itinai content

←back to Blog

SEA-LION v4: Multimodal Language Modeling for Southeast Asia

SEA-LION v4: Multimodal Language Modeling for Southeast Asia

Target Audience Analysis

The primary audience for SEA-LION v4 includes:

  • Researchers: Individuals seeking advanced AI models for linguistic studies, particularly in low-resource languages.
  • Startups and Enterprises: Businesses looking to enhance their applications with multilingual capabilities and image understanding.
  • Developers: Technologists and engineers interested in implementing AI solutions in their products or services.

Key pain points for this audience include:

  • Limited access to high-quality language models that cater to Southeast Asian languages.
  • Challenges in deploying models that require extensive hardware resources.
  • The need for open-source solutions that allow for customization and integration into existing systems.

Goals and interests include:

  • Enhanced multilingual communication in local languages.
  • Integration of AI capabilities in business workflows.
  • Access to cutting-edge research and tools in AI and machine learning.

Communication preferences typically involve:

  • Technical language and detailed specifications.
  • Research outcomes and case studies demonstrating practical applications.
  • Active participation in forums, webinars, and newsletters related to AI advancements.

Overview of SEA-LION v4

AI Singapore (AISG) has released SEA-LION v4, an open-source multimodal language model developed in collaboration with Google and based on the Gemma 3 (27B) architecture. The model is designed to support Southeast Asian languages with limited digital resources and provides both text and image understanding capabilities. SEA-LION v4 uses a commercially permissive license, allowing for straightforward deployment on standard hardware platforms.

Benchmark Results

Performance evaluations on the SEA-HELM benchmark confirm SEA-LION v4’s capabilities across Burmese, Filipino, Indonesian, Malay, Tamil, Thai, and Vietnamese. The model achieves a top ranking among those under 200B parameters, coming in at #5 out of 55 models tested. Notable results include:

  • 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), just behind Gemma 3’s 57.78, outperforming Llama 4 MoE (109B)

In many languages, SEA-LION v4 performs on par with or better than models over 3–10x its size, making it one of the strongest openly available multilingual models for both research and industry use.

What’s New in SEA-LION v4

The fourth-generation model introduces several technical advancements:

  • Open Sourced: Released under the commercially permissive Gemma license, lowering adoption barriers for various users. Distribution is supported across platforms such as Hugging Face, Google Cloud Vertex AI, AWS SageMaker, and NVIDIA NIM.
  • Efficiency and Portability: Designed to run on consumer-grade hardware, SEA-LION v4 includes quantized versions in FP4 and FP8, achieving <0.5% performance drop vs. full precision and up to 50% faster inference.
  • Multimodality: Capable of combining text and image understanding, making it suitable for multilingual document analysis and image-grounded question answering.
  • Agentic and Structured Interactions: Features like function calling and structured outputs (JSON) extend applications to workflow orchestration and enterprise bot integrations.

Conclusion

SEA-LION v4 demonstrates how models with 27B parameters can achieve competitive results in multilingual tasks through optimization and domain-specific training. It offers multilingual performance, multimodal capabilities, an open license, and deployability across various platforms, contributing to advancements in regional AI models.

Explore the model on Hugging Face and SEA-LION Playground. For tutorials, codes, and notebooks, visit our GitHub Page. Follow us on Twitter, and join our 100k+ ML SubReddit for further discussions.