AMALIA-VL: A Native European Portuguese Open-Source Vision and Language Model

• URL: http://arxiv.org/abs/2606.19100v1
• Date: Wed, 17 Jun 2026 14:11:41 GMT
• Title: AMALIA-VL: A Native European Portuguese Open-Source Vision and Language Model
• Abstract summary: AMALIA-VL is the first open-source instruction-tuned LVLM built for pt-PT.<n>We will release model weights, training data, and construction pipelines along with machine-translated pt-PT evaluation benchmarks to help democratize pt-PT LVLM development.
• Score: 6.462620395914082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Vision and Language Models (LVLMs) have advanced rapidly, yet European Portuguese (pt-PT) remains systematically underserved by existing open-source multimodal models, which either conflate it with Brazilian Portuguese or severely under-represent it in their training data mixes. We introduce AMALIA-VL, the first open-source instruction-tuned LVLM built natively for pt-PT, pairing a high-resolution vision encoder with dynamic image tiling and a fully open pt-PT-optimized language model via a learned connector. We contribute with a purposefully designed three-stage training process - vision-language alignment, general visual instruction tuning, and preference optimization - together with a pt-PT-centric multimodal data mix combining curated and translated public datasets with novel datasets that address the near-total absence of European Portuguese multimodal resources. Our evaluation shows that AMALIA-VL establishes a strong baseline for open-source pt-PT LVLMs.We will release model weights, training data, and construction pipelines along with machine-translated pt-PT evaluation benchmarks to help democratize pt-PT LVLM development.

Related papers

• Annotation-Efficient Vision-Language Model Adaptation to the Polish Language Using the LLaVA Framework [38.98519875112922]
  Vision-language models (VLMs) are trained on English-centric data, limiting their performance in other languages and cultural contexts.<n>We reproduce and adapt the LLaVA-Next methodology to create a set of Polish VLMs.<n>We observe a +9.5% improvement over LLaVA-1.6-una-13B on a Polish-adapted MMBench, along with higher-quality captions in generative evaluations.
  arXiv  Detail & Related papers  (2026-02-15T09:54:40Z)
• NaViL: Rethinking Scaling Properties of Native Multimodal Large Language Models under Data Constraints [100.02131897927484]
  This paper focuses on the native training of Multimodal Large Language Models (MLLMs) in an end-to-end manner.<n>We propose a native MLLM called NaViL, combined with a simple and cost-effective recipe.<n> Experimental results on 14 multimodal benchmarks confirm the competitive performance of NaViL against existing MLLMs.
  arXiv  Detail & Related papers  (2025-10-09T17:59:37Z)
• InternVL3: Exploring Advanced Training and Test-Time Recipes for Open-Source Multimodal Models [139.19991097260115]
  We introduce InternVL3, a significant advancement in the InternVL series featuring a native multimodal pre-training paradigm.<n>In particular, InternVL3-78B achieves a score of 72.2 on the MMMU benchmark, setting a new state-of-the-art among open-source MLLMs.<n>In pursuit of open-science principles, we will publicly release both the training data and model weights to foster further research and development in next-generation MLLMs.
  arXiv  Detail & Related papers  (2025-04-14T17:59:25Z)
• Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling [191.7830199016589]
  We introduce InternVL 2.5, an advanced multimodal large language model (MLLM) series that builds upon InternVL 2.0.<n>InternVL 2.5 exhibits competitive performance, rivaling leading commercial models such as GPT-4o and Claude-3.5-Sonnet.<n>We hope this model contributes to the open-source community by setting new standards for developing and applying multimodal AI systems.
  arXiv  Detail & Related papers  (2024-12-06T18:57:08Z)
• NVLM: Open Frontier-Class Multimodal LLMs [64.00053046838225]
  We introduce NVLM 1.0, a family of frontier-class multimodal large language models (LLMs) that achieve state-of-the-art results on vision-language tasks. We propose a novel architecture that enhances both training efficiency and multimodal reasoning capabilities. We develop production-grade multimodality for the NVLM-1.0 models, enabling them to excel in vision-language tasks.
  arXiv  Detail & Related papers  (2024-09-17T17:59:06Z)
• MLLM-LLaVA-FL: Multimodal Large Language Model Assisted Federated Learning [25.45278447786954]
  We introduce a novel federated learning framework, named Multimodal Large Language Model Assisted Federated Learning (MLLM-LLaVA-FL)<n>Our framework is adept at harnessing the extensive, yet previously underexploited, open-source data accessible from websites and powerful server-side computational resources.
  arXiv  Detail & Related papers  (2024-09-09T21:04:16Z)
• Visual Anchors Are Strong Information Aggregators For Multimodal Large Language Model [82.93634081255942]
  We propose a vision-language connector that enables MLLMs to achieve high accuracy while maintain low cost. We first reveal the existence of the visual anchors in Vision Transformer and propose a cost-effective search algorithm to extract them. We introduce the Anchor Former (AcFormer), a novel vision-language connector designed to leverage the rich prior knowledge obtained from these visual anchors during pretraining.
  arXiv  Detail & Related papers  (2024-05-28T04:23:00Z)
• mBLIP: Efficient Bootstrapping of Multilingual Vision-LLMs [50.17767479660832]
  Vision-language models (Vision-LLMs) align pretrained image encoders with (frozen) large language models (LLMs) and post-hoc condition LLMs to understand' the image input. We present mBLIP, the first Vision-LLM leveraging multilingual LLMs, which we obtain in a computationally efficient manner on consumer-level hardware.
  arXiv  Detail & Related papers  (2023-07-13T17:51:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.