Retrieval Augmented Generation
In this video, we’ll learn how to use Retrieval Augmented Generation with Chroma and LangChain to provide an OpenAI/GPT LLM prompt with more data to effectively answer our questions about the Wimbledon 2023 tennis tournament. |
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Consistent JSON with OpenAI/GPT
In this video, we’ll learn how to return a consistent/predictable/valid JSON response to a sentiment analysis prompt using OpenAI. |
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Running Mixtral with Ollama
In this video, we’ll learn about Mixtral, the latest large language model from Mistral AI. Mixtral employs a mixture of experts approach, with eight models and a router to manage queries, enhancing the AI’s response quality. We’re going to run Mixtral on our own machine using the awesome Ollama tool. We’ll then compare Mixtral with the original Mixtral model on a variety of tasks including sentiment analysis, summarisation, suggesting prompts to review books, and updating Python code. |
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Constraining LLMs with Guidance AI
In this video, we’ll learn how to use the Guidance library to control and constrain text generation by large language models, specifically integrating it with the llama CPP library and the Mistral 7B model. We’ll build an emotion detector with help from functions like select which restricts generation to an array of values and gen, which can be controlled by regular expressions. We’ll also learn how to create reusable components and output results in JSON format. |
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LLaVA: A large multi-modal language model
In this video, we’ll learn about LAVA (Large Language And Vision Assistant), a multimodal model that integrates a CLIP vision encoder and the VICUNA LLM. We’ll see how well it gets on describing a cartoon cat, a photo of me with AI generated parrots, and a bunch of images created by the Mid Journey Generative AI tool. And most importantly, we’ll find out whether it knows who Cristiano Ronaldo is! |
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