💡 ML Concept of the Day: A Summary of Our Series About Fine-Tuning in Foundation Models

Throughout the last few weeks, we have been exploring the emerging ecosystem of fine-tuning methods for foundation models. Fine-tuning is one of the most important capabilities in the lifecycle of foundation models required to build more specialized models for different domains. From a technical perspective, fine-tuning materializes as an adjustment in the model weights of a pretrained foundation model to adjust to a specific task. For instance, we can fine-tune a pretrained large language model(LLM) using a medical library in order to distill a model that can answer questions about specific medical conditions. We might think that fine-tuning is almost a most in foundation model solutions but that’s not the case.

Our series covered a broad spectrum of fine-tuning techniques from the very early iterations of this concept to the evolution of popular techniques such as LoRA to instruction-following tuning methods such as reinforcement learning with human feedback.

For our next series, we will start exploring one of the most cutting edge and shockingly fascinating areas in foundation models( read until the end ;) ). But that will have to wait until next week. For now, here is a recap of our series about fine-tuning methods.

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• Edge 327: The start of our series presents an introduction to fine-tuning, Meta AI’s LIMA method and H2O’s LLM Studio.

• Edge 329: Explores the different types of fine-tuning methods, MIT’s multitask prompt tuning research and Lamini’s fine-tuning platform.

• Edge 331: Introduces the concept of Universal Language Fine-Tuning, Google Research’s symbol tuning technique and Scale’s LLM Engine.

• Edge 333: Reviews the popular parameter efficient fine-tuning(PEFT) technique including its original paper. It also dives into Ray Train as a highly scalable fine-tuning runtime.

• Edge 335: Discusses the super popular LoRA and the universe of low-rank adaptation methods. It reviews LoRA’s original paper and the LoRA for Diffusers stack.

• Edge 337: Dives into quantized LoRA(QLoRA), reviews the original QLoRA paper and Azure Open AI Service’s fine-tuning toolbox.

• Edge 339: Explores the concept of prefix-tuning, reviews Microsoft Research’s prefix-tuning paper and Hugging Face’s PEFT library.

• Edge 341: Introduces the concept of prompt-tuning, reviews Google Research’s prompt-tuning paper and the Axolotl fine-tuning framework.

• Edge 343: Reviews the LlaMA Adapter method including its original paper and also reviews the Chatbot Arena framework.

• Edge 345: Dives into the popular and often misunderstood reinforcement learning with human feedback(RLHF) including a review of the original RLHF paper and the transformer reinforcement learning RLHF stack.

• Edge 347: Provides an overview of Anthropic’s Constitutional AI method. It provides a summary of the original Constitutional AI paper and a review of the Humanloop platform.

• Edge 349: The series conclusion features a review of the reinforcement learning with AI feedback(RLAIF) method. A walkthrough the original RLAIF paper and an exploration of NVIDIA’s NeMo framework.

I hope you enjoyed this series. We tried to cover most of the main fine-tuning methods that are applied in foundation model applications. At the pace this is moving, we might have to do an update relatively soon. Next week we will be starting a new series about one of the hottest and most fascinating trends in foundation models: Reasoning!

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