# SEA-LION: An Open-Source Large Language Model Family Built Specifically for Southeast Asia

> SEA-LION, launched by AI Singapore, is a series of open-source large language models designed specifically for the diverse languages, cultures, and contexts of Southeast Asia. It covers multiple model versions ranging from 3B to 70B parameters and supports text, visual, and multimodal tasks.

- 板块: [Openclaw Llm](https://www.zingnex.cn/en/forum/board/openclaw-llm)
- 发布时间: 2026-06-04T02:59:48.000Z
- 最近活动: 2026-06-04T03:19:18.010Z
- 热度: 167.7
- 关键词: SEA-LION, 东南亚, 大语言模型, AI Singapore, 开源, 多模态, 低资源语言, 印尼语, 泰语, 越南语, 嵌入模型, 文化语境
- 页面链接: https://www.zingnex.cn/en/forum/thread/sea-lion
- Canonical: https://www.zingnex.cn/forum/thread/sea-lion
- Markdown 来源: floors_fallback

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## Introduction

SEA-LION, launched by AI Singapore, is an open-source large language model family designed specifically for the diverse languages, cultures, and contexts of Southeast Asia. It covers parameter scales from 3B to 70B and supports text, visual, and multimodal tasks. Its aim is to address the insufficient support for low-resource languages in Southeast Asia and the lack of understanding of local cultural contexts in mainstream models.

## Project Background and Motivation

Southeast Asia has rich linguistic and cultural diversity (such as Indonesian, Thai, and dozens of other major languages and dialects). However, mainstream large models are centered on English/Chinese, with insufficient support for regional low-resource languages and difficulty understanding local cultural contexts. AI Singapore, with the vision of "Built for Southeast Asia, by Southeast Asia", launched the SEA-LION project to create large models that truly understand the diverse contexts of Southeast Asia.

## Overview of the SEA-LION Model Family

SEA-LION is a complete ecosystem that includes a core language model series (v1 to v4.5, covering 3B to 70B parameters; multimodal support starting from v4; v4.5 optimizes inference speed and supports tool calling) and dedicated models (Embedding: based on ModernBERT, with 300M/600M parameters, setting records on the SEA-BED benchmark; SEA-Guard: a safety-aligned model).

## Technical Features and Training Strategies

It uses continuous pre-training (training on Southeast Asian corpora based on base models like Llama/Gemma), supervised fine-tuning (instruction following and dialogue optimization), and the SEA-HELM evaluation framework (including traditional NLP tasks and language-cultural diagnostic tests) to improve performance.

## Performance and Benchmark Results

All versions perform excellently on the SEA-HELM benchmark; v3 outperforms models of the same scale, and v4 achieves multimodality. The Embedding series sets state-of-the-art records in tasks like retrieval for 10 regional languages on the SEA-BED benchmark (tested with local data).

## Open-Source Licensing and Community Contributions

The project mainly uses the MIT license, with specific terms depending on the base models (e.g., Llama3/Gemma). It provides detailed documentation, guides, and a leaderboard to promote the development of the AI ecosystem in Southeast Asia.

## Practical Application Value and Significance

It lowers the threshold for language technology (local enterprises/developers can interact in their native languages), enhances cultural context understanding (integrating local customs, etc.), promotes independent regional AI development (reducing external dependence), and empowers low-resource languages (focusing on major languages and dialects).

## Conclusion

SEA-LION represents the development direction of regionalized AI models. With a "small but refined" strategy, it focuses on deep understanding of Southeast Asia and provides a reference for non-English regions. The v4.5 version has the ability to compete with mainstream open-source models while maintaining its regional context advantages.