Abstract
Open-weights large language models remain difficult to deploy for Thai due to unstable generation under complex instructions, despite strong English performance. To mitigate these limitations, We present SiamGPT-32B, an open-weights model based on Qwen3-32B, fine-tuned with a Quality-First strategy emphasizing curated supervision over data scale. The fine-tuning pipeline combines translated high-complexity English instruction data with a Thai-adapted AutoIF framework for instruction and linguistic constraints. Using supervised fine-tuning only, without continual pretraining or corpus expansion, SiamGPT-32B improves instruction adherence, multi-turn robustness, and linguistic stability. Evaluations on the SEA-HELM benchmark show that SiamGPT-32B achieves the strongest overall performance among similar-scale open-weights Thai models, with consistent gains in instruction following, multi-turn dialogue, and natural language understanding.
Abstract (translated)
开源权重的大规模语言模型在泰语部署中仍然面临挑战，因为它们在处理复杂指令时生成的结果不稳定，尽管这些模型在英语上的表现很强。为了缓解这一问题，我们提出了SiamGPT-32B，这是一个基于Qwen3-32B的开源权重模型，并通过强调精心策划监督而非数据规模的Quality-First策略进行了微调。 该微调管道结合了翻译后的高复杂度英文指令数据与适应泰国语言环境的AutoIF框架，用于处理指令和语言约束。仅使用有监督的微调，而没有持续预训练或语料库扩展的情况下，SiamGPT-32B在遵循指令、多轮对话稳定性和语言稳定性方面都有所改进。 在SEA-HELM基准测试中，SiamGPT-32B展示了与其他类似规模的开源权重泰语模型相比最强的整体性能，并且在遵循指令、多轮对话和自然语言理解方面均有持续提升。
URL
https://arxiv.org/abs/2512.19455
