The Llama Impact Grants program offers $500,000 in grants to projects using Llama 2, an open-source large language model, to address significant challenges in education, the environment, and social innovation.
Mixture-of-experts (MoE) architectures use sparse activation to initial the scaling of model sizes while preserving high training and inference efficiency. However, training the router network creates the challenge of optimizing a non-differentiable, discrete objective despite the efficient scaling by MoE models. Recently, an MoE architecture called SMEAR was introduced, which is fully non-differentiable and merges experts gently in the parameter space. SMEAR is very efficient, but its effectiveness is limited to small-scale fine-tuning experiments on downstream classification tasks.
Hugging Face has recently introduced LeRobot, a machine learning (ML) model created especially for practical robotics use. LeRobot provides an adaptable platform with an extensive library for advanced model training, data visualization, and sharing. This release represents a major advancement in the goal of increasing robots’ usability and accessibility for a broad spectrum of users.
Computer vision is a rapidly growing field of artificial intelligence that enables computers to understand and interpret images and videos. It has a wide range of applications, including self-driving cars, medical imaging, and robotics.
