Emu3 Chat hf EU AI Act Compliance Profile

Emu3 Team, BAAI

Below is the model card of Emu3-Chat model, which is adapted from the original Emu3 model card that you can find here.

Model details

Model type: Emu3 is an open-source multimodal models trained with next-token prediction task. By tokenizing images and text into a discrete space, Emu3 is trained as a single transformer from scratch on a mixture of multimodal sequences. It is an auto-regressive language model, based on the transformer architecture.

Paper or resources for more information: https://github.com/baaivision/Emu3

Highlights

• Emu3 is capable of generating high-quality images following the text input, by simply predicting the next vision token. The model naturally supports flexible resolutions and styles.
• Emu3 shows strong vision-language understanding capabilities to see the physical world and provides coherent text responses. Notably, this capability is achieved without depending on a CLIP and a pretrained LLM.
• Emu3 simply generates a video causally by predicting the next token in a video sequence, unlike the video diffusion model as in Sora. With a video in context, Emu3 can also naturally extend the video and predict what will happen next.
• Emu3 outperforms several well-established task-specific models in both generation and perception tasks, surpassing flagship open models such as SDXL, LLaVA-1.6 and OpenSora-1.2, while eliminating the need for diffusion or compositional architectures.

How to use the model

First, make sure to have transformers >= 4.48.0. Make sure also to follow the correct prompt template (USER: xxxASSISTANT:) and add the

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