Skip to main content

What's the difference between Foundation Model and Large Language Models

 

AspectFoundation ModelsLarge Language Models (LLMs)
PurposeServe as a starting point for specialized modelsAre powerful models designed for a wide range of NLP tasks
Pre-trainingTypically pre-trained on large text corporaAlso pre-trained on extensive text data
Parameter CountCan have a moderate number of parametersCharacterized by a large number of parameters (tens of millions to billions)
Fine-tuningFine-tuned for specific tasks or domainsCan be fine-tuned for specialized tasks
AdaptabilityUsed as a foundation for domain-specific modelsVersatile and adaptable to various NLP tasks
ExamplesHugging Face Transformers, TensorFlow Serving, PyTorch ServingOpenAI's GPT-3, BERT by Google, RoBERTa, T5, etc.
PerformanceMay not achieve state-of-the-art performanceOften achieves state-of-the-art performance in NLP benchmarks
CustomizationCustomized for specific applicationsServe as powerful out-of-the-box models
Use CasesProvide building blocks for NLP applicationsUsed for a wide range of NLP tasks, including chatbots, translation, summarization, question-answering, and more
Resource RequirementsCan be less resource-intensiveTends to be more resource-intensive, especially larger LLMs
ComplexityGenerally less complex than LLMsCharacterized by complexity due to the large parameter count
ScalabilityEasier to scale for specific applicationsCan be scaled for diverse NLP tasks but may require more resources
Research and DevelopmentOften used as research toolsUsed in both research and production environments
ExamplesHugging Face Transformers, TensorFlow Serving, PyTorch ServingGPT-3, BERT, RoBERTa, T5, and many more
Labels:

Comments

Post a Comment

Popular posts from this blog

Error: could not find function "read.xlsx" while reading .xlsx file in R

Got this during the execution of following command in R > dat Error: could not find function "read.xlsx" Tried following command > install.packages("xlsx", dependencies = TRUE) Installing package into ‘C:/Users/amajumde/Documents/R/win-library/3.2’ (as ‘lib’ is unspecified) also installing the dependencies ‘rJava’, ‘xlsxjars’ trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/rJava_0.9-8.zip' Content type 'application/zip' length 766972 bytes (748 KB) downloaded 748 KB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsxjars_0.6.1.zip' Content type 'application/zip' length 9485170 bytes (9.0 MB) downloaded 9.0 MB trying URL 'https://cran.rstudio.com/bin/windows/contrib/3.2/xlsx_0.5.7.zip' Content type 'application/zip' length 400968 bytes (391 KB) downloaded 391 KB package ‘rJava’ successfully unpacked and MD5 sums checked package ‘xlsxjars’ successfully unpacked ...
Post a Comment
Read more

What is the benefit of using Quantization in LLM

Quantization is a technique used in LLMs (Large Language Models) to reduce the memory requirements for storing and training the model parameters. It involves reducing the precision of the model weights from 32-bit floating-point numbers (FP32) to lower precision formats, such as 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Bottomline: You can use Quantization to reduce the memory footprint off the model during the training. The usage of quantization in LLMs offers several benefits: Memory Reduction: By reducing the precision of the model weights, quantization significantly reduces the memory footprint required to store the parameters. This is particularly important for LLMs, which can have billions or even trillions of parameters. Quantization allows these models to fit within the memory constraints of GPUs or other hardware accelerators. Training Efficiency: Quantization can also improve the training efficiency of LLMs. Lower precision formats require fewer computati...
Post a Comment
Read more

What is Tensor Parallelism and relationship between Buffer and GPU

  Tensor Parallelism in GPU Tensor parallelism is a technique used to distribute the computation of large tensor operations across multiple GPUs or multiple cores within a GPU .   It is an essential method for improving the performance and scalability of deep learning models, particularly when dealing with very large models that cannot fit into the memory of a single GPU. Key Concepts Tensor Operations : Tensors are multidimensional arrays used extensively in deep learning. Common tensor operations include matrix multiplication, convolution, and element-wise operations. Parallelism : Parallelism involves dividing a task into smaller sub-tasks that can be executed simultaneously. This approach leverages the parallel processing capabilities of GPUs to speed up computations. How Tensor Parallelism Works Splitting Tensors : The core idea of tensor parallelism is to split large tensors into smaller chunks that can be processed in parallel. Each chunk is assigned to a different GP...
Post a Comment
Read more