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license: apache-2.0
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# Model Card for SEA LION

<!-- Provide a quick summary of what the model is/does. -->
SEA LION is a collection of LLMs which has been pretrained and instruct-tuned for the Southeast Asia region.
The models range from 3 billion to 7 billion parameters.
This is the repository for the 3B pretrained model.

## Model Details

### Model Description

<!-- Provide a longer summary of what this model is. -->
The SEA LION model is a significant leap forward in the field of natural language processing and understanding,
specifically trained to understand South-East Asia (SEA) regional context.
SEA LION stands for SouthEast Asian Languages In One Network.
The SEA LION model comes in two variants, one with 3 billion parameters and another with 7 billion parameters.
Both variants are built on the robust MPT architecture and utilize a vocabulary size of 256K.
The model employs our proprietary SEABPETokenizer for tokenization.
Our SEABPETokenizer is specially tailored for SEA languages, ensuring optimal model performance.
The training data for SEA LION is encompasses 1 trillion tokens.


- **Developed by:** Products Pillar, AI Singapore
- **Funded by [optional]:** Singapore NRF
- **Shared by [optional]:** N/A
- **Model type:** Decoder
- **Language(s) (NLP):** English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino/Tagalog, Tamil, Burnese, Khmer, Lao
- **License:** Apache 2.0
- **Finetuned from model [optional]:** N/A

### Model Sources [optional]

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- **Repository:** _Coming soon_
- **Paper [optional]:** _Coming soon_
- **Demo [optional]:** _Coming soon_

## Uses

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### Direct Use

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### Downstream Use [optional]

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### Out-of-Scope Use

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## Bias, Risks, and Limitations

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[More Information Needed]

### Recommendations

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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.

## How to Get Started with the Model

Use the code below to get started with the model.

[More Information Needed]

## Training Details

### Training Data

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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).

| Data Source            | Tokens | Percentage |
|------------------------|--------|------------|
| RefinedWeb - English   | 571.3B |     62.80% |
| mC4 - Chinese          |  91.2B |     10.03% |
| mC4 - Indonesian       |   3.6B |      0.40% |
| mC4 - Malay            |   0.7B |      0.08% |
| mC4 - Filipino/Tagalog |   1.3B |      0.15% |
| mC4 - Burmese          |   1.2B |      0.13% |
| mC4 - Vietnamese       |  63.4B |      6.97% |
| mC4 - Thai             |  10.8B |      1.19% |
| mC4 - Lao              |   0.3B |      0.03% |
| mC4 - Khmer            |   0.9B |      0.11% |
| mC4 - Tamil            |   2.5B |      0.28% |
| Python                 |  20.9B |      2.30% |
| Javascript             |  55.6B |      6.11% |
| Shell                  |   1.3B |      0.14% |
| SQL                    |   6.4B |      0.70% |
| Markdown               |  26.6B |      2.91% |
| StackExchange          |  21.2B |      2.33% |
| ArXiv                  |  30.6B |      3.35% |

### Training Procedure 

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#### Preprocessing [optional]

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#### Training Hyperparameters

- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->

#### Speeds, Sizes, Times [optional]

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[More Information Needed]

## Evaluation

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### Testing Data, Factors & Metrics

#### Testing Data

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#### Factors

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#### Metrics

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### Results

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#### Summary



## Model Examination [optional]

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## Environmental Impact

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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).

- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]

## Technical Specifications [optional]

### Model Architecture and Objective

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### Compute Infrastructure

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#### Hardware

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#### Software

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## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## More Information [optional]

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## Model Card Authors [optional]

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## Model Card Contact

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