aisingapore
/

sea-lion-7b

+# SEA-LION
+SEA-LION is a collection of LLMs which has been pretrained and instruct-tuned for the South-East Asia (SEA) region.
+The models range from 3 billion to 7 billion parameters.
+This is the card for the SEA-LION 7B model.
+SEA-LION stands for <i>South-East Asia Languages In One Network</i>.
+## 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 is 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 encompasses 980B tokens.
+- **Developed by:** Products Pillar, AI Singapore
+- **Funded by:** Singapore NRF
+- **Model type:** Decoder
+- **Language(s) (NLP):** English, Chinese, Indonesian, Malay, Thai, Vietnamese, Filipino, Tamil, Burmese, Khmer, Lao
+- **License:** MIT License
+## Training Details
+### Data
+SEA-LION was trained on 980B tokens of the following data:
+| 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            |   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% |
+| the Stack - Python        |  20.9B |      2.30% |
+| the Stack - Javascript    |  55.6B |      6.11% |
+| the Stack - Shell         |   1.3B |      0.14% |
+| the Stack - SQL           |   6.4B |      0.70% |
+| the Stack - Markdown      |  26.6B |      2.91% |
+| RedPajama - StackExchange |  21.2B |      2.33% |
+| RedPajama - ArXiv         |  30.6B |      3.35% |
+### Infrastructure
+SEA-LION was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+on the following hardware:
+| Training Details     | SEA-LION 7B  |
+|----------------------|:------------:|
+| AWS EC2 p4d.24xlarge | 32 instances |
+| Nvidia A100 40GB GPU | 256          |
+| Training Duration    | 22 days      |
+### Configuration
+| HyperParameter    | SEA-LION 7B        |
+|-------------------|:------------------:|
+| Precision         | bfloat16           |
+| Optimizer         | decoupled_adamw    |
+| Scheduler         | cosine_with_warmup |
+| Learning Rate     | 6.0e-5             |
+| Global Batch Size | 2048               |
+| Micro Batch Size  | 4                  |
+## Technical Specifications
+### Model Architecture and Objective
+SEA-LION is a decoder model using the MPT architecture.
+| Parameter       | SEA-LION 7B |
+|-----------------|:-----------:|
+| Layers          | 32          |
+| d_model         | 4096        |
+| head_dim        | 32          |
+| Vocabulary      | 256000      |
+| Sequence Length | 2048        |
+### Tokenizer Details
+We sample 20M lines from the training data to train the tokenizer.<br>
+The framework for training is [SentencePiece](https://github.com/google/sentencepiece).<br>
+The tokenizer type is Byte-Pair Encoding (BPE).
+## The Team
+Hamsawardhini Rengarajan<br>
+Lam Zhiwen Clarence<br>
+Leong Weiqi<br>
+Li Yier<br>
+Liu Darius<br>
+Lovenia Holy<br>
+Ng Raymond<br>
+Ngui Jian Gang<br>
+Ong Tat-Wee David<br>
+Railey Montalan<br>
+Tai Ngee Chia<br>
+Tan Choon Meng<br>
+Thanh Ngan Nguyen<br>
+Teo Jin Howe<br>
+Teo Wei Yi<br>
+William Tjhi<br>
+Yeo Yeow Tong<br>
+Yong Xianbin<br>
+Yosephine<br>
+Leslie Teo<br>
+## Contact
+For more info, please contact us at [email protected]