Running Llama 3, Mixtral, and GPT-4o

This blog post discusses various ways to run the G-Generation part of Retrieval Augmented Generation (RAG) using different models and inference endpoints. The author provides step-by-step instructions on how to use Llama 3 from Meta, Mixtral from Mistral, and the newly announced GPT-4o from OpenAI. They also cover running these models locally or through Anyscale, OctoAI, and Groq endpoints. Additionally, the author explains how to evaluate answers using Ragas and provides a summary table of results for each model endpoint. The conclusion emphasizes the importance of considering answer quality, latencies, and costs when choosing an appropriate model and inference endpoint for the G-Generation part of RAG.

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