language:
- en
- zh
- vi
- id
- th
- fil
- ta
- ms
- km
- lo
- my
license: llama3.1
library_name: transformers
pipeline_tag: text-generation
base_model: meta-llama/Llama-3.1-70B-Instruct
Llama3.1 70B CPT SEA-LIONv3
SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
Llama3.1 70B CPT SEA-LIONv3 Base is a multilingual model which has undergone continued pre-training on approximately 200B tokens across 11 SEA languages: Burmese, Chinese, English, Filipino, Indonesia, Khmer, Lao, Malay, 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: Burmese, Chinese, English, Filipino, Indonesia, Khmer, Lao, Malay, Tamil, Thai, Vietnamese.
- License: Llama 3.1 Community License
Model Details
Model Description
We performed continued pre-training in English and SEA languages on Llama-3.1-70B-Instruct, a decoder model using the Llama 3.1 architecture, to create Llama3.1 70B CPT SEA-LIONv3 Base.
For tokenisation, the model employs the default tokenizer used in Llama 3.1 70B Instruct.
Benchmark Performance
We evaluated Llama3.1 70B CPT SEA-LIONv3 base model on general language capabilities and constraint-following behaviour.
General Language Capabilities and Constraint-following Behaviour
For the evaluation of general language capabilities, we employed the SEA-HELM (also known as 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 Summarisation (Abssum), Causal Reasoning (Causal) and Natural Language Inference (NLI).
Note: SEA-HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.
The evaluation was done five-shot with native prompts on a sample of 100-1000 instances for each dataset.
Following the implementation of IFEval in OpenLLM leaderboard, we also implement SEA-IFEval to provide a comparison of the ability of the model to follow specific constraints in English and in SEA languages.
SEA-IFEval
Based on IFEval, the linguists and native speakers in the team worked together to filter, localise and translate the datasets into the respective target languages to ensure that the examples remained reasonable, meaningful and natural.
SEA-IFEval evaluates a model's ability to adhere to constraints provided in the prompt, for example beginning a response with a specific word/phrase or answering with a certain number of sections. Additionally, accuracy is normalised by the proportion of responses in the correct language (if the model performs the task correctly but responds in the wrong language, it is judged to have failed the task).
For more details on Llama3.1 70B CPT SEA-LIONv3 base benchmark performance, please refer to the SEA-HELM leaderboard, https://leaderboard.sea-lion.ai/.
Technical Specifications
Infrastructure
Llama3.1 70B CPT SEA-LIONv3 was trained in two stages using MosaicML Composer on the following hardware:
| Stage | Training Details | Llama3.1 70B CPT SEA-LIONv3 |
|---|---|---|
| First Stage | AWS p5e.48xlarge | 8 instances |
| Nvidia H200 140GB GPU | 64 | |
| Training Duration | 200 hrs (step 0 - 9000) | |
| Second Stage | SingTel HGX-100 | 16 instances |
| Nvidia H100 80GB GPU | 128 | |
| Training Duration | 495 hrs (step 9000 - 47684) |
Configuration
| HyperParameter | Llama3.1 70B CPT SEA-LIONv3 |
|---|---|
| Precision | bfloat16 |
| Optimizer | decoupled_adamw |
| Scheduler | weight_stable_decay |
| Learning Rate | 1.0e-5 |
| Global Batch Size | 512 |
Data
Llama3.1 70B CPT SEA-LIONv3 base model was continued pre-trained on 200B tokens of the following data:
| Language | Source | Total Tokens (B) | Percentage (%) | Total percentage (%) |
|---|---|---|---|---|
| Code | StackV2 | 40 | 20 | 20 |
| English | Dolma | 37.5 | 18.75 | 25 |
| Fineweb-Edu | 7.5 | 3.75 | ||
| Others | 5 | 2.5 | ||
| Chinese | SEA-LION Pile v1 | 12 | 6 | 13 |
| Others | 14 | 7 | ||
| Vietnamese | SEA-LION Pile v1 | 8.4 | 4.2 | 13 |
| VinBigData | 16 | 8 | ||
| Others | 1.6 | 0.8 | ||
| Indonesian | SEA-LION Pile v1 | 7 | 3.5 | 13 |
| SEA-LION Pile v2 | 7 | 3.5 | ||
| Others | 12 | 6 | ||
| Thai | SEA-LION Pile v1 | 10.7 | 5.35 | 10 |
| WangChanBERTa | 8.5 | 4.25 | ||
| Others | 0.8 | 0.4 | ||
| Filipino - Malay - Tamil | SEA-LION Pile v1, AI4Bharat Sangraha | 4.28 | 2.14 | 3 |
| Others | 1.72 | 0.86 | ||
| Khmer - Lao - Burmese | SEA-LION Pile v1 | 5.2 | 2.6 | 3 |
| Others | 0.8 | 0.4 |
Note:
- All token counts are counted using Llama 3.1 70B Instruct tokenizer
- SEA-LION Pile v1 is processed from Common Crawl WET, which is published here. The cutoff date of this version is September 2020.
- SEA-LION Pile v2 is processed from Common Crawl WARC from October 2020 to April 2024.
- Tamil data from Sangraha is published here. The paper can be found here.
- 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
Chan Adwin, Cheng Nicholas, Choa Esther, Huang Yuli, Hulagadri Adithya Venkatadri, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, 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, Teng Walter, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, 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}
}