SEA-LION v4: Multimodal Language Modeling for Southeast Asia
AI Singapore (AISG) has launched SEA-LION v4, an open-source multimodal language mannequin developed in collaboration with Google and primarily based on the Gemma 3 (27B) structure. The mannequin is designed to assist Southeast Asian languages, together with these with restricted digital assets, and offers each textual content and picture understanding capabilities. SEA-LION v4 makes use of a commercially permissive license and is meant for simple deployment on customary {hardware} platforms.
Benchmark Outcomes: “Small” however State-of-the-Artwork
Efficiency evaluations on the SEA-HELM benchmark—a rigorous multilingual suite designed particularly to check Southeast Asian (SEA) languages—verify SEA-LION v4’s capabilities. Throughout duties in Burmese, Filipino, Indonesian, Malay, Tamil, Thai, and Vietnamese, v4 achieves a high rating amongst fashions underneath 200B parameters, and globally locations #5 out of 55 fashions examined.
This result’s hanging: the mannequin is just not solely outperforming open-source friends like Llama 3, Qwen 3, and Gemma 3, but additionally holding its personal towards proprietary giants with parameter counts a number of occasions bigger.
- Filipino: 74.53 (v4) vs. 74.09 (Gemma 3-27B)
- Malay: 71.31 (v4) vs. 71.20 (Gemma 3-27B)
- Tamil: 68.47 (v4) vs. 68.45 (Gemma 3-27B)
- Burmese: 57.18 (v4) simply behind Gemma 3’s 57.78, outperforming Llama 4 MoE (109B).
In lots of languages, SEA-LION v4 performs on par with or higher than fashions over 3–10x its measurement. This steadiness of effectivity and functionality makes it one of many strongest overtly out there multilingual fashions for each analysis and business use.
What’s New in SEA-LION v4
The fourth-generation mannequin introduces a number of main technical developments that make it uniquely suited to each regional and international purposes:
1. Open Sourced
Not like many closed fashions, SEA-LION v4 is launched underneath the commercially permissive Gemma license, decreasing adoption limitations for startups, researchers, and enterprises. Distribution is supported throughout a number of ecosystems:
- Hugging Face (fine-tuned and base fashions)
- Google Cloud Vertex AI
- AWS SageMaker
- Kaggle for light-weight experimentation
- NVIDIA NIM and Ollama for edge deployment
This openness ensures SEA-LION v4 may be built-in into workflows throughout each cloud-scale enterprises and on-device environments.
2. Effectivity and Portability at Scale
Regardless of its 27B parameters, SEA-LION v4 is designed to run virtually wherever. With quantized variations in FP4 and FP8, customers can obtain:
- <0.5% efficiency drop vs. full precision
- As much as 50% quicker inference
- Deployment on consumer-grade {hardware} (e.g., a laptop computer with 32GB RAM)
This effectivity democratizes entry: a high-quality multimodal mannequin that beforehand required in depth infrastructure is now out there to researchers or builders with modest setups.
3. Multimodality: Textual content + Imaginative and prescient
SEA-LION v4 is the initiative’s first multimodal launch. Past textual content era and understanding, the mannequin can “see,” interpret photos, and mix multimodal info in responses. This makes it extremely related to be used circumstances equivalent to:
- Multilingual doc evaluation and translation with embedded photos
- Picture-grounded query answering in native languages
- Interactive agentic workflows requiring textual content + picture context
The mannequin additionally helps 128K token context home windows, enabling prolonged reasoning over lengthy paperwork, transcripts, or multi-turn prompts, a essential functionality for enterprise and analysis purposes.
4. Agentic and Structured Interactions
SEA-LION v4 consists of instruments past uncooked language era, together with:
- Operate calling—enabling integration with exterior APIs and brokers
- Structured outputs—JSON and schema-compliant generations for downstream automation
- Compatibility with agentic workflows in style in enterprise adoption of LLMs
Collectively, these enhancements lengthen SEA-LION v4 past static Q&A into real-world purposes equivalent to workflow orchestration, analysis assistants, and multimodal enterprise bots.
Skilled for Southeast Asia, Constructed for the World
A singular differentiator of SEA-LION v4 is its coaching basis. The mannequin is educated on over 1 trillion tokens, with heavy emphasis on a curated Southeast Asian dataset. This makes it significantly robust in dealing with low-resource regional languages, dialects, and cultural contexts, the place international basis fashions usually fail.
In SEA-HELM’s Filipino, Malay, Tamil, and Burmese duties, SEA-LION v4 is constantly among the many best-performing fashions throughout all parameter ranges. This makes it a essential enabler for digital fairness in a area the place over 600 million folks depend on numerous linguistic ecosystems.
On the identical time, as a result of it inherits Gemma’s robust general-purpose reasoning, the mannequin stays aggressive in English and international duties, making it a flexible alternative for common deployment.
Conclusion
SEA-LION v4 clarify how fashions with 27B parameters, when optimized and educated on domain-specific knowledge, can obtain aggressive ends in multilingual duties. It provides multilingual efficiency, multimodal capabilities, an open license, and deployability throughout varied platforms, contributing to developments in regional AI fashions.
Take a look at the Model on Hugging Face and SEA-LION Playground. Be at liberty to take a look at our GitHub Page for Tutorials, Codes and Notebooks. Additionally, be at liberty to comply with us on Twitter and don’t overlook to hitch our 100k+ ML SubReddit and Subscribe to our Newsletter.
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