fuyu 8b EU AI Act Compliance Profile

Fuyu-8B Model Card

We’re releasing Fuyu-8B, a small version of the multimodal model that powers our product. The model is available on HuggingFace. We think Fuyu-8B is exciting because:

1. It has a much simpler architecture and training procedure than other multi-modal models, which makes it easier to understand, scale, and deploy.
2. It’s designed from the ground up for digital agents, so it can support arbitrary image resolutions, answer questions about graphs and diagrams, answer UI-based questions, and do fine-grained localization on screen images.
3. It’s fast - we can get responses for large images in less than 100 milliseconds.
4. Despite being optimized for our use-case, it performs well at standard image understanding benchmarks such as visual question-answering and natural-image-captioning.

Please note that the model we have released is a base model. We expect you to need to finetune the model for specific use cases like verbose captioning or multimodal chat. In our experience, the model responds well to few-shotting and fine-tuning for a variety of use-cases.

Model

Fuyu-8B is a multi-modal text and image transformer trained by Adept AI.

Architecturally, Fuyu is a vanilla decoder-only transformer - there is no image encoder. Image patches are instead linearly projected into the first layer of the transformer, bypassing the embedding lookup. We simply treat the transformer decoder like an image transformer (albeit with no pooling and causal attention). See the below diagram for more details.

This simplification allows us to support arbitrary image resolutions. To accomplish this, we treat the sequence of image tokens like the sequence of text tokens. We remove image-specific position embeddings and feed in as many image tokens as necessary in raster-scan order. To tell the model when a line has broken, we simply use a special image-newline c

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