This podcast episode explores the cutting edge of large language models (LLMs) by examining several recently released research papers. The discussion will cover a range of topics, including model architectures like Mixtral's Mixture of Experts approach and Apple's on-device and server-based models. We'll delve into training datasets such as SlimPajama and FineWeb and the critical techniques used to filter and deduplicate them. The podcast will also investigate parameter-efficient fine-tuning (PEFT) methods such as DoRA, which improves upon LoRA, as well as methods for aligning models using techniques like PPO and DPO. Finally, we'll touch on the responsible AI principlesand the safety taxonomies used to evaluate models for harmful content, along with the various metrics and benchmarks used to measure model performance and biases.
