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 # MAmmoTH-VL-8B
 
-[🏠 Homepage](https://mammoth-vl.github.io/) | [🤖 MAmmoTH-VL-8B](https://huggingface.co/MAmmoTH-VL/MAmmoTH-VL-8B) | [💻 Code](https://github.com/orgs/MAmmoTH-VL/MAmmoTH-VL) | [📄 Arxiv](https://arxiv.org/abs/2412.05237) | [📕 PDF](https://arxiv.org/pdf/2412.05237) | [🖥️ Demo](https://huggingface.co/spaces/paralym/MAmmoTH-VL-8B)
+[🏠 Homepage](https://mammoth-vl.github.io/) | [🤖 MAmmoTH-VL-8B](https://huggingface.co/MAmmoTH-VL/MAmmoTH-VL-8B) | [💻 Code](https://github.com/MAmmoTH-VL/MAmmoTH-VL) | [📄 Arxiv](https://arxiv.org/abs/2412.05237) | [📕 PDF](https://arxiv.org/pdf/2412.05237) | [🖥️ Demo](https://huggingface.co/spaces/paralym/MAmmoTH-VL-8B)
 
 # Abstract
 Open-source multimodal large language models (MLLMs) have shown significant potential in a broad range of multimodal tasks. However, their reasoning capabilities remain constrained by existing instruction-tuning datasets, which were predominately repurposed from academic datasets such as VQA, AI2D, and ChartQA. These datasets target simplistic tasks, and only provide phrase-level answers without any intermediate rationales.