Levelmodel
ClassLLM
Parametersnot in the notes
Contextnot in the notes
Released11 Aug 2025
Opennessopen weights restricted
Runs onnot in the notes
Statusactive
Benchmark Results
maintainer-run
SEA-HELMSoutheast Asian and Indonesian
Checkpoint Gemma-SEA-LION-v4-27B-IT as listed; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
70.55 95% CI [-1.42, +1.46]
04080
Score in score. Higher is better.
SEA-HELM leaderboard, 58 models, Indonesian (ID)
Checkpoint Gemma-SEA-LION-v4-27B-IT as listed; release view (data last updated 18 Sep 2026); score is the average of 2000 bootstrapsmaintainer-run
64.35 95% CI [-0.72, +0.71]
04080
Score in score. Higher is better.
SEA-HELM leaderboard, 58 models, SEA average
Where this comes from
Every score names who ran it: vendor-reported, maintainer-run or independent. A rank is shown with the population it was ranked in (gap rule 3).
Lineage
instruct offinetune
Gemma-SEA-LION-v4-27B is the parent (instruct of)high confidenceHugging Face base_modelWhere this comes from
Parent and child relations from model cards, Hugging Face base_model fields and the genealogy tables of the research notes. The tree shows two generations each way, 3 models. Confidence and evidence are the notes' own.
R&D Topics
Identity
- Developer
- AI Singapore
- Family
- not in the notes
- Released
- 11 Aug 2025 sourcesregistry date
- Weights published
- –
- Record checked
- 10 Oct 2026
Where this comes from
Developer and dates from the research notes. Release date is the vendor's public announcement; weights date is when open weights were published.
Architecture and Size
- Architecture
- not in the notes
- Total parameters
- – sources
- Active parameters
- –
- Checkpoint parameters
- –
- Note
- 27B
- Context length
- not in the notes
- Input
- text
- Output
- text
Where this comes from
Parameter counts as the developer states them. Where the checkpoint holds a different total, both are shown (register A16).
Openness and Access
- Openness
- open weights restricted
- Licence
- Gemma Terms of Use sources
- Access routes
- open weights download
Where this comes from
The licence file governs over registry tags and aggregator labels.
Deployment
- Runs on
- not in the notes
- Footprint
- not in the notes
- Runtimes
- not in the notes
Where this comes from
Deployment tiers and runtimes as named in the notes. Footprint is the notes' figure, not a measurement by AURION.
Indonesia
- Indonesian support
- not stated
- Regional languages
- not in the notes
- Jakarta hosting
- unknown
- Languages
- not in the notes
Where this comes from
Indonesia fields are carried on every model; unknown is an allowed value.
Industry Use Cases
No industry use case links this model.
Links
DeveloperAI Singaporenational lab, Singapore
AI Singaporenational lab, SingaporeModelGemma-SEA-LION-v4-27B-ITAI Singapore, LLM
Gemma-SEA-LION-v4-27B-ITAI Singapore, LLMR&D TopicsLow-Resource and Regional Language ResourcesOpen-Weight Frontier Releases as Research SubstrateRegional or National Foundation Model Training (Continued Pretraining at 7B to 70B)
Use CasesIndonesian and Regional-Language LLMsPublic SectorIndonesian NLU (Classification, NER)Public SectorBanking Customer Service AssistantsFinanceCitizen Service AssistantsPublic Sector+8 moreUse cases of models in the same R&D topic; none is recorded for this model itself.
Who Runs It
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026
PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026
PT PegadaianIn production, 5 Jun 2026
Bank Central Asia (BCA)In production, 16 Mar 2026+19 moreDeployments of these use cases; none names this model.
PT Amman Mineral Nusa Tenggara (AMMAN)In production, 8 Jul 2026PT Bank Tabungan Negara (BTN)In production, 1 Jul 2026PT PegadaianIn production, 5 Jun 2026Bank Central Asia (BCA)In production, 16 Mar 2026+19 moreDeployments of these use cases; none names this model.Organisations 1
R&D topics 3
Open-Weight Frontier Releases as Research SubstrateUsing released weights as the base for downstream research.used byRegional or National Foundation Model Training (Continued Pretraining at 7B to 70B)Pretraining or continued pretraining a mid-size model to serve a national or regional lan…produced byLow-Resource and Regional Language ResourcesDatasets, benchmarks and adaptation for under-served languages.produced by
Sources
- huggingface.co/api/models/aisingapore/Gemma-SEA-LION-v4-27B https://huggingface.co/api/models/aisingapore/Gemma-SEA-LION-v4-27B
- huggingface.co/aisingapore/Gemma-SEA-LION-v4-27B-IT https://huggingface.co/aisingapore/Gemma-SEA-LION-v4-27B-IT
To cite this page: Project AURION, AI Use-Cases Research & Innovation, "Gemma-SEA-LION-v4-27B-IT", https://aurion.semai.id/models/ai-singapore-gemma-sea-lion-v4-27b-it/, records of October 2026.