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# Base model for paper "AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling"
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<a href='https://junzhan2000.github.io/AnyGPT.github.io/'><img src='https://img.shields.io/badge/Project-Page-Green'></a> <a href='https://arxiv.org/pdf/2402.12226.pdf'><img src='https://img.shields.io/badge/Paper-Arxiv-red'></a> [](https://huggingface.co/datasets/fnlp/AnyInstruct)
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## Introduction
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We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. The [base model](https://huggingface.co/fnlp/AnyGPT-base) aligns the four modalities, allowing for intermodal conversions between different modalities and text. Furthermore, we constructed the [AnyInstruct](https://huggingface.co/datasets/fnlp/AnyInstruct) dataset based on various generative models, which contains instructions for arbitrary modal interconversion. Trained on this dataset, our [chat model](https://huggingface.co/fnlp/AnyGPT-chat) can engage in free multimodal conversations, where multimodal data can be inserted at will.
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# Base model for paper "AnyGPT: Unified Multimodal LLM with Discrete Sequence Modeling"
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## Introduction
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We introduce AnyGPT, an any-to-any multimodal language model that utilizes discrete representations for the unified processing of various modalities, including speech, text, images, and music. The [base model](https://huggingface.co/fnlp/AnyGPT-base) aligns the four modalities, allowing for intermodal conversions between different modalities and text. Furthermore, we constructed the [AnyInstruct](https://huggingface.co/datasets/fnlp/AnyInstruct) dataset based on various generative models, which contains instructions for arbitrary modal interconversion. Trained on this dataset, our [chat model](https://huggingface.co/fnlp/AnyGPT-chat) can engage in free multimodal conversations, where multimodal data can be inserted at will.
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