SEA-LION is a collection of Large Language Models (LLMs) which have been pretrained and instruct-tuned for the Southeast Asia (SEA) region.

Llama-SEA-LION-v2-8B 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.

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 Llama-SEA-LION-v2-8B.

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

We evaluated Llama-SEA-LION-v2-8B on 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 Llama-SEA-LION-v2-8B benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/

Llama-SEA-LION-v2-8B was trained using MosaicML Composer on the following hardware:

Llama-SEA-LION-v2-8B was continued pre-trained on 48B tokens of the following data:

Note:

The training counts and dataset mixture details reported in this model card reflect the exact constructed training pool consumed during this specific model run. Figures may differ slightly from public dataset releases, which represent downloadable open-source subsets of the broader corpus.

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.

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

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.

For more info, please contact us using this SEA-LION Inquiry Form.

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.