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license: apache-2.0 |
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# Model Card for SEA LION |
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<!-- Provide a quick summary of what the model is/does. --> |
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SEA LION is a collection of LLMs which has been pretrained and instruct-tuned for the Southeast Asia region. |
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The models range from 3 billion to 7 billion parameters. |
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This is the repository for the 3B pretrained model. |
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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 and understanding, |
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specifically trained to understand South-East Asia (SEA) regional context. |
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SEA LION stands for SouthEast Asian Languages In One Network. |
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The SEA LION model comes in two variants, one with 3 billion parameters and another with 7 billion parameters. |
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Both variants are built on the robust MPT architecture and utilize a vocabulary size of 256K. |
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The model employs our proprietary SEABPETokenizer for tokenization. |
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Our SEABPETokenizer is specially tailored for SEA languages, ensuring optimal model performance. |
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The training data for SEA LION is encompasses 1 trillion tokens. |
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- **Developed by:** Products Pillar, AI Singapore |
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- **Funded by [optional]:** Singapore NRF |
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- **Shared by [optional]:** N/A |
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- **Model type:** Decoder |
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- **Language(s) (NLP):** English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino/Tagalog, Tamil, Burnese, Khmer, Lao |
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- **License:** Apache 2.0 |
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- **Finetuned from model [optional]:** N/A |
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### Model Sources [optional] |
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- **Repository:** _Coming soon_ |
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- **Paper [optional]:** _Coming soon_ |
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- **Demo [optional]:** _Coming soon_ |
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## Uses |
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### Direct Use |
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<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. --> |
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[More Information Needed] |
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### Downstream Use [optional] |
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<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app --> |
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[More Information Needed] |
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### Out-of-Scope Use |
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<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. --> |
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[More Information Needed] |
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## Bias, Risks, and Limitations |
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<!-- This section is meant to convey both technical and sociotechnical limitations. --> |
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[More Information Needed] |
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### Recommendations |
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<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. --> |
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations. |
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## How to Get Started with the Model |
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Use the code below to get started with the model. |
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[ Todo: Insert Code Here ] |
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## Training Details |
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### Training Data |
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<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. --> |
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SEA LION 3B was trained on 980B tokens of RefinedWeb (English) and mC4 (Chinese, Indonesian, Malay, Filipino/Tagalog, Burmese, Vietnamese, Thai, Lao, Khmer, Tamil). |
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| Data Source | Tokens | Percentage | |
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|------------------------|--------|------------| |
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| RefinedWeb - English | 571.3B | 62.80% | |
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| mC4 - Chinese | 91.2B | 10.03% | |
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| mC4 - Indonesian | 3.6B | 0.40% | |
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| mC4 - Malay | 0.7B | 0.08% | |
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| mC4 - Filipino/Tagalog | 1.3B | 0.15% | |
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| mC4 - Burmese | 1.2B | 0.13% | |
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| mC4 - Vietnamese | 63.4B | 6.97% | |
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| mC4 - Thai | 10.8B | 1.19% | |
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| mC4 - Lao | 0.3B | 0.03% | |
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| mC4 - Khmer | 0.9B | 0.11% | |
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| mC4 - Tamil | 2.5B | 0.28% | |
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| Python | 20.9B | 2.30% | |
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| Javascript | 55.6B | 6.11% | |
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| Shell | 1.3B | 0.14% | |
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| SQL | 6.4B | 0.70% | |
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| Markdown | 26.6B | 2.91% | |
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| StackExchange | 21.2B | 2.33% | |
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| ArXiv | 30.6B | 3.35% | |
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### Training Procedure |
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. --> |
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SEA LION 3B was trained on 256 A100 40GB GPUs, using MosaicML Composer. |
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#### Preprocessing [optional] |
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N/A |
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#### Training Hyperparameters |
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| Hyperparameter | Value | |
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|-------------------|-------------------| |
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| Precision | bfloat16 | |
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| Optimizer | decoupled_adamw | |
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| Scheduler | cosin_with_warmup | |
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| Learning Rate | 1.6e-4 | |
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| Global Batch Size | 1200 | |
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| Micro Batch Size | 5 | |
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#### Speeds, Sizes, Times [optional] |
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The training took 14 days to complete. |
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## Evaluation |
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### Testing Data, Factors & Metrics |
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#### Testing Data |
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<!-- This should link to a Dataset Card if possible. --> |
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_Coming soon_ |
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#### Factors |
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. --> |
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_Coming soon_ |
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#### Metrics |
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<!-- These are the evaluation metrics being used, ideally with a description of why. --> |
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_Coming soon_ |
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### Results |
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_Coming soon_ |
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#### Summary |
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## Model Examination [optional] |
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<!-- Relevant interpretability work for the model goes here --> |
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[More Information Needed] |
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## Environmental Impact |
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly --> |
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700). |
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- **Hardware Type:** [More Information Needed] |
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- **Hours used:** [More Information Needed] |
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- **Cloud Provider:** [More Information Needed] |
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- **Compute Region:** [More Information Needed] |
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- **Carbon Emitted:** [More Information Needed] |
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## Technical Specifications [optional] |
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### Model Architecture and Objective |
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SEA LION 3B is a decoder model using the MPT architecture. |
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| Parameter | Value | |
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|-----------------|--------| |
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| Layers | 40 | |
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| d_model | ? | |
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| head_dim | ? | |
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| Vocabulary | 256000 | |
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| Sequence Length | 2048 | |
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### Compute Infrastructure |
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#### Hardware |
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SEA LION 3B was trained on AWS EC2 cluster comprising 32 p4d.24xlarge instances, using a total of 256 A100 40GB GPUs. |
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#### Software |
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SEA LION 3B was trained using MosaicML Composer using PyTorch FullyShardedDataParallelism (FSDP). |
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## Citation [optional] |
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. --> |
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**BibTeX:** |
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N/A |
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**APA:** |
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N/A |
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## Glossary [optional] |
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. --> |
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N/A |
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## More Information [optional] |
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N/A |
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## The Team |
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Hamsawardhini Rengarajan<br> |
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Holy Lovenia<br> |
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Lam Clarence<br> |
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Leong Weiqi<br> |
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Li Yier<br> |
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Ng Raymond<br> |
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Ngui Jian Gang<br> |
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Railey Montalan<br> |
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Tai Ngee Chia<br> |
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Tan Choon Meng<br> |
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Thanh Ngan Nguyen<br> |
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Teo Jin Howe<br> |
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Teo Wei Yi<br> |
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Yeo Yeow Tong<br> |
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Yong Xianbin<br> |
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Yosephine<br> |
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William Tjhi<br> |
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David Ong Tat-Wee<br> |
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Darius Liu<br> |
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Leslie Teo<br> |
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## Model Card Contact |
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[ Todo: Get AISG Contact ] |
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