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Tag: LLMs

  • Matrix Multiplication with PyTorch and CUDA

    Matrix Multiplication with PyTorch and CUDA Matrix Multiplication is a fundamental operation in linear algebra and is crucial in many machine learning algorithms, especially in the layers of neural networks. CUDA significantly accelerates this operation by parallelizing the numerous multiply-accumulate operations involved. Code Example with PyTorch and CUDA import torch # Check if CUDA is Read more

  • CUDA vs. ROCm for LLM Training

    CUDA vs. ROCm CUDA (Compute Unified Device Architecture) and ROCm (Radeon Open Compute) are the two primary software platforms for General-Purpose computing on Graphics Processing Units (GPGPU) used in accelerating computationally intensive tasks, including the training of Large Language Models (LLMs). CUDA is developed by NVIDIA and is designed for their GPUs, while ROCm is Read more

  • Exploring CUDA (Compute Unified Device Architecture)

    Exploring CUDA CUDA is a parallel computing platform and programming model developed by NVIDIA for use with their GPUs. It allows software developers to leverage the massive parallel processing power of NVIDIA GPUs for general-purpose computing tasks, significantly accelerating applications beyond traditional CPU-bound processing. 1. CUDA Architecture: The Hardware Foundation NVIDIA GPUs are designed with Read more

  • Can AMD GPUs Train LLMs?

    Can AMD GPUs Train LLMs? AMD GPUs can be used to train Large Language Models (LLMs). While NVIDIA GPUs, particularly those with CUDA architecture, have historically dominated the LLM training landscape, AMD has been making significant strides in this area with its ROCm (Radeon Open Compute) platform. 1. ROCm Platform ROCm is AMD’s open-source software Read more

  • Vector Embeddings in LLMs: A Detailed Explanation

    Vector Embeddings in LLMs: A Detailed Explanation What are Vector Embeddings? Vector embeddings are numerical representations of data points, such as words, phrases, sentences, or even entire documents. These representations exist as vectors in a high-dimensional space. The key idea behind vector embeddings is to capture the semantic meaning and relationships between these data points, Read more

  • How GPU Architecture revolutionized LLMs

    How GPU Architecture Helped LLMs The development and advancement of Large Language Models (LLMs) have been significantly propelled by the unique architecture of Graphics Processing Units (GPUs). Their parallel processing capabilities, high memory bandwidth, and specialized compute units have made training and deploying these massive models feasible and efficient. 1. Massively Parallel Processing LLMs involve Read more

  • Understanding Transformer Models in LLMs

    Transformer Models in LLMs 1. Core Innovation: Self-Attention The Transformer model’s revolutionary aspect for Large Language Models (LLMs) and Natural Language Processing (NLP) lies in its ability to process sequential data efficiently and understand context effectively. Unlike sequential models like Recurrent Neural Networks (RNNs), Transformers can process entire sequences in parallel. The key to this Read more

  • Salesforce Agentic AI: A Comprehensive Overview

    Salesforce Agentic AI: A Comprehensive Overview Salesforce Agentic AI represents a significant evolution in how artificial intelligence is integrated into the Salesforce platform. Moving beyond simple automation and predictive analytics, Agentic AI aims to create intelligent, autonomous agents capable of understanding complex goals, planning multi-step actions, and executing tasks on behalf of users. This detailed Read more

  • Implementing Graph-Based Retrieval Augmented Generation

    Implementing Graph-Based Retrieval Augmented Generation Implementing Graph-Based Retrieval Augmented Generation This document outlines the implementation of a system that combines the power of Large Language Models (LLMs) with structured knowledge from a graph database to perform advanced question answering. This approach, known as Graph-Based Retrieval Augmented Generation (RAG), allows us to answer complex queries that Read more

  • Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval

    Detailed Implementation of Backend-Only Advanced RAG with Multi-Hop Retrieval This article provides a comprehensive guide to implementing a backend-only Retrieval-Augmented Generation (RAG) system enhanced with Multi-Hop Retrieval capabilities. This advanced technique, leveraging LangChain’s SelfQueryRetriever, OpenAI’s language models and embeddings, and ChromaDB for vector storage, enables more sophisticated question answering over a knowledge base. Understanding Multi-Hop Read more