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--- |
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license: mit |
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language: |
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- en |
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- zh |
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- id |
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- ms |
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- th |
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- vi |
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- fil |
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- ta |
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- my |
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- km |
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- lo |
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--- |
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# Note: This model has been deprecated. Please use our latest release at [gemma2-9b-cpt-sea-lionv3-base](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-base). |
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<hr> |
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# SEA-LION |
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SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region. |
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The size of the models range from 3 billion to 7 billion parameters. |
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This is the card for the SEA-LION 7B base model. |
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SEA-LION stands for <i>Southeast Asian Languages In One Network</i>. |
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## Model Details |
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### Model Description |
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The SEA-LION model is a significant leap forward in the field of Natural Language Processing, |
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specifically trained to understand the SEA regional context. |
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SEA-LION is built on the robust MPT architecture and has a vocabulary size of 256K. |
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For tokenization, the model employs our custom SEABPETokenizer, which is specially tailored for SEA languages, ensuring optimal model performance. |
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The training data for SEA-LION encompasses 980B tokens. |
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- **Developed by:** Products Pillar, AI Singapore |
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- **Funded by:** Singapore NRF |
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- **Model type:** Decoder |
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- **Languages:** English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao |
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- **License:** MIT License |
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### Performance Benchmarks |
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SEA-LION has an average performance on general tasks in English (as measured by Hugging Face's LLM Leaderboard): |
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| Model | ARC | HellaSwag | MMLU | TruthfulQA | Average | |
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|-------------|:-----:|:---------:|:-----:|:----------:|:-------:| |
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| SEA-LION 7B | 39.93 | 68.51 | 26.87 | 35.09 | 42.60 | |
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## Training Details |
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### Data |
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SEA-LION was trained on 980B tokens of the following data: |
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| Data Source | Unique Tokens | Multiplier | Total Tokens | Percentage | |
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|---------------------------|:-------------:|:----------:|:------------:|:----------:| |
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| RefinedWeb - English | 571.3B | 1 | 571.3B | 58.20% | |
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| mC4 - Chinese | 91.2B | 1 | 91.2B | 9.29% | |
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| mC4 - Indonesian | 3.68B | 4 | 14.7B | 1.50% | |
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| mC4 - Malay | 0.72B | 4 | 2.9B | 0.29% | |
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| mC4 - Filipino | 1.32B | 4 | 5.3B | 0.54% | |
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| mC4 - Burmese | 1.2B | 4 | 4.9B | 0.49% | |
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| mC4 - Vietnamese | 63.4B | 1 | 63.4B | 6.46% | |
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| mC4 - Thai | 5.8B | 2 | 11.6B | 1.18% | |
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| WangChanBERTa - Thai | 5B | 2 | 10B | 1.02% | |
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| mC4 - Lao | 0.27B | 4 | 1.1B | 0.12% | |
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| mC4 - Khmer | 0.97B | 4 | 3.9B | 0.40% | |
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| mC4 - Tamil | 2.55B | 4 | 10.2B | 1.04% | |
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| the Stack - Python | 20.9B | 2 | 41.8B | 4.26% | |
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| the Stack - Javascript | 55.6B | 1 | 55.6B | 5.66% | |
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| the Stack - Shell | 1.2B5 | 2 | 2.5B | 0.26% | |
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| the Stack - SQL | 6.4B | 2 | 12.8B | 1.31% | |
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| the Stack - Markdown | 26.6B | 1 | 26.6B | 2.71% | |
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| RedPajama - StackExchange | 21.2B | 1 | 21.2B | 2.16% | |
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| RedPajama - ArXiv | 30.6B | 1 | 30.6B | 3.12% | |
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### Infrastructure |
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SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer) |
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on the following hardware: |
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| Training Details | SEA-LION 7B | |
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|----------------------|:------------:| |
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| AWS EC2 p4d.24xlarge | 32 instances | |
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| Nvidia A100 40GB GPU | 256 | |
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| Training Duration | 22 days | |
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### Configuration |
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| HyperParameter | SEA-LION 7B | |
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|-------------------|:------------------:| |
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| Precision | bfloat16 | |
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| Optimizer | decoupled_adamw | |
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| Scheduler | cosine_with_warmup | |
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| Learning Rate | 6.0e-5 | |
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| Global Batch Size | 2048 | |
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| Micro Batch Size | 4 | |
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## Technical Specifications |
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### Model Architecture and Objective |
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SEA-LION is a decoder model using the MPT architecture. |
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| Parameter | SEA-LION 7B | |
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|-----------------|:-----------:| |
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| Layers | 32 | |
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| d_model | 4096 | |
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| head_dim | 32 | |
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| Vocabulary | 256000 | |
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| Sequence Length | 2048 | |
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### Tokenizer Details |
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We sample 20M lines from the training data to train the tokenizer.<br> |
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The framework for training is [SentencePiece](https://github.com/google/sentencepiece).<br> |
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The tokenizer type is Byte-Pair Encoding (BPE). |
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## The Team |
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Lam Wen Zhi Clarence<br> |
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Leong Wei Qi<br> |
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Li Yier<br> |
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Liu Bing Jie Darius<br> |
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Lovenia Holy<br> |
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Montalan Jann Railey<br> |
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Ng Boon Cheong Raymond<br> |
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Ngui Jian Gang<br> |
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Nguyen Thanh Ngan<br> |
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Ong Tat-Wee David<br> |
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Rengarajan Hamsawardhini<br> |
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Susanto Yosephine<br> |
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Tai Ngee Chia<br> |
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Tan Choon Meng<br> |
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Teo Jin Howe<br> |
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Teo Eng Sipp Leslie<br> |
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Teo Wei Yi<br> |
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Tjhi William<br> |
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Yeo Yeow Tong<br> |
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Yong Xianbin<br> |
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## Acknowledgements |
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AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. |
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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. |
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## Contact |
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For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6) |
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[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion) |
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## Disclaimer |
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This the repository for the base model. |
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The model has _not_ been aligned for safety. |
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Developers and users should perform their own safety fine-tuning and related security measures. |
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In no event shall the authors be held liable for any claim, damages, or other liability |
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arising from the use of the released weights and codes. |
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## References |
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### Thai Pre-Training Data Reference |
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```bibtex |
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@misc{lowphansirikul2021wangchanberta, |
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title={WangchanBERTa: Pretraining transformer-based Thai Language Models}, |
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author={Lalita Lowphansirikul and Charin Polpanumas and Nawat Jantrakulchai and Sarana Nutanong}, |
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year={2021}, |
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eprint={2101.09635}, |
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archivePrefix={arXiv}, |
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primaryClass={cs.CL} |
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} |
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``` |
