Future Development Plans for Nx, Axon, and Bumblebee in Elixir

Sean Moriarity provides an overview of the recent developments in the field of machine learning involving Nx, Axon, and Bumblebee within the Elixir ecosystem. He emphasizes the impact of quantization in reducing the memory footprint of large language models (LLMs) making them more feasible to run on consumer hardware, and the introduction of the Axon.Quantization module which facilitates 8-bit integer quantization. Moriarity also discusses the advancements in Low-Rank Adaptation (LoRA) which allows for efficient fine-tuning of LLMs by adding small task-specific layers without modifying the original weights. Another significant point raised is the future possibility of model sharding to run LLMs across multiple devices and the upcoming cross-compatibility enhancements facilitated by MLIR and IREE technology. The article ends with an invitation for the community to contribute to the ongoing advancements by adding new Bumblebee architectures or creating educational content.