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Fuyu-8B is a multi-modal text and image transformer model developed by Adept AI. It has a simple architecture compared to other multi-modal models, with a decoder-only transformer that linearly projects image patches into the first layer, bypassing the embedding lookup. This allows the model to handle arbitrary image resolutions without the need for separate high and low-resolution training stages. The model is optimized for digital agents, supporting tasks like answering questions about graphs and diagrams, UI-based questions, and fine-grained localization on screen images. Model inputs and outputs Inputs Text**: The model can consume text inputs. Images**: The model can also consume image inputs of arbitrary size, treating the image tokens like the sequence of text tokens. Outputs Text**: The model generates text outputs in response to the provided text and image inputs. Capabilities The Fuyu-8B model is designed to be a versatile multi-modal AI assistant. It can understand and reason about both text and images, enabling it to perform tasks like visual question answering, image captioning, and multimodal chat. The model's fast inference speed, with responses for large images in under 100 milliseconds, makes it well-suited for real-time applications. What can I use it for? The Fuyu-8B model can be a powerful tool for a variety of applications, such as: Digital Assistants**: The model's multi-modal capabilities and focus on supporting digital agents make it a great fit for building conversational AI assistants that can understand and respond to both text and image inputs. Content Creation**: The model can be used to generate creative text formats like poetry, scripts, and marketing copy, while also incorporating relevant visual elements. Visual Question Answering**: The model can be used to build applications that can answer questions about images, diagrams, and other visual content. Things to try One interesting aspect of the Fuyu-8B model is its ability to handle arbitrary image resolutions. This means you can experiment with feeding the model different image sizes and observe how it responds. You can also try fine-tuning the model on specific datasets or tasks to see how it adapts and improves its performance.

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Updated 5/28/2024