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metadata
base_model:
  - aisingapore/gemma2-9b-cpt-sea-lionv3-base
language:
  - en
  - id
  - jv
  - su
license: gemma

Gemma2 9B CPT Sahabat AI v1

Sahabat AI v1 is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian languages. This is the card for the Gemma2 9B CPT Sahabat AI v1 base model which has undergone continued pre-training from the base Gemma2 9B CPT SEA-LIONv3 base model.

Sahabat is Indonesian for "Close Friends."

Model Details

Model Description

The continued pre-training data for Gemma2 9B CPT Sahabat AI v1 base model encompasses approximately 50B tokens.

  • Developed by: PT GoTo Gojek Tokopedia Tbk, AI Singapore
  • Funded by: PT GoTo Gojek Tokopedia Tbk, AI Singapore
  • Model type: Decoder
  • Languages: English, Indonesian, Javanese, Sundanese
  • License: Gemma Community License

For tokenisation, the model employs the default tokenizer used in Gemma-2-9B. The model has a context length of 8192.

Benchmark Performance

We evaluated Gemma2 9B CPT Sahabat AI v1 base model on general language capabilities.

General Language Capabilities

For the evaluation of general language capabilities, we employed the

  • SEA HELM (also known as BHASA) evaluation benchmark across a variety of tasks.
    • These tasks include Question Answering (QA), Sentiment Analysis (Sentiment), Toxicity Detection (Toxicity), Translation in both directions (Eng>Lang & Lang>Eng), Abstractive Summarization (Summ), Causal Reasoning (Causal) and Natural Language Inference (NLI).
    • We also added support for Javanese and Sundanese for the BHASA tasks whenever applicable
  • IndoMMLU
    • These tasks include examination questions on Humanities, Indonesian language, Local languages and cultures, Social science and STEM across primary, middle, and high school levels.
  • and the well known English MMLU

Note: SEA HELM is implemented using prompts to elicit answers in a strict format. For all tasks, the model is expected to provide an answer tag from which the answer is automatically extracted. For tasks where options are provided, the answer should comprise one of the pre-defined options. The scores for each task is normalised to account for baseline performance due to random chance.

The evaluation was done five-shot with native prompts on a sample of 100-1000 instances for each dataset.

Training Details

Data

Gemma2 9B CPT Sahabat AI v1 base model was continued pre-trained on 50B tokens of the following data:

Data Source Unique Tokens (B) Multiplier Total Tokens (B) Percentage (%)
Dolma Refined Web 9.5 1 9.5 17.36
Dolma arXiv 0.6 1 0.6 1.10
Dolma Star Coder 5.5 1 5.5 10.05
Dolma Semantic Scholar 1.2 1 1.2 2.19
Dolma Reddit 1.7 1 1.7 3.11
Dolma C4 1.5 1 1.4 2.56
Wiki* + News* - Indonesian 1.3 4 5.2 9.50
SEA-LION Pile - Indonesian 27.0 1 27.0 49.34
SEA-LION Pile - Javanese 0.5 1.5 0.75 1.37
CC 100 - Javanese 0.05 1.5 0.075 0.14
HPLT - Javanese 0.3 1.5 0.45 0.82
SEA-LION Pile - Sundanese 0.2 3.6 0.75 1.37
CC 100 - Sundanese 0.02 3.6 0.075 0.14
HPLT - Sundanese 0.16 3.6 0.45 0.82
Others (Javanese, Sundanese) 0.034 2.2 0.076 0.14

Note:

  • All token counts are counted using Gemma2 tokenizer
  • Wiki* sources includes Wikipedia, Wiki Books, Wiki Source, Wiki Voyage and Fandom Wiki
  • News* sources includes VOA, Global Voices, MediaCorp

Infrastructure

Gemma2 9B CPT Sahabat AI v1 was trained using MosaicML Composer on the following hardware:

Training Details Gemma2 9B CPT Sahabat AI v1
Nvidia H100 80GB GPU 32
Training Duration 7 days

Configuration

HyperParameter Gemma2 9B CPT Sahabat AI v1
Precision bfloat16
Optimizer decoupled_adamw
Scheduler weight_stable_decay
Learning Rate 1.0e-5
Global Batch Size 256
Micro Batch Size 1

The Team (by ascending alphabetical order)

AI Singapore

Chan Adwin
Chau Shiau Ching
Cheng Nicholas
Choa Esther
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
Teng Walter
Teo Eng Sipp Leslie
Teo Wei Yi
Tjhi William
Yeo Yeow Tong
Yong Xianbin

PT GoTo Gojek Tokopedia Tbk

Anissa Dininta
Choiri Hendra Hadhil
Goel Priyank
Saini Ajay Kumar
Shalev Ofir
Tan Daryl
Tep Kilian Rithi
Tiwari Anupam
Widjojo Daniel

Contact

For more info, please contact us using this Sahabat Inquiry Form.

Disclaimer

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

IndoMMLU Reference 

@inproceedings{koto-etal-2023-indommlu,
    title = "Large Language Models Only Pass Primary School Exams in {I}ndonesia: A Comprehensive Test on {I}ndo{MMLU}",
    author = "Fajri Koto and Nurul Aisyah and Haonan Li and Timothy Baldwin",
    booktitle = "Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP)",
    month = December,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
}
}