Update README.md

README.md

@@ -20,14 +20,10 @@ base_model: meta-llama/Llama-3.1-8B-Instruct
 ---
 # Llama3.1 8B CPT SEA-LIONv3
 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 model card for the Llama3.1 8B CPT SEA-LIONv3 base model which has undergone continued pre-training from the instruct [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) model.
 
-SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
-
-## Model Details
-### Model Description
+Llama3.1 8B CPT SEA-LIONv3 Base is a multilingual model which has undergone continued pre-training from [Llama-3.1-8B-Instruct](https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct) on English and Southeast Asian text.
 
-The continued pre-training data for Llama3.1 8B CPT SEA-LIONv3 base model encompasses approximately 200B tokens.
+SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 
 - **Developed by:** Products Pillar, AI Singapore
 - **Funded by:** Singapore NRF
@@ -35,6 +31,10 @@ The continued pre-training data for Llama3.1 8B CPT SEA-LIONv3 base model encomp
 - **Languages:** English, Chinese, Vietnamese, Indonesian, Thai, Filipino, Tamil, Malay, Khmer, Lao, Burmese, Javanese, Sundanese
 - **License:** [Llama 3.1 Community License](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
 
+## Model Details
+### Model Description
+The continued pre-training data for Llama3.1 8B CPT SEA-LIONv3 Base encompasses approximately 200B tokens.
+
 For tokenisation, the model employs the default tokenizer used in Llama3.1 8B Instruct.
 
 ### Benchmark Performance
@@ -50,8 +50,28 @@ The evaluation was done **five-shot** with native prompts on a sample of 100-100
 
 For more details on Llama3.1 8B CPT SEA-LIONv3 base benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
 
-## Training Details
-### Data
+## Technical Specifications
+### Infrastructure
+Llama3.1 8B CPT SEA-LIONv3 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
+on the following hardware:
+
+| Training Details     | Llama3.1 8B CPT SEA-LIONv3 |
+|----------------------|:------------------------:|
+| SingTel HGX-100      |        8+1 instances     |
+| Nvidia H100 80GB GPU |        64+8              |
+| Training Duration    |        10 days           |
+
+### Configuration
+| HyperParameter    | Llama3.1 8B CPT SEA-LIONv3 |
+|-------------------|:------------------------:|
+| Precision         | bfloat16                 |
+| Optimizer         | decoupled_adamw          |
+| Scheduler         | weight_stable_decay      |
+| Learning Rate     | 1.0e-5                   |
+| Global Batch Size | 512                      |
+| Micro Batch Size  | 1                        |
+
+## Data
 Llama3.1 8B CPT SEA-LIONv3 base model was continued pre-trained on 200B tokens of the following data:
 
 | Data Source                           | Unique Tokens (B) | Multiplier | Total Tokens (B) | Percentage (%)|
@@ -98,43 +118,25 @@ Note:
 - News* sources includes VOA, Global Voices, MediaCorp, VinBigData-News
 - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)
 
-### Infrastructure
-Llama3.1 8B CPT SEA-LIONv3 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
-on the following hardware:
-
-| Training Details     | Llama3.1 8B CPT SEA-LIONv3 |
-|----------------------|:------------------------:|
-| SingTel HGX-100      |        8+1 instances     |
-| Nvidia H100 80GB GPU |        64+8              |
-| Training Duration    |        10 days           |
-
-### Configuration
-| HyperParameter    | Llama3.1 8B CPT SEA-LIONv3 |
-|-------------------|:------------------------:|
-| Precision         | bfloat16                 |
-| Optimizer         | decoupled_adamw          |
-| Scheduler         | weight_stable_decay      |
-| Learning Rate     | 1.0e-5                   |
-| Global Batch Size | 512                      |
-| Micro Batch Size  | 1                        |
+## 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
 Chan Adwin, Choa Esther, Cheng Nicholas, Huang Yuli, Lau Wayne, Lee Chwan Ren, Leong Wai Yi, Leong Wei Qi, Limkonchotiwat Peerat, Liu Bing Jie Darius, 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 is 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