aisingapore/llama3-8b-cpt-sea-lionv2-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. This is the card for the Llama3 8B CPT SEA-LIONv2 base model which has undergone continued pre-training from the Meta-Llama-3-8B-Instruct model.

SEA-LION stands for Southeast Asian Languages In One Network.

Model Details

Model Description

The continued pre-training data for Llama3 8B CPT 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

For tokenization, the model employs the default tokenizer used in Meta-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 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

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

Infrastructure

Llama3 8B CPT SEA-LIONv2 was trained using 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

The Team

Choa Esther
Cheng Nicholas
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 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

Link to SEA-LION's GitHub repository

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

Thai Pre-Training Data Reference

@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}
}