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license: apache-2.0
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AndesVL is a suite of mobile-optimized Multimodal Large Language Models (MLLMs) with **0.6B to 4B parameters**, built upon Qwen3's LLM and various visual encoders. Designed for efficient edge deployment, it achieves first-tier performance on diverse benchmarks, including those for text-rich tasks, reasoning tasks, Visual Question Answering (VQA), multi-image tasks, multilingual tasks, and GUI tasks. Its "1+N" LoRA architecture and QALFT framework facilitate efficient task adaptation and model compression, enabling a 6.7x peak decoding speedup and a 1.8 bits-per-weight compression ratio on mobile chips.
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Detailed model sizes and components are provided below:
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license: apache-2.0
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pipeline_tag: image-text-to-text
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library_name: transformers
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<div align="center">
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<h1>AndesVL-1B-Thinking</h1>
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<a href='https://arxiv.org/abs/2510.11496'><img src='https://img.shields.io/badge/arXiv-2510.11496-b31b1b.svg'></a>
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<a href='https://huggingface.co/OPPOer'><img src='https://img.shields.io/badge/🤗%20HuggingFace-AndesVL-ffd21f.svg'></a>
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<a href='https://github.com/OPPO-Mente-Lab/AndesVL_Evaluation'><img src="https://img.shields.io/badge/GitHub-OPPOer-blue.svg?logo=github" alt="GitHub"></a>
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</div>
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AndesVL is a suite of mobile-optimized Multimodal Large Language Models (MLLMs) with **0.6B to 4B parameters**, built upon Qwen3's LLM and various visual encoders. Designed for efficient edge deployment, it achieves first-tier performance on diverse benchmarks, including those for text-rich tasks, reasoning tasks, Visual Question Answering (VQA), multi-image tasks, multilingual tasks, and GUI tasks. Its "1+N" LoRA architecture and QALFT framework facilitate efficient task adaptation and model compression, enabling a 6.7x peak decoding speedup and a 1.8 bits-per-weight compression ratio on mobile chips.
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Detailed model sizes and components are provided below:
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