PLaMo 2.1-VL Technical Report

Tommi Kerola; Yuya Masuda; Takashi Masuko; Toshiki Nakanishi; Daisuke Nishino; Kuniyuki Takahashi; Hanqin Wang; Yoshihiro Yamada

arXiv · 2604.19324

PLaMo 2.1-VL Technical Report

Tommi Kerola·Yuya Masuda·Takashi Masuko·Toshiki Nakanishi·Daisuke Nishino·Kuniyuki Takahashi·Hanqin Wang·Yoshihiro Yamada

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Abstract

We introduce PLaMo 2.1-VL, a lightweight Vision Language Model (VLM) for autonomous devices, available in 8B and 2B variants and designed for local and edge deployment with Japanese-language operation. Focusing on Visual Question Answering (VQA) and Visual Grounding as its core capabilities, we develop and evaluate the models for two real-world application scenarios: factory task analysis via tool recognition, and infrastructure anomaly detection. We also develop a large-scale synthetic data generation pipeline and comprehensive Japanese training and evaluation resources. PLaMo 2.1-VL outperforms comparable open models on Japanese and English benchmarks, achieving 61.5 ROUGE-L on JA-VG-VQA-500 and 85.2% accuracy on Japanese Ref-L4. For the two application scenarios, it achieves 53.9% zero-shot accuracy on factory task analysis, and fine-tuning on power plant data improves anomaly detection bbox + label F1-score from 39.7 to 64.9.

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Tommi Kerola, Yuya Masuda, Takashi Masuko, Toshiki Nakanishi, Daisuke Nishino, Kuniyuki Takahashi, Hanqin Wang, Yoshihiro Yamada. 2026-04-21. PLaMo 2.1-VL Technical Report. https://arxiv.org/abs/2604.19324

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