Learn how to implement an open-source Large Language Model (LLM) that runs locally on your machine, even in offline mode. Explore Meta's OPT model, a 175-billion-parameter model rivaling GPT-3 in performance, with a focus on its instruction-tuned version, OPT-IML. Discover the process of setting up and utilizing this powerful offline LLM using LlamaIndex. Gain insights into the OPT architecture, its capabilities, and the advantages of instruction-tuned models. Dive into practical implementation steps, code examples, and best practices for leveraging this technology in your projects. Understand the implications of locally-hosted LLMs for privacy, customization, and offline accessibility. This video is part of a comprehensive series on LangChain and LLMs, offering valuable resources and references for further exploration in the field of natural language processing and AI.
Locally-Hosted Offline LLM with LlamaIndex and OPT - Implementing Open-Source Instruction-Tuned Language Models
Samuel Chan via YouTube
Overview
Syllabus
Locally-hosted, offline LLM w/LlamaIndex + OPT (open source, instruction-tuning LLM)
Taught by
Samuel Chan
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