HunyuanOCR Integrations

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Hugging Face is an AI community platform that provides state-of-the-art machine learning models, datasets, and APIs to help developers build intelligent applications. The platform’s extensive repository includes models for text generation, image recognition, and other advanced machine learning tasks. Hugging Face’s open-source ecosystem, with tools like Transformers and Tokenizers, empowers both individuals and enterprises to build, train, and deploy machine learning solutions at scale. It offers integration with major frameworks like TensorFlow and PyTorch for streamlined model development.

HunyuanVision, an innovative vision-language model created by Tencent's Hunyuan team, employs a mamba-transformer hybrid architecture that excels in performance and offers efficient inference for multimodal reasoning challenges. The latest iteration, Hunyuan-Vision-1.5, focuses on the concept of “thinking on images,” enabling it to not only comprehend the interplay of visual and linguistic content but also engage in advanced reasoning that includes tasks like cropping, zooming, pointing, box drawing, or annotating images for enhanced understanding. This model is versatile, supporting various vision tasks such as image and video recognition, OCR, and diagram interpretation, in addition to facilitating visual reasoning and 3D spatial awareness, all within a cohesive multilingual framework. Designed for compatibility across different languages and tasks, HunyuanVision aims to be open-sourced, providing access to checkpoints, a technical report, and inference support to foster community engagement and experimentation. Ultimately, this initiative encourages researchers and developers to explore and leverage the model's capabilities in diverse applications.

arXiv serves as a free platform and open-access repository housing 2,228,103 scholarly articles across a variety of disciplines, including physics, mathematics, computer science, quantitative biology, quantitative finance, statistics, electrical engineering, systems science, and economics. The contents available on this platform do not undergo a peer-review process conducted by arXiv. Users can submit articles, utilize a TeX compilation service, access search and discovery tools, and benefit from web distribution intended for human consumption, as well as API access and machine-readable datasets, alongside tools developed by the community. The focus on openness, collaboration, and academic integrity is a fundamental aspect that sustains the growth and vitality of arXiv. Built upon principles such as transparency, open-mindedness, collaboration, and adaptability, arXiv fosters an engaging environment for knowledge sharing. Rather than being mere recipients of information, our institutional members, collaborators, moderators, authors, and readers form an active and integral part of the arXiv community, contributing to its ongoing success and evolution. This collaborative spirit not only enhances the platform but also enriches the scholarly landscape as a whole.