Sea Lion V4: Multi-mode modeling in Southeast Asia
AI Singapore (AISG) has released Sea-Lion V4, an open source multi-model model developed in collaboration with Google and based on the Gemma 3 (27b) architecture. The model is designed to support Southeast Asian languages, including languages with limited digital resources, and provides text and image comprehension capabilities. Sea-Lion V4 uses commercially permitted licenses designed to be deployed directly on standard hardware platforms.
Benchmark results: “small” but most advanced
Performance evaluation Conch spiral reference– Strict multilingual suite dedicated to testing Southeast Asian (SEA) languages - Confirm Sea-Lion V4’s capabilities. Cross-task Myanmar, Philippines, Indonesian, Malay, Tamil, Thai and VietnameseV4 has been implemented Ranking in models with 200b parametersout of 55 models tested, the world’s No. 5 position.
This result is shocking: not only does the model outperform open source peers such as Llama 3, Qwen 3 and Gemma 3, but also performs several times the number of proprietary giants with parameter counts.
- the Philippines: 74.53 (V4) vs 74.09 (Gemma 3-27b)
- Malay: 71.31 (V4) vs. 71.20 (Gemma 3-27b)
- Tamils: 68.47 (V4) vs. 68.45 (Gemma 3-27b)
- Myanmar: 57.18 (V4) is just behind Gemma 3’s 57.78, outperforming the Llama 4 Moe (109b).
In many languages, the Sea Lion V4 performs comparable or better than models 3-10 times higher. The balance of efficiency and capability makes it one of the most powerful open multilingual models used in research and industry.
New in Sea-Lion V4
The fourth generation model has introduced several Major technological advances This makes it ideal for regional and global applications:
1. Open source
Unlike many closed models, Sea Lion V4 is in Commercially permitted Gemma licensesreduce barriers to adoption for startups, researchers and businesses. Support distribution in multiple ecosystems:
- Hug the face (fine-tuning and basic model)
- Google Cloud Vertex AI
- AWS Sagemaker
- Kaggle For lightweight experiments
- Nvidia Nim and Ollama For edge deployment
This openness ensures that Sea-Lion V4 can be integrated into workflows in cloud-scale enterprises and device environments.
2. Large-scale efficiency and portability
Despite the 27b parameter, the Sea-Lion V4 is designed to run almost anywhere. and Quantitative versions in FP4 and FP8users can implement:
- With complete accuracy
- Up to 50% inference
- Deploy on consumer hardware (e.g., laptop with 32GB RAM)
This efficiency democratizes access: a high-quality multi-model model that previously required a wide range of infrastructure to be used by researchers or developers with modest settings.
3. Multimodal: Text + Vision
Sea Lion V4 is an initiative The first multi-mode release. In addition to text generation and understanding, the model can also “view”, interpret images, and incorporate multi-modal information into the response. This makes it relevant to the following use cases.
- Multilingual document analysis and translation with embedded images
- The question of image grounding is answered in local language
- Interactive proxy workflow requires text + image context
This model also supports 128K token context windowcan be extended inference on long documents, transcripts or multiple turn tips, which is a key capability for businesses and research applications.
4. Agent and structured interactions
Sea-Lion V4 includes tools other than original language generation, including:
- Function Calls– Integration with external APIs and proxy
- Structured output– Downstream automation of JSON and compliant patterns
- and Agent workflow Popular in LLM’s corporate adoption
These enhancements extend Sea-Lion V4 beyond static Q&A, extending it to real-world applications such as workflow orchestration, research assistants, and multimodal enterprise robots.
Training for Southeast Asia and building for the world
The unique difference between Sea Lion V4 is its training foundation. The model has been trained More than 1 trillion tokensstrongly emphasized Curated Southeast Asian dataset. This makes it particularly powerful in dealing with low-resource regional languages, dialects, and cultural contexts, where global basic models often fail.
Sea-Lion V4 is always one of the best performing models in all parameter ranges in Sea-Helm’s Filipino, Malay, Tamil and Burmese tasks. This makes it The key driver of digital equity In a region where more than 600 million people rely on a multilingual ecosystem.
At the same time, since it inherits Gemma’s powerful general reasoning, the model is English and global missionsmaking it a versatile option for universal deployments.
in conclusion
Sea-Lion V4 explains how models using 27b parameters can achieve competitive results in multilingual tasks when optimized and trained on domain-specific data. It provides multilingual performance, multimodal capabilities, open licenses and deployability across a variety of platforms, which contribute to advancement in regional AI models.
Check Model embracing face and Sea Lion Playground. Check out ours anytime Tutorials, codes and notebooks for github pages. Also, please stay tuned for us twitter And don’t forget to join us 100K+ ml reddit And subscribe Our newsletter.
Asif Razzaq is CEO of Marktechpost Media Inc. As a visionary entrepreneur and engineer, ASIF is committed to harnessing the potential of artificial intelligence to achieve social benefits. His recent effort is to launch Marktechpost, an artificial intelligence media platform that has an in-depth coverage of machine learning and deep learning news that can sound both technically, both through technical voices and be understood by a wide audience. The platform has over 2 million views per month, demonstrating its popularity among its audience.
