Building Small Language Models Out of Large Language Models course

In this course,
you'll learn

Build LLMs for the Developing World

Create efficient language models that can run on low-power devices, bringing the benefits of AI to underserved communities and emerging markets.

Power Resource-Constrained Devices

Deploy LLMs on embedded systems, IoT devices, and other platforms with limited resources, bringing AI to the edge of the network.

Reduce Development Costs

Build and deploy LLMs with significantly lower computational and memory requirements, making AI development more accessible and affordable.

Disrupt Industries with Efficient AI

Leverage your knowledge of model compression to create innovative AI solutions that are both powerful and practical, giving you a competitive edge in the rapidly growing AI market.

Your instructor

Vahe Aslanyan

Vahe Aslanyan has empowered hundreds of developers by clarifying complex AI and data science concepts, helping them innovate cutting-edge software solutions.

Trusted by over 10.000 students

Course program

Here's a glimpse of what you'll learn throughout the course

Module 1

Module 1: Introduction to LLMs and the Need for Compression – Bridging the Gap Between Power and Efficiency

Explore the capabilities and limitations of Large Language Models (LLMs), understanding why model compression is crucial for deploying them in real-world applications and unlocking their full potential.

Module 2

Module 2: Mastering the Art of Structured Pruning – Sculpting LLMs for Efficiency

Dive deep into structured pruning techniques, learning how to surgically remove unnecessary components from LLMs while preserving their core functionality and achieving significant size reductions.

Module 3

Module 3: Harnessing the Power of Knowledge Distillation – Transferring Wisdom to Smaller Models

Uncover the secrets of knowledge distillation, learning how to transfer the knowledge of a large, powerful LLM to a smaller, more efficient model without sacrificing significant performance.

Module 4

Module 4: Combining Pruning and Distillation – The Art of Creating Compact LLMs

Master the art of combining pruning and distillation techniques to create remarkably compact LLMs that retain impressive performance, exploring iterative approaches and best practices for optimal results.

Module 5

Module 5: Evaluating and Benchmarking Compressed LLMs – Measuring Success and Comparing Performance

Learn how to evaluate the performance of your compressed LLMs using a range of metrics, benchmarking them against other models, and analyzing the trade-offs between compression, accuracy, and efficiency.

Module 6

Module 6: Deploying Compressed LLMs in Real-World Applications – From Theory to Practice

Explore deployment strategies for deploying your compressed LLMs on different platforms and devices, optimizing them for specific hardware architectures and unlocking their potential in a wide range of real-world applications.

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Why choose us

Focus on Your Success

LunarTech is invested in your goals, with personalized learning plans and support structures to help you overcome challenges.

Industry-Aligned Curriculum

Our courses are constantly updated to reflect the latest tools, techniques, and in-demand skills employers are seeking.

Hands-On Practice

Study on your schedule. SkillSprint is accessible 24/7, so you can learn at your pace, anytime, anywhere.

Flexible Learning

Study at your own pace, from anywhere and at any time, on your terms.

Certificate of Completion

Receive a SkillSprint certificate to showcase your new skills.

Lifetime Alumni Network

Even after graduation, stay connected to the LunarTech community for ongoing support, job opportunities, and industry insights.

Frequently asked questions

Need help? Check out our FAQ section for quick and easy solutions.

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Let’s do the math together:

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Building Small Language Models Out of Large Language Models