Large language models are rapidly gaining popularity, but their slow response times often frustrate users, driving them toward less capable alternatives. In this post, we’ll explore the reasons behind these delays and will explore an innovative decoding technique—Assisted Generation— that can dramatically improve performance, cutting latency by up to 10 times on standard hardware! Understanding … Continue reading Assisted Decoding
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Understanding the Quadratic Cost of Autoregressive Models
Background Autoregressive models generate sequences by predicting the next token in the sequence based on previous tokens. In each step of generation, the model processes the sequence up to the current token to predict the next one. Problem Statement The statement in question is:"This autoregressive nature of the Decode stage contributes to longer processing times, … Continue reading Understanding the Quadratic Cost of Autoregressive Models
Unlock the Power of LLaMA 3: Your Ultimate Resource Guide
https://ai.meta.com/blog/meta-llama-3/ Meta developed and released the Meta Llama 3 family of large language models (LLMs). It is the most capable openly available LLM to date. Providers LLaMA 3 models will soon be available on a range of platforms, including AWS, Databricks, Google Cloud, Hugging Face, Kaggle, IBM WatsonX, Microsoft Azure, NVIDIA NIM, and Snowflake, with … Continue reading Unlock the Power of LLaMA 3: Your Ultimate Resource Guide
Unveiling LORA 🚀: Fine-tuning Neural Networks with Low-Rank Adaptation
Introduction 🌐 Hey, folks! Welcome to the exploration of a groundbreaking technique: LORA—Low Rank Adaptation of large language models. LORA emerged from the labs at Microsoft around two years ago, and today, we'll uncover its mysteries. We'll understand its significance, operational mechanics. So, let's dive into the realm of language models and explore the wonders … Continue reading Unveiling LORA 🚀: Fine-tuning Neural Networks with Low-Rank Adaptation
Demystifying Neural Networks: A Deep Dive into Manual Backpropagation
Backpropagation, a fundamental concept in artificial neural networks and machine learning, operates as a supervised learning algorithm for training neural networks. The term itself stems from the process of propagating error information backward through the network. In essence, backpropagation seeks to minimize the gap between a neural network's predicted output and the actual target values. … Continue reading Demystifying Neural Networks: A Deep Dive into Manual Backpropagation
Navigating Backpropagation: A Practical Dive into Andrej Karpathy’s Tutorial 🚀 PART 1 📚
🌟 Introduction: 🌐: 📚 My Learning Adventure: Join me on this exciting journey from confusion to clarity as we navigate the fascinating world of backpropagation with Andrej Karpathy's tutorial! 🌐🚶♂️ 🧠 Why Backpropagation Matters: Imagine backpropagation as the superhero of neural networks, wielding a magical recipe to make computers smarter! 🦸♂️💻✨ 🙏 Thank You, Andrej … Continue reading Navigating Backpropagation: A Practical Dive into Andrej Karpathy’s Tutorial 🚀 PART 1 📚
Building a Simple RNN with PyTorch: A Step-by-Step Guide
Are you ready to dive into the world of Recurrent Neural Networks (RNNs) and explore their potential for sequential data analysis? Look no further! In this blog post, we will walk through an example of building a simple RNN neural network using PyTorch, a popular deep learning framework. Let's start by importing the necessary libraries: … Continue reading Building a Simple RNN with PyTorch: A Step-by-Step Guide
Building a Simple Convolutional Neural Network (CNN) with PyTorch
In the world of deep learning, Convolutional Neural Networks (CNNs) have revolutionized computer vision tasks, including image classification. In this blog post, we will explore how to code a simple CNN using the PyTorch framework. By the end of this tutorial, you'll have a basic understanding of building and training a CNN. Let's start by … Continue reading Building a Simple Convolutional Neural Network (CNN) with PyTorch
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