Update README.md

README.md

@@ -18,15 +18,12 @@ library_name: transformers
 pipeline_tag: text-generation
 ---
 # Gemma2 9B CPT SEA-LIONv3
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 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 Gemma2 9B CPT SEA-LIONv3 base model which has undergone continued pre-training from the base [Gemma-2-9B](https://huggingface.co/google/gemma-2-9b) model.
 
 SEA-LION stands for <i>Southeast Asian Languages In One Network</i>.
 
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 ## Model Details
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 ### Model Description
 
 The continued pre-training data for Gemma2 9B CPT SEA-LIONv3 base model encompasses approximately 200B tokens.
@@ -53,9 +50,7 @@ The evaluation was done **five-shot** with native prompts on a sample of 100-100
 For more details on Gemma2 9B CPT SEA-LIONv3 base benchmark performance, please refer to the SEA HELM leaderboard, https://leaderboard.sea-lion.ai/
 
 ## Training Details
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 ### Data
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 Gemma2 9B 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 (%)|
@@ -103,7 +98,6 @@ Note:
 - Tamil news is sourced with permission from [Seithi](https://seithi.mediacorp.sg/)
 
 ### Infrastructure
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 Gemma2 9B CPT SEA-LIONv3 was trained using [MosaicML Composer](https://github.com/mosaicml/composer)
 on the following hardware:
 
@@ -113,9 +107,7 @@ on the following hardware:
 | Nvidia H100 80GB GPU |        64+8              |
 | Training Duration    |        10 days           |
 
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 ### Configuration
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 | HyperParameter    | Gemma2 9B CPT SEA-LIONv3 |
 |-------------------|:------------------------:|
 | Precision         | bfloat16                 |
@@ -125,32 +117,27 @@ on the following hardware:
 | Global Batch Size | 512                      |
 | Micro Batch Size  | 1                        |
 
+## Indonesian, Javanese & Sudanese Specific SEA-LION
+Our partners at GoTo have continued pretrained and instruction tuned a variant of Gemma2 9B CPT SEA-LIONv3, specifically enhancing its capabilities for Indonesian, Javanese, and Sundanese languages. Find the continued pretrained model at [Gemma2 9B CPT SahabatAIv1 Base](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), and its corresponding instructioned tuned version at [Gemma2 9B CPT SahabatAIv1 Instruct](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-instruct). 
 
 ## The Team
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 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
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 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.
 
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 ## Contact
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 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)
 
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 ## Disclaimer
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 This is the repository for the base 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.
 
-
 ## References
 ### Thai Pre-Training Data Reference