Emu2
Maintainer: BAAI
84
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| Property | Value |
|---|---|
| Run this model | Run on HuggingFace |
| API spec | View on HuggingFace |
| Github link | No Github link provided |
| Paper link | No paper link provided |
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Model overview
Emu2 is a generative multimodal model with 37 billion parameters, developed by the Beijing Academy of Artificial Intelligence (BAAI). It was trained on large-scale multimodal sequences with a unified autoregressive objective to enhance its multimodal in-context learning abilities. Emu2 exhibits strong performance on various multimodal understanding and generation tasks, even in few-shot settings, setting new state-of-the-art results on several benchmarks.
Similar models include ul2, which uses a mixture-of-denoisers pre-training objective to create a universally effective model across datasets and setups, and bge-m3, a versatile multilingual, multimodal model that supports dense, sparse, and multi-vector retrieval.
Model inputs and outputs
Inputs
- Images:
Emu2can accept high-resolution images up to 1.8 million pixels (e.g., 1344x1344) at any aspect ratio. - Text: The model can process both short and long text inputs, with a maximum sequence length of 8192 tokens.
- Multimodal prompts:
Emu2is capable of understanding and generating content based on a combination of visual and textual inputs.
Outputs
- Text generation: The model can generate coherent, contextually relevant text in response to prompts.
- Multimodal generation:
Emu2can generate images, captions, and other multimodal content based on input prompts. - Multimodal understanding: The model exhibits strong performance on multimodal understanding tasks, such as visual question answering and object-grounded generation.
Capabilities
Emu2 demonstrates impressive multimodal in-context learning abilities, allowing it to solve tasks that require on-the-fly reasoning, such as visual prompting and object-grounded generation, even with just a few demonstrations or simple instructions. The model also sets new state-of-the-art results on multiple multimodal understanding benchmarks in few-shot settings.
What can I use it for?
Emu2 can be used as a powerful base model and general-purpose interface for a wide range of multimodal tasks, such as:
- Visual question answering
- Image captioning
- Multimodal dialogue systems
- Multimodal content generation (e.g., generating images and text based on a prompt)
- Downstream multimodal tasks that require in-context learning abilities
Things to try
One interesting aspect of Emu2 is its ability to perform object-grounded generation. This means the model can generate text that is grounded in the visual elements of an input image, demonstrating a deeper understanding of the image content. Experimenting with prompts that involve specific visual elements, such as objects or scenes, could be an engaging way to explore the model's multimodal capabilities.
Another area to explore is the model's few-shot learning abilities. You could try providing the model with only a handful of examples for a particular task and observe how it performs, or experiment with different types of multimodal prompts to see how the model responds.
This summary was produced with help from an AI and may contain inaccuracies - check out the links to read the original source documents!
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