Optimizing and Serving Large AI Models with Elixir and Nx

The author, Toran Billups, outlines the challenging task of fine-tuning the massive Mistral 7B AI model, which typically requires over 100GB of vRAM, with just a 24GB RTX 4090 GPU. Leveraging a Python project called lit-gpt, Billups achieves local fine-tuning, allowing for rapid iteration and better data privacy. He provides a step-by-step guide that includes cloning the required GitHub repository, preparing the dataset using a custom JSON format, and executing fine-tuning scripts without full precision to accommodate the GPU's memory constraints. Post fine-tuning, the author merges the model weights and prepares the model for evaluation. Billups then transitions into leveraging Elixir's Nx library for serving the model, showing how to load the fine-tuned model, its tokenizer, and configuring the generation of predictions. Integrating Nx.Serving into an Elixir application, Billups showcases how to prompt the model and utilize its output—a MathJSON expression. The blog also acknowledges contributions from Jon Durbin and Sean Moriarity, who provided inspiration, resources, and implementation support, enhancing the Elixir ecosystem for large AI models.