🐙 Lunch & Learn: Intro to Open Source Models

The speaker begins by apologizing for starting late due to heavy rain in South Carolina. They acknowledge the inconvenience of the early timing, especially for those joining from different time zones. The speaker mentions an upcoming flight to Austin and greets viewers by name, addressing the early hour on a Saturday. There is a brief discussion about sound levels and the limitations of the audio setup, with some viewers expressing satisfaction while others suggest improvements. The speaker expresses excitement about a potential appearance on Joe Rogan's podcast and shares thoughts on coding co-pilots, indicating their personal belief in their helpfulness. The conversation transitions to the main topic, which is an introduction to open-source large language models, with an invitation for viewers to share their experience with such models.

The speaker provides an overview of open source and closed AI models, using OpenAI as an example of a closed-source model. They explain that while OpenAI's models like GPT-3.5 and GPT-4 are not directly accessible, users can interact with them through APIs provided by OpenAI. The speaker contrasts this with open source models, which allow more freedom and transparency, but may require more technical knowledge to use effectively. They also touch on privacy concerns related to closed-source models, the benefits of hosting models locally, and the cost differences between free open source models and paid closed source ones. The speaker then introduces the topic of specialized open source models, such as those designed for legal or financial analysis.

The speaker guides the audience through exploring open source models available on Hugging Face, a platform hosting a vast array of models for various tasks. They discuss the overwhelming number of models and the different categories they fall into, such as natural language processing, audio, and reinforcement learning. The speaker also mentions the availability of models for different languages and tasks, and the ability to download, train, and deploy these models directly. However, they suggest using additional software like Ama or LM Studio for a better user experience and easier management of these models.

The speaker demonstrates how to use LM Studio and AMA, two software platforms that facilitate the use of open source models. They explain that LM Studio offers a user-friendly interface for downloading and interacting with models, while AMA is more suited for advanced users who are comfortable with command-line interfaces. The speaker shows the process of downloading models using both platforms and highlights the features of LM Studio, such as its chat interface and model management capabilities. They also discuss the importance of considering hardware requirements when running these models locally.

The speaker provides a tutorial on customizing and running models using AMA. They explain how to use the command-line interface to pull models from a repository, customize them with specific parameters like temperature to adjust creativity, and create model files with system prompts to guide the model's behavior. The speaker attempts to demonstrate these steps but encounters technical difficulties, suggesting that the process is straightforward but may require some familiarity with coding and command-line operations.

The speaker shares a personal project where they aim to analyze their steps data from an Apple Health app using an open source model. They describe creating a model named 'analyzer' that is designed to identify trends and patterns in daily step counts. The speaker outlines the process of setting up a system prompt for the model and adjusting parameters to tailor the model's responses. They also discuss the potential benefits of using open source models for personal data analysis and the ability to customize models to specific tasks.

The speaker continues the project of creating a custom model for analyzing steps data. They detail the process of writing a system message to guide the model, setting parameters like temperature, and incorporating the steps data into the model. The speaker attempts to demonstrate how to run the model with the new configuration but faces technical issues. Despite the challenges, they manage to create a new model named 'steps-analyzer-llama-2' and show the initial steps to interact with it.

The speaker reviews the analysis of their steps data performed by the custom model they created. They discuss the model's output, which includes identifying the highest and lowest steps counts and providing suggestions for increasing steps on days with lower activity. The speaker acknowledges inaccuracies in the model's analysis and apologizes for not being able to demonstrate all planned features due to time constraints and technical difficulties. They ask for feedback from the audience regarding the demonstration and whether they would like a follow-up session to cover more details.