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This is the GAIR Anole project, which aims to build and opensource large multimodal models with comprehensive multimodal understanding and generation capabilities.

Anole is the first open-source, autoregressive, and natively trained large multimodal model capable of interleaved image-text generation (without using stable diffusion). While it builds upon the strengths of Chameleon, Anole excels at the complex task of generating coherent sequences of alternating text and images. Through an innovative fine-tuning process using a carefully curated dataset of approximately 6,000 images, Anole achieves remarkable image generation and understanding capabilities with minimal additional training. This efficient approach, combined with its open-source nature, positions Anole as a catalyst for accelerated research and development in multimodal AI. Preliminary tests demonstrate Anole's exceptional ability to follow nuanced instructions, producing high-quality images and interleaved text-image content that closely aligns with user prompts.

We have provided open-source model weights, code, and detailed tutorials below to ensure that each of you can reproduce these results, and even fine-tune the model to create your own stylistic variations. (Democratization of technology is always our goal.)

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