mailitics

Category: inference

  • Optimizing PyTorch Model Inference on AWS Graviton

    Optimizing PyTorch Model Inference on AWS Graviton Tips for accelerating AI/ML on CPU — Part 2 The post Optimizing PyTorch Model Inference on AWS Graviton appeared first on Towards Data Science. Chaim Rand Go to original source

  • Optimizing PyTorch Model Inference on CPU

    Optimizing PyTorch Model Inference on CPU Flyin’ Like a Lion on Intel Xeon The post Optimizing PyTorch Model Inference on CPU appeared first on Towards Data Science. Chaim Rand Go to original source

  • I Made My AI Model 84% Smaller and It Got Better, Not Worse

    I Made My AI Model 84% Smaller and It Got Better, Not Worse The counterintuitive approach to AI optimization that’s changing how we deploy models The post I Made My AI Model 84% Smaller and It Got Better, Not Worse appeared first on Towards Data Science. Arjun Kaarat Go to original source

  • Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning

    Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning It’s like grading papers, but your student is an LLM The post Evaluating LLMs for Inference, or Lessons from Teaching for Machine Learning appeared first on Towards Data Science. Stephanie Kirmer Go to original source

  • The Case for Centralized AI Model Inference Serving

    The Case for Centralized AI Model Inference Serving As AI models continue to increase in scope and accuracy, even tasks once dominated by traditional algorithms are gradually being replaced by Deep Learning models. Algorithmic pipelines — workflows that take an input, process it through a series of algorithms, and produce an output — increasingly rely…

  • Mastering the Poisson Distribution: Intuition and Foundations

    Mastering the Poisson Distribution: Intuition and Foundations You’ve probably used the normal distribution one or two times too many. We all have — It’s a true workhorse. But sometimes, we run into problems. For instance, when predicting or forecasting values, simulating data given a particular data-generating process, or when we try to visualise model output…

  • How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference

    How LLMs Work: Pre-Training to Post-Training, Neural Networks, Hallucinations, and Inference With the recent explosion of interest in large language models (LLMs), they often seem almost magical. But let’s demystify them. I wanted to step back and unpack the fundamentals — breaking down how LLMs are built, trained, and fine-tuned to become the AI systems we interact…

  • Combining Large and Small LLMs to Boost Inference Time and Quality

    Combining Large and Small LLMs to Boost Inference Time and Quality Implementing Speculative and Contrastive Decoding Large Language models are comprised of billions of parameters (weights). For each word it generates, the model has to perform computationally expensive calculations across all of these parameters. Large Language models accept a sentence, or sequence of tokens, and…