GoTo releases a 27B Indonesian-language model with open weights on Hugging Face: Gemma 3 continued pre-training with AI Singapore, six languages incl. Javanese, Sundanese, Batak and Balinese, 128K context

Why it matters

【Key points】

GoTo, Indonesia's largest internet group (parent of Gojek and Tokopedia), released a 27B-parameter language model for Bahasa Indonesia and regional languages at Bali Tech Week on October 19. Weights are on GoTo's official Hugging Face account as GoToCompany/gemma3-27b-cpt-gotoai-v3-instruct (updated about 01:25 UTC).

Per the model card, it is Google's Gemma 3 27B with continued pre-training plus instruction tuning, initiated by GoTo and developed by GoTo and AI Singapore. It supports English, Indonesian, Javanese, Sundanese, Batak Toba and Balinese with a 128K-token context. FP16/BF16 inference needs about 54–60 GB of VRAM (one A100 80GB, two L40S or three RTX 4090s); vLLM and Transformers usage is provided. Evaluation uses IndoMMLU, SEA-HELM, SEA-IFEval and SEA-MTBench, with scores on the GoTo-AI leaderboard; no independently verified comparisons were provided.

GoTo says training data included Indonesian text from media partners and public-sector websites, with Batak data gathered with Universitas Sumatera Utara. It builds on the Sahabat-AI effort GoTo launched with Indosat in 2024, which already includes a 70B model.

【Verifiable】

Model card: https://huggingface.co/GoToCompany/gemma3-27b-cpt-gotoai-v3-instruct

GoTo HF org: https://huggingface.co/GoToCompany

TNGlobal: https://technode.global/2026/10/19/goto-releases-27b-indonesian-language-model/

【Our read】

1. GoTo calls it open source, but the card lists the Gemma license, not Apache/MIT; check the terms before commercial use.

2. It is continued pre-training on Gemma 3, not a from-scratch model. The point is that a national champion in a 280M-person, many-dialect market is shipping locally deployable open weights, another sign Southeast Asian 'sovereign AI' is going the open-weights route.

3. Benchmarks are self-reported so far; real quality on Indonesian and dialect tasks awaits community testing.