If your GPU can run inference, it should be able to fine-tune too. [P]
A new sparse fine-tuning method called USAF enables fine-tuning of MoE models on GPUs that can run inference. The method is open-source and has been tested on an AMD RX 6750 XT.
![If your GPU can run inference, it should be able to fine-tune too. [P]](https://images.weserv.nl/?url=external-preview.redd.it%2FtJiyaDh2kitc1_2PamSep77jZzZKRn0ulQtOKK2KIHk.png%3Fwidth%3D640%26crop%3Dsmart%26auto%3Dwebp%26s%3D162da8eb861430b130052c274775e37372a5a4f1&w=1200&fit=inside&q=72&output=webp&dpr=2&we=1&il=1)
- USAF is a new sparse fine-tuning method for MoE models
- The method enables fine-tuning on GPUs that can run inference
- USAF has been tested on an AMD RX 6750 XT with 12 GB of memory
- The project is open-source under the Apache 2.0 license
The USAF method focuses on training sparse expert weights and the router instead of adapters, allowing for fine-tuning on GPUs with limited memory.
This approach has significant implications for the development and deployment of AI models, as it enables fine-tuning on a wider range of hardware.
The project's open-source nature under the Apache 2.0 license will likely facilitate further research and adoption of the USAF method.
The ability to fine-tune models on GPUs that can run inference opens up new possibilities for AI applications, particularly in areas where computational resources are limited.
Source: If your GPU can run inference, it should be able to fine-tune too. [P]. Read the full piece at the source.
enables fine-tuning on a wider range of hardware
makes AI more accessible and reduces computational costs
opens up new opportunities for AI applications
advances the development and deployment of AI models
- MoE models
- Mixture of Experts models, a type of neural network architecture
- USAF
- a sparse fine-tuning method for MoE models