Reducing the size and increasing the speed of AI models is a key goal in today’s machine learning world. One effective way to do this is through model quantization, which minimizes the model’s memory footprint and boosts inference performance. This process is especially useful for deploying AI on resource-limited devices like gaming PCs or edge
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Machine Learning & Research
Creating a reliable machine learning pipeline can seem complex, but with the right tools, it becomes much simpler. ZenML is a framework that helps you build, track, and manage end-to-end ML workflows. This guide walks through setting up a production-grade pipeline, including custom components, metadata tracking, and hyperparameter tuning. Setting Up the Environment and Initializing
Speed and cost are big hurdles for companies using large language models (LLMs). These models often generate thousands of tokens per query, but current hardware struggles to keep up. Now, researchers have found a way to make these models faster by modifying how they predict tokens, without needing extra draft models or complex decoding methods.
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Researchers from MIT, the Improbable AI Lab, and ETH Zurich have developed a new fine-tuning technique to tackle a common problem in AI called “catastrophic forgetting.” This issue happens when models lose previously learned skills after being updated with new information. Their new method, called self-distillation fine-tuning (SDFT), aims to help models learn new tasks
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Researchers have developed a new fine-tuning technique that helps large language models learn new skills without losing what they already know. This approach aims to fix a common problem called “catastrophic forgetting,” which happens when models forget previous knowledge after updates. The new method is designed to make updating AI systems easier and more cost-effective
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Chinese AI company Deepseek has introduced a new way to train large language models more efficiently and at a lower cost. This innovative approach is called Manifold-Constrained Hyper-Connections (mHC). It builds on earlier technology that was developed to improve how AI models learn and grow. The development could be a big step forward for the
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Many teams working with machine learning (ML) and data face a sneaky problem: annotation mistakes. These errors can slow down progress and make the whole process more expensive. Recent studies show that annotation errors are more common than many realize. For example, Apple’s research found about 10% error rates in search relevance tasks. Even the
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