---
base_model:
- GoToCompany/gemma2-9b-cpt-sahabatai-v1-base
language:
- en
- id
- jv
- su
license: gemma
---
# Gemma2 9B CPT Sahabat AI V1 Instruct

Sahabat AI is a collection of Large Language Models (LLMs) which has been pretrained and instruct-tuned for Indonesian languages.

Gemma2 9B CPT Sahabat AI v1.0 Instruct is an Indonesian-focused model which has been fine-tuned with around **448,000 Indonesian instruction-completion pairs** alongside an Indonesian-dialect pool consisting of **96,000 instruction-completion pairs in Javanese** and **98,000 instruction-completion pairs in Sundanese**, as well as **129,000 instruction-completion pairs in English**.

Sahabat is Indonesian for "Close Friends"

- **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](https://ai.google.dev/gemma/terms)

## Model Details

### Model Description
We performed instruction tuning in Indonesian, Javanese, Sundanese as well as English on our [continued pre-trained Gemma2 9B CPT Sahabat AI v1.0](https://huggingface.co/GoToCompany/gemma2-9b-cpt-sahabatai-v1-base), a decoder model using the Gemma2 architecture, to create Gemma2 9B CPT Sahabat AI v1.0 Instruct.

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 v1.0 Instruct 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](https://arxiv.org/abs/2309.06085v2) 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](https://arxiv.org/pdf/2310.04928)
  - 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](https://arxiv.org/pdf/2009.03300)

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 **zero-shot** with native prompts on a sample of 100-1000 instances for each dataset.


### Usage
Gemma2 9B CPT Sahabat AI v1.0 Instruct can be run using the 🤗 Transformers library 
```python
# Please use transformers==4.45.2

import transformers
import torch

model_id =  # PLACEHOLDER 

pipeline = transformers.pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device_map="auto",
)
messages = [
    {"role": "user", "content": "Apa sentimen dari kalimat berikut ini?\nKalimat: Buku ini sangat membosankan.\nJawaban: "},
]

outputs = pipeline(
    messages,
    max_new_tokens=256,
)
print(outputs[0]["generated_text"][-1])
```

### Caveats
It is important for users to be aware that our model exhibits certain limitations that warrant consideration. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content, introducing fictional elements that are not grounded in the provided context. Users should also exercise caution in interpreting and validating the model's responses due to the potential inconsistencies in its reasoning.

## Limitations
### Safety

Current Sahabat AI models, including this commercially permissive release, have 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.

## Technical Specifications
### Fine-Tuning Details
Gemma2 9B CPT Sahabat AI v1.0 Instruct was built using a combination of a full parameter fine-tune, on-policy alignment, and model merges of the best performing checkpoints. The training process for fine-tuning was approximately 4 hours, with alignment taking 2 hours, both on 8x H100-80GB GPUs.

## Data
Gemma2 9B CPT Sahabat AI v1.0 Instruct was trained on a wide range of synthetic instructions, alongside publicly available instructions hand-curated by the team with the assistance of native speakers. In addition, special care was taken to ensure that the datasets used had commercially permissive licenses through verification with the original data source. 

## Call for Contributions
We encourage researchers, developers, and language enthusiasts to actively contribute to the enhancement and expansion of Sahabat. 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 Indonesian languages. Join us in shaping the future of Sahabat by sharing your expertise and insights to make these models more accessible, accurate, and versatile.

## The Team (by ascending alphabetical order)

### AI Singapore
Chan Adwin<br>
Chau Shiau Ching<br>
Cheng Nicholas<br>
Choa Esther<br>
Huang Yuli<br>
Lau Wayne<br>
Lee Chwan Ren<br>
Leong Wai Yi<br>
Leong Wei Qi<br>
Limkonchotiwat Peerat<br>
Liu Bing Jie Darius<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>
Teng Walter<br>
Teo Eng Sipp Leslie<br>
Teo Wei Yi<br>
Tjhi William<br>
Yeo Yeow Tong<br>
Yong Xianbin<br>

### PT GoTo Gojek Tokopedia Tbk
Anissa Dininta<br>
Choiri Hendra Hadhil<br>
Goel Priyank<br>
Saini Ajay Kumar<br>
Shalev Ofir<br>
Tan Daryl<br>
Tep Kilian Rithi<br>
Tiwari Anupam<br>
Widjojo Daniel<be>

<!--
## Acknowledgements

 [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 [Sahabat Inquiry Form](https://docs.google.com/forms/d/1_us969eQtEooYOn4XkvGkdP5VHOyCbO6L_sd9kTMnaA/edit)

## 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

```bibtex
@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",
}
}
```