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Category: LLMs

  • Top 10 LLMs on Hugging Face & Use Cases

    Top 10 LLMs on Hugging Face & Use Cases Top 10 Popular LLMs on Hugging Face & Their Use Cases Please note that “top” can be subjective and based on various factors like downloads, recent interest, and performance on specific benchmarks. This list reflects a mix of widely used and influential models as of early Read more

  • Pretrained Models for Document Extraction

    Pretrained Models for Document Extraction Cloud-Based Pretrained Models Google Cloud Document AI: Offers pretrained models for various document types (invoices, receipts, IDs, etc.) for key-value pair, table extraction, and classification. AWS Textract: Provides pretrained models for OCR, key-value pair extraction, and table extraction from documents and images. Azure Form Recognizer (now Document Intelligence): Offers pretrained Read more

  • Tensor Reshaping with PyTorch and CUDA

    Tensor Reshaping with PyTorch and CUDA Tensor Reshaping involves changing the shape of a tensor without altering its underlying data. This operation is frequently used to prepare tensors for different operations in neural networks and other numerical computations. While the reshaping operation itself is typically not computationally intensive, performing it on a GPU using CUDA Read more

  • 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

  • Tensor Multiplication (Element-wise) with PyTorch and CUDA

    Tensor Multiplication (Element-wise) with PyTorch and CUDA Element-wise Tensor Multiplication, also known as Hadamard product, involves multiplying corresponding elements of two tensors that have the same shape. Utilizing CUDA on a GPU significantly accelerates this operation through parallel processing. Code Example with PyTorch and CUDA import torch # Check if CUDA is available and set Read more

  • Tensor Addition with PyTorch and CUDA

    Tensor Addition with PyTorch and CUDA Tensor Addition is a fundamental operation in tensor algebra. It involves adding corresponding elements of two tensors that have the same shape, resulting in a new tensor of the same shape where each element is the sum of the corresponding elements of the input tensors. When performed on a 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

  • 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