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.

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 Southeast Asian Languages In One Network.

• Developed by: Products Pillar, AI Singapore
• Funded by: Singapore NRF
• Model type: Decoder
• Languages supported: English, Indonesian, Thai, Vietnamese, Tamil
• License: Llama3 Community License

Model Details

Model Description

We performed continued pre-training in English and SEA languages on 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 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 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

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 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.

Link to SEA-LION's GitHub repository.

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

@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}
}