license: apache-2.0
Model Card for SEA LION
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
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]
- Repository: Coming soon
- Paper [optional]: Coming soon
- Demo [optional]: Coming soon
Uses
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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Recommendations
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.
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Training Details
Training Data
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
Preprocessing [optional]
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Training Hyperparameters
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Speeds, Sizes, Times [optional]
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Evaluation
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
Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).
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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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