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toeniant
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Model Details
Model Provider
toeniant
Model Tree
aisingapore/Qwen-SEA-LION-v4.5-27B-IT
Input Modalities
Output Modalities
Supported Functionality
Qwen-SEA-LION-v4.5-27B-IT 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.
Key features inherited from Qwen3.6:
bash
pip install vllm>=0.7vllm serve toeniant/Qwen-SEA-LION-v4.5-27B-IT-NVFP4A16 \--quantization compressed-tensors \--max-model-len 262144
python
from vllm import LLM, SamplingParamsllm = LLM(model="toeniant/Qwen-SEA-LION-v4.5-27B-IT-NVFP4A16",quantization="compressed-tensors",max_model_len=262144,)sampling = SamplingParams(temperature=0.7,top_p=0.80,top_k=20,max_tokens=512,)messages = [{"role": "user", "content": "Tolong carikan flat 4-bilik dekat Tampines, bajet bawah $500,000."}]out = llm.chat(messages, sampling_params=sampling)print(out[0].outputs[0].text)
For benchmark results, please refer to the SEA-LION Leaderboard and the original model card.
The model has not been aligned for safety. Developers and users should perform their own safety fine-tuning and related security measures. Like many LLMs, the model can hallucinate and occasionally generates irrelevant content. Users should exercise caution in interpreting and validating the model's responses.
Original model by AI Singapore, supported by the National Research Foundation Singapore and IMDA under its National Large Language Model Funding Initiative. See the original model card for full credits and team information.
Original authors: sealion@aisingapore.org