Google open-sources speedy DiffusionGemma text diffusion model
Google LLC today released DiffusionGemma, a large language model based on an emerging machine learning approach known as text diffusion.
The company says the algorithm can generate text four times faster than traditional LLMs. Furthermore, DiffusionGemma does so using less RAM. The model’s memory efficiency enables it to run on high-end consumer graphics cards that usually struggle to support LLMs.
DiffusionGemma’s text diffusion architecture is derived from a method that AI models use to generate images. The image generation workflow begins with a blurry photo that contains a type of error called Gaussian noise. An AI model removes a small portion of the noise, analyzes the enhanced photo and uses its findings to restore another batch of pixels. It then repeats the process until arriving at a usable image.
When DiffusionGemma receives a prompt, it generates a placeholder response that comprises random words. It then replaces a subset of the random text with words that will form part of its answer to the user’s prompt. DiffusionGemma reviews the edits, generates a few more words and repeats the process until its prompt response is ready.
