Update README.md

README.md

@@ -6,28 +6,28 @@ language:
 - th
 - vi
 license: llama3
+base_model: meta-llama/Meta-Llama-3-8B-Instruct
+new_version: aisingapore/llama3.1-8b-cpt-sea-lionv3-base
 ---
 # Llama3 8B CPT SEA-LIONv2
 
 SEA-LION is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for the Southeast Asia (SEA) region.
-This is the card for the Llama3 8B CPT SEA-LIONv2 base model which has undergone continued pre-training from the [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) model.
 
-SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
-
-
-## Model Details
+Llama3 8B CPT SEA-LIONv2 Base is a multilingual model which has undergone continued pre-training on approximately **48B** tokens across 5 SEA languages: English, Indonesia, Tamil, Thai and Vietnamese.
 
-### Model Description
-
-The continued pre-training data for Llama3 8B CPT SEA-LIONv2 base model encompasses approximately 48B tokens.
+SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 
 - **Developed by:** Products Pillar, AI Singapore
 - **Funded by:** Singapore NRF
 - **Model type:** Decoder
-- **Languages:** English, Indonesian, Thai, Vietnamese, Tamil
+- **Languages supported:** English, Indonesian, Thai, Vietnamese, Tamil
 - **License:** [Llama3 Community License](https://huggingface.co/meta-llama/Meta-Llama-3-8B/blob/main/LICENSE)
 
-For tokenization, the model employs the default tokenizer used in Meta-Llama-3-8B-Instruct.
+## Model Details
+### Model Description
+We performed continued pre-training in English and SEA languages on [Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct), a decoder model using the Llama 3 architecture, to create Llama3 8B CPT SEA-LIONv2 Base.
+
+For tokenisation, the model employs the default tokenizer used in Llama 3 8B Instruct.
 
 ### Benchmark Performance
 We evaluated Llama3 8B CPT SEA-LIONv2 base model on general language capabilities.
@@ -41,9 +41,27 @@ The evaluation was done **five-shot** with native prompts and only a sample of 1
 For more details on Llama3 8B CPT SEA-LIONv2 base benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
 
 ## Training Details
+### Infrastructure
+Llama3 8B CPT SEA-LIONv2 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+on the following hardware:
+
+| Training Details     | Llama3 8B CPT SEA-LIONv2 |
+|----------------------|:--------------------:|
+| AWS EC2 p5d.24xlarge |          8 instances |
+| Nvidia H100 80GB GPU |          64          |
+| Training Duration    |          2 days      |
 
-### Data
+### Configuration
+| HyperParameter    | Llama3 8B CPT SEA-LIONv2 |
+|-------------------|:--------------------:|
+| Precision         | bfloat16             |
+| Optimizer         | decoupled_adamw      |
+| Scheduler         | weight_stable_decay  |
+| Learning Rate     | 1.0e-5               |
+| Global Batch Size | 512                  |
+| Micro Batch Size  | 2                    |
 
+## Data
 Llama3 8B CPT SEA-LIONv2 base model was continued pre-trained on 48B tokens of the following data:
 
 | Data Source               | Unique Tokens (B) | Multiplier | Total Tokens (B) | Percentage (%) |
@@ -69,83 +87,25 @@ Note:
 - wiki* sources includes Wikipedia, Wiki Books, Wiki Source and Wiki Voyage
 - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)
 
-### Infrastructure
-
-Llama3 8B CPT SEA-LIONv2 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
-on the following hardware:
-
-| Training Details     | Llama3 8B CPT SEA-LIONv2 |
-|----------------------|:--------------------:|
-| AWS EC2 p5d.24xlarge |          8 instances |
-| Nvidia H100 80GB GPU |          64          |
-| Training Duration    |          2 days      |
-
-
-### Configuration
-
-| HyperParameter    | Llama3 8B CPT SEA-LIONv2 |
-|-------------------|:--------------------:|
-| Precision         | bfloat16             |
-| Optimizer         | decoupled_adamw      |
-| Scheduler         | weight_stable_decay  |
-| Learning Rate     | 1.0e-5               |
-| Global Batch Size | 512                  |
-| Micro Batch Size  | 2                    |
-
+## Call for Contributions
+We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of SEA-LION. Contributions can involve identifying and reporting bugs, sharing pre-training, instruction, and preference data, improving documentation usability, proposing and implementing new model evaluation tasks and metrics, or training versions of the model in additional Southeast Asian languages. Join us in shaping the future of SEA-LION by sharing your expertise and insights to make these models more accessible, accurate, and versatile. Please check out our GitHub for further information on the call for contributions.
 
 ## The Team
-
-Choa Esther<br> 
-Cheng Nicholas<br> 
-Huang Yuli<br> 
-Lau Wayne<br> 
-Lee Chwan Ren<br> 
-Leong Wai Yi<br> 
-Leong Wei Qi<br> 
-Li Yier<br> 
-Liu Bing Jie Darius<br> 
-Lovenia Holy<br> 
-Montalan Jann Railey<br> 
-Ng Boon Cheong Raymond<br> 
-Ngui Jian Gang<br> 
-Nguyen Thanh Ngan<br> 
-Ong Brandon<br> 
-Ong Tat-Wee David<br> 
-Ong Zhi Hao<br> 
-Rengarajan Hamsawardhini<br> 
-Siow Bryan<br> 
-Susanto Yosephine<br> 
-Tai Ngee Chia<br> 
-Tan Choon Meng<br> 
-Teo Eng Sipp Leslie<br> 
-Teo Wei Yi<br> 
-Tjhi William<br> 
-Teng Walter<br> 
-Yeo Yeow Tong<br> 
-Yong Xianbin<br>
-
+Cheng Nicholas, Choa Esther, Huang Yuli, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Li Yier, Liu Bing Jie Darius, Lovenia Holy, Montalan Jann Railey, Ng Boon Cheong Raymond, Ngui Jian Gang, Nguyen Thanh Ngan, Ong Brandon, Ong Tat-Wee David, Ong Zhi Hao, Rengarajan Hamsawardhini, Siow Bryan, Susanto Yosephine, Tai Ngee Chia, Tan Choon Meng, Teo Eng Sipp Leslie, Teo Wei Yi, Tjhi William, Teng Walter, Yeo Yeow Tong, Yong Xianbin
 
 ## Acknowledgements
-
-AI Singapore is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore.
-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.
-
+[AI Singapore](​​https://aisingapore.org/) is a national programme supported by the National Research Foundation, Singapore and hosted by the National University of Singapore. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of the National Research Foundation or the National University of Singapore. 
 
 ## Contact
+For more info, please contact us using this [SEA-LION Inquiry Form.](https://forms.gle/sLCUVb95wmGf43hi6)
 
-For more info, please contact us using this [SEA-LION Inquiry Form](https://forms.gle/sLCUVb95wmGf43hi6)
-
-[Link to SEA-LION's GitHub repository](https://github.com/aisingapore/sealion)
-
+[Link to SEA-LION's GitHub repository.](https://github.com/aisingapore/sealion)
 
 ## Disclaimer
-
-This the repository for the base model.
+This is the repository for the commercial instruction-tuned model.
 The model has _not_ been aligned for safety.
 Developers and users should perform their own safety fine-tuning and related security measures.
-In no event shall the authors be held liable for any claim, damages, or other liability
-arising from the use of the released weights and codes.
-
+In no event shall the authors be held liable for any claims, damages, or other liabilities arising from the use of the released weights and codes.
 
 ## References
 ### Thai Pre-Training Data Reference