Domain Adapted Large Language Models for Additive Manufacturing
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 ta...
| Authors | Peter Pak, Amir Barati Farimani |
|---|---|
| Primary category | cs.LG |
| Published | 23 March 2026 |
| Last updated at source | 8 June 2026 |
Models and datasets referencing this paper
- ppak10/AdditiveLLM2-OADataset
- Helosljdlaj/AdditiveLLM2-OADataset
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