aisingapore/Qwen-SEA-LION-v4.5-27B-IT
Qwen-SEA-LION-v4.5-27B-IT is a 27-billion parameter causal language model developed by AI Singapore, built upon the Qwen3.6 dense architecture with a hybrid Linear and Full Attention design. It has been extensively post-trained and instruct-tuned for the Southeast Asia (SEA) region, demonstrating multilingual and multicultural fluency across English and key SEA languages. This model excels in reasoning, agentic coding, and unified vision-language tasks, featuring a large 262K context window and retaining historical reasoning context for iterative development.
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Model Overview
AI Singapore's Qwen-SEA-LION-v4.5-27B-IT is a 27-billion parameter causal language model, part of the SEA-LION collection, specifically designed and optimized for the Southeast Asia (SEA) region. Built on the Qwen3.6 dense architecture, it features a hybrid Linear and Full Attention design and a substantial 262K context window. The model underwent distillation from Qwen3.5-397B-A17B on an updated aisingapore/SEA-Instruct-2602 dataset, enhancing its multilingual and multicultural fluency across English, Burmese, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese.
Key Capabilities
- Multilingual Fluency: Proficient in English and seven key SEA languages, achieved through targeted post-training.
- Advanced Reasoning: Inherits strong reasoning capabilities from the Qwen3.6 foundation, with configurable thinking modes.
- Agentic Coding: High-precision handling of repository-level reasoning and frontend workflows.
- Unified Vision-Language: Early fusion training provides good performance across multimodal reasoning, coding, and visual tasks.
- Context Preservation: Retains historical reasoning context, streamlining iterative development and reducing compute overhead.
Use Cases
This model is particularly well-suited for applications requiring deep understanding and generation in Southeast Asian languages, complex reasoning tasks, and agentic workflows. It has been evaluated on SEA-HELM for general language capabilities and SEA-IFEval/SEA-MTBench for instruction-following and multi-turn chat, demonstrating robust performance in these areas. Developers should note that the model has not been aligned for safety and requires further safety fine-tuning for production use.