      |
| Dolma Reddit - English    |        1.339      |          1 |       1.339      |      2.42      |
| Dolma Semantic Scholar    |        0.959      |          1 |       0.959      |      2.79      |
| Dolma arXiv               |        0.469      |          1 |       0.469      |      1.99      |
| Dolma StarCoder           |        4.422      |          1 |       4.422      |      0.98      |
| SEA-LION Pile - Indonesian|          3.4      |          2 |         6.8      |     14.17      |
| Wiki* - Indonesian        |          0.3      |          4 |         1.2      |      2.50      |
| SEA-LION Pile - Tamil     |          5.6      |          1 |         5.6      |     11.67      |
| Wiki* + News - Tamil      |          0.6      |          4 |         2.4      |      5.00      |
| SEA-LION Pile - Thai      |         2.28      |          1 |        2.28      |      4.75      |
| WangChanBERTa - Thai      |            5      |          1 |           5      |     10.42      |
| Wiki* - Thai              |         0.18      |          4 |        0.72      |      1.50      |
| SEA-LION Pile - Vietnamese|         6.76      |          1 |        6.76      |     14.08      |
| Wiki* - Vietnamese        |         0.31      |          4 |        1.24      |      2.58      |

Note: 
- All token counts are counted using Llama3 tokenizer
- wiki* sources includes Wikipedia, Wiki Books, Wiki Source and Wiki Voyage
- Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)

## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.

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

## Acknowledgements
[AI Singapore](​​https://aisingapore.org/) 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 the National Research Foundation or the National University of 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 is the repository for the commercial instruction-tuned 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 claims, damages, or other liabilities arising from the use of the released weights and codes.

## References
### Thai Pre-Training Data Reference

```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}
}
```