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README.md
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## Introduction
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We present
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- **Task Mix.** For all-purpose capabilities, we mix a variety of vision-language tasks for mutual improvement: VQA, REC, REG, OCR, etc.
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- **Domain Mix.** For data from real-world and synthetic domains, we mix the weights of two domain-specific models for complementarity.
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<p align="left">
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<img src="figs/pipeline1.png"/ width="
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</p>
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<p align="left">
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<img src="figs/pipeline2.png"/ width="
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</p>
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## Result
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<p align="left">
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<img src="figs/table1.png"/ width="
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</p>
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<p align="left">
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<img src="figs/table2.png"/ width="
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</p
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<p align="left">
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<img src="figs/table3.png"/ width="
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</p>
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<p align="left">
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<img src="figs/table4.png"/ width="
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</p>
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## Inference
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## Introduction
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We present SPHINX, a versatile multi-modal large language model (MLLM) with a mixer of training tasks, data domains, and visual embeddings.
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- **Task Mix.** For all-purpose capabilities, we mix a variety of vision-language tasks for mutual improvement: VQA, REC, REG, OCR, etc.
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- **Domain Mix.** For data from real-world and synthetic domains, we mix the weights of two domain-specific models for complementarity.
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<p align="left">
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<img src="figs/pipeline1.png"/ width="100%"> <br>
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</p>
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<p align="left">
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<img src="figs/pipeline2.png"/ width="100%"> <br>
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</p>
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## Result
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**Evaluation Prompt Design**
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<p align="left">
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<img src="figs/table1.png"/ width="100%"> <br>
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</p>
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**Benchmarks on Multimodal Large Language Models**
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<p align="left">
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<img src="figs/table2.png"/ width="100%"> <br>
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</p
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**Visual Question Answering**
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<p align="left">
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<img src="figs/table3.png"/ width="100%"> <br>
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</p>
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**Visual Grounding**
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<p align="left">
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<img src="figs/table4.png"/ width="100%"> <br>
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</p>
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## Inference
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