Qwen-SEA-LION-v4.5-27B-IT

[Last update: 2026-06-17]

Qwen-SEA-LION-v4.5-27B-IT is built upon the Qwen3.6-27B dense architecture, a 27-billion parameter model featuring a hybrid Linear and Full Attention design. To ensure deep domain adaptation, the model underwent distillation from Qwen3.5-397B-A17B on an updated aisingapore/SEA-Instruct-2602, instilling multilingual and multicultural fluency across English and key SEA languages including: Burmese, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese.

Qwen-SEA-LION-v4.5-27B-IT inherits the following features from Qwen3.6:

• Context Window (262K): Large context window to enable strong reasoning capabilities.
• Reasoning: Highly capable reasoning model, with configurable thinking modes.
• Thinking Preservation: Retains historical reasoning context to streamline iterative development and reduce compute overhead.
• Agentic Coding: High-precision handling of repository-level reasoning and frontend workflows.
• Unified Vision-Language: Early fusion training delivers good performance across multimodal reasoning, coding, and visual tasks.

SEA-LION stands for Southeast Asian Languages In One Network.

We performed post-training in English and SEA languages on Qwen3.6-27B, a multimodal learning model using the Qwen3.6 architecture, to create Qwen-SEA-LION-v4.5-27B-IT.

For tokenization, the model employs the default tokenizer used in Qwen3.6.

• Developed by: AI Products Pillar, AI Singapore
• Funded by: Singapore NRF
• Shared by: AI Products Pillar, AI Singapore
• Model type: Causal Language Model with Vision Encoder
• Training Stage: Post-Training (Logit Distillation & Model Merging))
• Context length: 262k
• Language(s): fine-tuned on Burmese, Indonesian, Filipino, Malay, Tamil, Thai, and Vietnamese
• License: MIT
• Finetuned from model: https://huggingface.co/Qwen/Qwen3.6-27B

For more details, please refer to AI Singapore’s HuggingFace page for this model. The original GGUF files can be obtained from this HuggingFace repository.

Additional note: To disable thinking in your API calls to the model, submit "reasoning_effort": "none" under the parameters.

curl http://localhost:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "aisingapore/Qwen-SEA-LION-v4.5-27B-IT",
    "messages": [
      { "role": "user", "content": "Translate the following to Indonesian: I enjoy playing racquet sports such as tennis." }
    ],
    "stream": false,
    "reasoning_effort": "none"
  }'