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ANOLE: An Open, Autoregressive, Native Large Multimodal Models for Interleaved Image-Text Generation

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arxiv 2407.06135 v1 pith:WMYI6KBX submitted 2024-07-08 cs.CL cs.AIcs.CV

Ethan Chern , Jiadi Su , Yan Ma , Pengfei Liu This is my paper
classification cs.CLcs.AIcs.CV
keywords generationmultimodalanolelargemodelsnativeautoregressiveimage-text
verification ladder T0 review T1 audit T2 compute T3 formal
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Previous open-source large multimodal models (LMMs) have faced several limitations: (1) they often lack native integration, requiring adapters to align visual representations with pre-trained large language models (LLMs); (2) many are restricted to single-modal generation; (3) while some support multimodal generation, they rely on separate diffusion models for visual modeling and generation. To mitigate these limitations, we present Anole, an open, autoregressive, native large multimodal model for interleaved image-text generation. We build Anole from Meta AI's Chameleon, adopting an innovative fine-tuning strategy that is both data-efficient and parameter-efficient. Anole demonstrates high-quality, coherent multimodal generation capabilities. We have open-sourced our model, training framework, and instruction tuning data.

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Cited by 12 Pith papers

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