LangChain + Streamlit Llama : Bringing Conversational AI to Your Local Machine

In the past few months, Large Language Models (LLMs) have gained significant attention, capturing the interest of developers across the planet. These models have created exciting prospects, especially for developers working on chatbots, personal assistants, and content creation. The possibilities that LLMs bring to the table have sparked a wave of enthusiasm in the Developer | AI | NLP community. <h1>What are LLMs?</h1> Large Language Models (LLMs) refer to machine learning models capable of producing text that closely resembles human language and comprehending prompts in a natural manner. These models undergo training using extensive datasets comprising books, articles, websites, and other sources. By analyzing statistical patterns within the data, LLMs predict the most probable words or phrases that should follow a given input. <img alt="" src="https://miro.medium.com/v2/resize:fit:700/1*RduQ4TS4F3JAmKBQ8307BQ.png" style="height:354px; width:700px" /> A timeline of LLMs in recent years: <a href="https://arxiv.org/abs/2303.18223" rel="noopener ugc nofollow" target="_blank">A Survey of Large Language Models</a> By utilizing Large Language Models (LLMs), we can incorporate domain-specific data to address inquiries effectively. This becomes especially advantageous when dealing with information that was not accessible to the model during its initial training, such as a company’s internal documentation or knowledge repository. <a href="https://ai.plainenglish.io/%EF%B8%8F-langchain-streamlit-llama-bringing-conversational-ai-to-your-local-machine-a1736252b172">Read More</a>