用5000万词数据微调大模型，让AI精通3D打印知识
Domain Adapted Large Language Models for Additive Manufacturing
- 基于Gemma/Qwen等开源模型，用5000万词的3D打印文献进行领域预训练
- 在专用评测集上准确率达90%以上，语言与视觉任务均表现优异
- 方法轻量易复现，适合想快速定制工业AI的工程师和研究者
本研究构建了一系列基于指令微调开源大模型（Gemma 3、Qwen 3、Gemma 4）的多模态领域自适应大模型，使用约5000万词的公开3D打印期刊文章数据进行领域自适应预训练与视觉指令微调。所开发模型在包含多个3D打印特定任务的Additive-Manufacturing-Benchmark评测中表现出色，语言与视觉任务准确率均超过90%，展现了在增材制造领域的强知识理解能力。该领域自适应预训练与指令微调策略为大模型向3D打印等专业领域高效迁移提供了可复现的可行路径。
原文摘要 · Abstract (English)
This work presents a collection of multi-modal domain adapted large language models built upon the instruction tuned variants of open weight models (Gemma 3, Qwen 3, Gemma 4) using a relatively small dataset of around 50 million tokens. The dataset consists of open-access additive manufacturing journal articles with data extracted for the domain adaptive pretraining and visual instruction tuning processes. Various stages of the developed model are evaluated with the Additive-Manufacturing-Benchmark which consists of additive manufacturing domain specific tasks compiled published resources. Domain adapted and instruction tuned models exhibit proficiency in both language and vision based tasks, achieving accuracies upwards of 90% in general additive manufacturing knowledge. This domain adaptive pretraining and instruction tuning strategy outline an accessible specialization method for large language models to a domain such as additive manufacturing.
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