A Potential Revolution in AI – Introducing DLMs
A significant advancement has emerged in the field of large language models, promising enhanced speed and reduced costs by employing a novel technique inspired by text-to-image generation models. This innovation, known as diffusion large language models (DLMs), marks a departure from traditional autoregressive models. Check out Inception Labs: https://www.inceptionlabs.ai/news
How Diffusion Language Models Work
Traditional large language models generate text sequentially, producing one token at a time, which requires each token to be completed before the next can be generated. Diffusion language models, however, generate the entire response simultaneously in a coarse manner, and then iteratively refine it. This approach mirrors diffusion text-to-image generation models, which begin with a noisy image and progressively refine it into a coherent image. Inception Labs has developed the first production-grade diffusion-based large language model.
- Speed and Efficiency: Diffusion models can be significantly faster and more cost-effective. For example, Mercury coder mini is roughly equal to DC coder V2 light and other small models.
- Iterative Refinement: The model starts with an almost nonsensical set of text and refines it over iterations.
- Hardware: These models do not require custom hardware. For example, Mercury runs at over 1,000 tokens per second on an Nvidia H100.
Advantages of Diffusion Models
Diffusion models offer several advantages over traditional autoregressive models:
- Reasoning and Error Correction: Diffusion models are not restricted to only considering previous outputs, which makes them better at reasoning and structuring their responses. Because they generate the whole thing at once, they can correct mistakes and hallucinations through iterative refinement.
- Controllable Generation: DLMs can edit their output and generate tokens in any order, allowing users to infill text, align outputs with objectives like safety, or produce outputs that reliably conform to user-specified formats.
Potential Applications
The architecture, speed, and size of diffusion models have significant implications for various applications:
- Agents: The speed of the model is often the only limit, diffusion models allow agents to work faster and achieve higher quality results.
- Advanced Reasoning: With faster inference, these models can perform more computation at test time, leading to better performance.
- Edge Applications: The smaller footprint of these models makes them suitable for running on laptops or mobile devices.