Komodo
Indonesian large language model supporting 11 regional languages, built on Llama-2 by Yellow.ai researchers.
Overview
Komodo-7B is an Indonesian large language model from Yellow.ai's research team, developed through incremental pre-training and vocabulary expansion on top of Llama-2-7B. It supports Indonesian, English and 11 regional languages of Indonesia including Javanese, Sundanese, Balinese, Acehnese and Minangkabau, with the tokenizer expanded by roughly 3,000 words specific to Indonesian and regional tongues. It ships as Komodo-7B-Base and an instruction-tuned Komodo-7B-Instruct; the base model is published openly on Hugging Face.
Pricing
Pricing shown for reference only. These figures reflect RECATOOLS research as of 24 Jul 2026 and may be out of date or incomplete. This is not financial or purchasing advice — always confirm the current price on the provider’s official website before making any decision.
ASEAN Perspective
Komodo in Southeast Asia
ASEAN-region availability and pricing notes coming soon. Drop the editorial team a note via /contact/ if you can supply local context (Singapore/Malaysia/Indonesia/Thailand/Vietnam).
What this is for: An Indonesian-focused LLM covering Indonesian plus 11 regional languages for local-language NLP.
Who this is for: Developers and researchers building Indonesian and regional-language applications, especially for linguistic inclusion across Indonesia.
Availability: Open base weights on Hugging Face; the base model is not instruction-tuned and needs further fine-tuning for most tasks.
What people say
Komodo-7B has no consumer-review footprint; it's an open research model, and its reception lives in a March 2024 arXiv paper by Yellow.ai researchers, its Hugging Face base checkpoint, and Indonesian tech press (Slator, Heaptalk) that covered it as "the first LLM for Indonesia's regional languages." The press consistently highlights its regional language support for Indonesian plus 11 local tongues, including Javanese, Sundanese, Balinese, Acehnese, and Minangkabau.
Technically the credible detail is the vocabulary work: the team expanded Llama-2's tokenizer with roughly 2,000 Indonesian and 1,000 regional-language words and did incremental pretraining on 8.5B+ tokens, and the paper reports state-of-the-art results on their Indonesian evaluations. There's also independent academic pickup — at least one journal paper builds on Komodo-7B for Indonesian government-administration QA — which is genuine third-party use beyond the authors.
The model's limitations are straightforward. It's built on the now-dated Llama-2. Only the base model is openly published (the instruct variant is harder to find), and those base weights need fine-tuning to be useful. The benchmark claims are largely the authors' own, evaluation for low-resource regional languages is inherently hard to standardize, and there's Beyond Yellow.ai's own customer-support context, there is little evidence of the model being used in production.
Summary of public user & expert reviews, compiled by RECATOOLS.
About this listing
This entry was compiled from publicly available data including Komodo's official website, press releases, documentation, and reputable third-party publications. RECATOOLS is not affiliated with Komodo unless explicitly stated.
Third-party AI tools update their pricing, features, availability, and policies frequently. Information here may be outdated by the time you read this — we make reasonable efforts to keep listings current, but cannot guarantee absolute accuracy.
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