Small Language Models and Model Distillation

~60 min · 15 stations

Small Language Models and Model Distillation is a self-paced learning path in Computer Science & AI, free to read, written at General Public / 9th Grade reading level. Across 15 structured stations, you will work through the core ideas step by step, each with a short quiz to check your understanding. By the end you will be able to define the architecture of large language models; identify limitations of massive model deployment; describe the process of knowledge transfer.

Conductor

The Conductor

All aboard the efficiency express! We are shrinking the giants of AI down to size so they can run on your smallest devices.

What you will learn

Complete each station to unlock the next.

FOUNDATION

Establishes the core vocabulary and essential context you need before going further.

• Define the architecture of large language models

Station 01: Understanding Large Language Models▶

• Identify limitations of massive model deployment

Station 02: The Need for Small Models▶

• Describe the process of knowledge transfer

Station 03: Defining Model Distillation▶

CORE CONCEPTS

Unpacks the ideas and principles that the subject is built on.

• Analyze the function of teacher models

Station 04: Teacher Model Roles▶

• Examine student model design requirements

Station 05: Student Model Architecture▶

• Evaluate hardware limitations for AI models

Station 06: Hardware Constraints▶

• Calculate loss during model training

Station 07: Loss Functions in Distillation▶

MECHANICS

Examines how things actually work — the processes, rules, and systems in action.

• Execute layer-based knowledge mapping

Station 08: Layer-wise Knowledge Transfer▶

• Apply attention maps in training

Station 09: Attention-based Distillation▶

• Design data sets for distillation

Station 10: Data Augmentation Strategies▶

APPLICATION

Puts knowledge to use through real-world scenarios and practical problems.

• Implement weight quantization for models

Station 11: Quantization Techniques▶

• Apply pruning to neural networks

Station 12: Model Pruning Methods▶

• Optimize models for edge hardware

Station 13: Deployment on Edge Devices▶

SYNTHESIS

Connects everything together and explores broader implications and open questions.

• Assess model accuracy versus size

Station 14: Evaluating Model Performance▶

• Predict trends in model efficiency

Station 15: Future of Small AI▶

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General Public / 9th GradeAI Generated · gemini-3.1-flash-lite