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Multimodal · Retrieval · Training

CM3Leon

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CM3Leon
The curator’s take

Meta's retrieval-augmented multi-modal language model that generates both text and images.

Key points
01

Autoregressive multi-modal: Unifies text and image generation in a single autoregressive token-based architecture, handling both modalities in any order.

02

5x training efficiency: Achieves SOTA image generation quality with 5x less training compute than comparable methods due to retrieval augmentation and instruction tuning.

03

Instruction tuning for images: Demonstrates that supervised fine-tuning and instruction tuning - originally developed for LLMs - also massively improves multimodal generation quality.

04

Any-to-any direction: Early proof-of-concept for unified any-to-any multi-modal models, pre-dating and inspiring 2024 systems like Chameleon and GPT-4o.

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