Welcome to The Sequence Knowledge( formerly Edge). As mentioned in our Sunday series, we are starting 2025 with a very exciting editorial calendar with 6 editions.

1. The Sequence Knowledge: Continuing with educational topics and related research. We’re kicking off an exciting series on RAG and have others lined up on evaluations, decentralized AI, code generation, and more.

2. The Sequence Engineering: A standalone edition dedicated to engineering topics such as frameworks, platforms, and case studies. I’ve started three AI companies in the last 18 months so have a lot of opinions about engineering topics.

3. The Sequence Chat: Our interview series featuring researchers and practitioners in the AI space.

4. The Sequence Research: Covering current research papers.

5. The Sequence Insights: Weekly essays on deep technical or philosophical topics related to AI.

6. The Sequence Radar: Our Sunday edition covering news, startups, and other relevant topics.

It is ambitious but certainly fun so please subscribe before prices increase :)

Throughout the last few weeks, we have explored the core concepts and more important techniques related to knowledge distillation. Today, we are concluding the series with a summary of the contents we have covered and leave you with a final essay exploring the pros and cons of this technique.

How does distillation work exactly?

Conceptually, distillation is the process of transferring knowledge from a larger complex model to a more efficient model. The larger model is often referred to as the teacher while the smaller model is known as the student. The core idea is for the student model to mimic the behavior of the teacher model for a specific task.

In this series, we explored the fundamentals of knowledge distillations as well as its most important variations:

1. TS Knowledge 445: Introduced the series and reviewed of one of the first papers about knowledge distillation. Google’s Data Commons framework to ground LLMs on factual knowledge.

2. TS Knowledge 447: Provides an overview the different types of model distillation. Discussed the original model distillation paper from Google Research and the Haystack framework for RAG applications.

3. TS Knowledge 449: Dives into adversarial distillation. Reviews the introspective adversarial distillation paper from Alibaba and LMQL framework.

4. TS Knowledge 451: Explores the concepts behind multi-teacher distillation. Discusses the research behind MT-BERT, one of the first distillation methods for foundation models. It also covers the Potkey framework for LLM guardrailing.

5. TS Knowledge 453: Covers the principles of cross modal distillation including UC Berkeley’s paper about cross modal distillation for supervision transfer. It also discusses HuggingFaces’s Gradio framework for building web-AI apps.

6. TS Knowledge 455: Dives into the ideas behind graph-based distillation and discusses a detailed survey about the most interesting methods in this area. It also provides an overview of HuggingFace’s Autrain framework for training foundation models.

7. TS Knowledge 457: Provides an overview of attention-based distillation methods. It covers the paper that outlined the Attention and Feature Transfer-based Knowledge Distillation (AFT-KD) technique. The tech section focuses on Microsoft’s famous OmniParser framework for vision-based agents.

8. TS Knowledge 459: Explores the ideas behind quantized distillation. It includes a review or the Model Compression via Quantized Distillation from ETH Zurich and DeepMind. It also explore IBM Granite 3.0 platform for enterprise generative AI.

9. TS Knowledge 461: Discusses the challenges of knowledge distillation. Reviews Meta AI’s famous System 2 distillation paper. It also provides an overview of Meta’s famous Llama Stack framework for building generative AI applications.

I hope this series have helped you better understand the principles and techniques of knowedledge distillation. If you are considering using distillation in an AI scenario, it is essential to underatand its benefits and drawbacks. And that’s the subject of our finaal mini-essay of this series.

A Practical View Into The Benefits and Challenges of Knowledge Distillation

Knowledge distillation (KD) has emerged as a powerful technique in the field of machine learning, particularly in the era of large language models (LLMs) and deep neural networks (DNNs). This essay will explore the advantages and disadvantages of knowledge distillation, delving into state-of-the-art research and methods. The discussion will be tailored for a highly technical audience, focusing on the intricacies of various KD approaches and their implications.

Advantages of Knowledge Distillation...