From c73e4e0f26be2cd35575370b78f1ae1e81c0afd4 Mon Sep 17 00:00:00 2001 From: Ying Hu Date: Tue, 20 Aug 2024 15:22:53 +0800 Subject: [PATCH] [doc] Update README.md (#633) fix the sentence for more general hardware --- DocSum/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/DocSum/README.md b/DocSum/README.md index c032a8790..6b1204078 100644 --- a/DocSum/README.md +++ b/DocSum/README.md @@ -1,6 +1,6 @@ # Document Summarization Application -Large Language Models (LLMs) have revolutionized the way we interact with text. These models can be used to create summaries of news articles, research papers, technical documents, legal documents and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference on Intel Xeon and Gaudi2 processors. +Large Language Models (LLMs) have revolutionized the way we interact with text. These models can be used to create summaries of news articles, research papers, technical documents, legal documents and other types of text. Suppose you have a set of documents (PDFs, Notion pages, customer questions, etc.) and you want to summarize the content. In this example use case, we utilize LangChain to implement summarization strategies and facilitate LLM inference using Text Generation Inference. The architecture for document summarization will be illustrated/described below: