Friend of the show, Matt Williams, explains how to run local ChatGPT and GitHub Copilot clones using Ollama and Docker's GenAI Stack. Topics include setting up LLM stacks, deploying models, utilizing RAG for customized responses, and integrating Docker for GPU utilization.
Running open source generative AI models locally with Olama and Docker enhances efficiency and data privacy.
Retrieval Augmented Generation (RAG) optimizes model responses by retrieving relevant data for more accurate outcomes.
Deep dives
Overview of LLMs and Olama in Tech Development
Working with Locally Run Language Models (LLMs) like Olama for developing with open source AI models offers insights into using Docker environments effectively. This episode delves into the usefulness of setting up model runs locally for efficient development. Highlighting the simplicity of using Olama to create and run models, it emphasizes the benefits of local setups for tools like Chat GPT clones.
Evolution of Olama and Its Purpose
Olama originated from the need to run AI models locally for enhanced usability and privacy. Initially designed as a Python framework, Olama transitioned to being built in Go for simpler installations. Olama's structure allows for easy model retrieval locally, promoting faster and more succinct responses compared to cloud-based counterparts, addressing concerns about data privacy and efficiency.
Retrieval augmented generation (RAG) serves as a strategy to enhance responses from models by retrieving relevant data from sources like Stack Overflow. RAG entails breaking down text inputs into manageable chunks, converting them to numerical embeddings for computational analysis. This method aids in tailoring responses to specific queries, guiding towards more accurate and informed model outcomes.
Utilizing Olama for Custom API Deployments
Once comfortable with an LLM model, Olama simplifies deploying it as an API endpoint for broader access within applications. By sending formatted requests to the local host, users can interface with models efficiently. Olama's compatibility with OpenAI API standards offers versatility in deployment, supporting diverse programming environments and integrations.
Bret and Nirmal are joined by friend of the show, Matt Williams, to learn how to run your own local ChatGPT clone and GitHub Copilot clone with Ollama and Docker's "GenAI Stack," to build apps on top of open source LLMs.
We've designed this conversation for tech people like myself, who are no strangers to using LLMs in web products like chat GPT, but are curious about running open source generative AI models locally and how they might set up their Docker environment to develop things on top of these open source LLMs.
Matt Williams is walking us through all the parts of this solution, and with detailed explanations, shows us how Ollama can make it easier on Mac, Windows, and Linux to set up LLM stacks.