1230亿参数模型深度适配新加坡本地知识，兼顾全球智能与本土应用。
Phoenix-VL 1.5 Medium Technical Report
- 基于1万亿多模态本地语料持续预训练，再用2500亿上下文扩展
- 融合220亿新加坡文化法律语料和50亿对齐数据，性能达领域顶尖
- 专为新加坡政府场景设计，适合本地政策、法律等垂直应用
我们提出Phoenix-VL 1.5 Medium，一个1230亿参数的原生多模态多语言基础模型，专门适配区域语言与新加坡语境。作为主权AI资产，该模型在最小损失通用智能与对齐性的情况下实现深度领域适配。通过在Mistral Medium 3.1上使用1万亿令牌的本地化多模态语料进行持续预训练，并完成2500亿令牌的长上下文扩展阶段。后续后训练引入新型人工标注的新加坡多模态数据集及涵盖新加坡文化、知识与立法的精选文本语料，总计220亿令牌。额外50亿令牌用于在线直接偏好优化实现模型对齐。该模型在新加坡多模态、法律及政府政策基准测试中表现达到同规模最优，同时在全球通用多模态、多语言与STEM基准上保持竞争力。我们还推出新型评估套件，包含本地化知识基准与机构对齐的行为安全框架。报告了数据构建原则、训练方法，并重点展示基准与推理性能。
原文摘要 · Abstract (English)
We introduce Phoenix-VL 1.5 Medium, a 123B-parameter natively multimodal and multilingual foundation model, adapted to regional languages and the Singapore context. Developed as a sovereign AI asset, it demonstrates that deep domain adaptation can be achieved with minimal degradation to broad-spectrum intelligence and alignment. Continued pretraining was performed on Mistral Medium 3.1 using a localized 1-trillion tokens multimodal corpus, followed by a 250-billion tokens long-context extension phase. Subsequent post-training incorporated a novel human-annotated Singapore multimodal dataset and curated textual corpus on Singapore culture, knowledge, and legislation, totaling 22-billion tokens. An additional 5 billion tokens of model alignment was performed through Online Direct Preference Optimization. Phoenix-VL 1.5 Medium achieves state-of-the-art performance for its size on Singapore multimodal, legal, and government policy benchmarks while remaining globally competitive on general multimodal intelligence, multilingual, and STEM benchmarks. We also introduce a novel evaluation suite encompassing localized knowledge benchmarks and an institutionally aligned model behavior and safety framework. We report the data curation principles, training methodology, and highlight benchmark and inference performance.
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