Generative AI models, like large language models, often exceed the capabilities of consumer-grade hardware and are expensive to run. Compressing models through methods such as quantization makes them more efficient, faster, and accessible. This allows them to run on a wide variety of devices, including smartphones, personal computers, and edge devices, and minimizes performance degradation.
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Generative AI models, like large language models, often exceed the capabilities of consumer-grade hardware and are expensive to run. Compressing models through methods such as quantization makes them more efficient, faster, and accessible. This allows them to run on a wide variety of devices, including smartphones, personal computers, and edge devices, and minimizes performance degradation.
Generative AI models, like large language models, often exceed the capabilities of consumer-grade hardware and are expensive to run. Compressing models through methods such as quantization makes them more efficient, faster, and accessible. This allows them to run on a wide variety of devices, including smartphones, personal computers, and edge devices, and minimizes performance degradation.
Join this course to:
1. Quantize any open source model with linear quantization using the Quanto library.
2. Get an overview of how linear quantization is implemented. This form of quantization can be applied to compress any model, including LLMs, vision models, etc.
3. Apply “downcasting,” another form of quantization, with the Transformers library, which enables you to load models in about half their normal size in the BFloat16 data type.
By the end of this course, you will have a foundation in quantization techniques and be able to apply them to compress and optimize your own generative AI models, making them more accessible and efficient.
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Activities
Review: 'Deep Learning with Python' by François Chollet
Show steps
Expand your understanding of deep learning concepts and techniques, which are fundamental to generative AI models.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
-
Read specific chapters relevant to generative AI and quantization.
-
Take notes and summarize key concepts.
-
Apply the knowledge gained to your generative AI projects.
Transformers Library Downcasting Tutorial
Show steps
Follow a guided tutorial to gain hands-on experience with downcasting using the Transformers library.
Show steps
-
Access the official Transformers library documentation.
-
Follow the step-by-step downcasting tutorial.
-
Implement downcasting in your own generative AI projects.
Review 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Understand the fundamentals of generative AI and large language models.
View
Deep Learning
on Amazon
Show steps
-
Read the first four chapters of the book.
-
Summarize the key concepts in each chapter.
-
Identify the connections between these concepts and the course material.
Nine other activities
Expand to see all activities and additional details
Show all 12 activities
Quantization Calculations Practice
Show steps
Practice applying quantization calculations to optimize model efficiency.
Show steps
-
Review quantization equations and formulas.
-
Solve practice problems involving quantization calculations.
-
Apply quantization calculations to real-world scenarios.
Practice linear quantization with the Quanto library
Show steps
Develop hands-on experience with linear quantization techniques.
Browse courses on
Quantization
Show steps
-
Install the Quanto library.
-
Load a pre-trained model into Quanto.
-
Quantize the model using linear quantization.
-
Evaluate the accuracy of the quantized model.
Follow tutorials on downcasting with the Transformers library
Show steps
Gain practical experience in applying downcasting for model optimization.
Browse courses on
Quantization
Show steps
-
Find tutorials on downcasting with the Transformers library.
-
Follow the tutorials to learn how to downcast a model to BFloat16.
-
Experiment with different downcasting techniques to optimize model performance.
BFloat16 Downcasting Exercises
Show steps
Gain proficiency in applying downcasting to reduce model size and optimize performance.
Show steps
-
Understand the concept of downcasting.
-
Practice downcasting on sample models.
-
Implement downcasting in your own generative AI projects.
Quantization Study Group
Show steps
Engage with peers to discuss and explore quantization techniques in depth.
Show steps
-
Form a study group with other students in the course.
-
Choose specific quantization topics to focus on.
-
Prepare presentations and lead discussions.
-
Collaborate on projects and share knowledge.
-
Provide feedback and support to group members.
Attend a conference or workshop on quantization
Show steps
Expand knowledge and connect with experts in the field of quantization.
Browse courses on
Quantization
Show steps
-
Find a conference or workshop on quantization that aligns with your interests.
-
Register for the event and attend the sessions related to quantization.
-
Network with other attendees and speakers to learn about their work and perspectives.
Quantization Tutorial Video
Show steps
Create a video tutorial explaining linear quantization and its benefits for generative AI models.
Show steps
-
Plan and outline the tutorial content.
-
Record the video tutorial using screen capture software.
-
Edit and polish the video, adding visuals and narration.
-
Publish the video tutorial on a platform like YouTube or Vimeo.
-
Promote the video tutorial to relevant audiences.
Create a presentation on the applications of quantization
Show steps
Demonstrate understanding of the practical uses of quantization.
Browse courses on
Quantization
Show steps
-
Research the various applications of quantization in different industries.
-
Identify the advantages and disadvantages of quantization for each application.
-
Create a presentation that summarizes your findings.
Write a blog post on the benefits of quantization for generative AI models
Show steps
Demonstrate a deep understanding of the advantages of quantization in the context of generative AI.
Browse courses on
Quantization
Show steps
-
Research the benefits of quantization for generative AI models.
-
Write a blog post that explains these benefits to a technical audience.
-
Publish the blog post on a relevant platform.
Review: 'Deep Learning with Python' by François Chollet
Show steps
Expand your understanding of deep learning concepts and techniques, which are fundamental to generative AI models.
View
Deep Learning with Python, Second Edition
on Amazon
Show steps
- Read specific chapters relevant to generative AI and quantization.
- Take notes and summarize key concepts.
- Apply the knowledge gained to your generative AI projects.
Transformers Library Downcasting Tutorial
Show steps
Follow a guided tutorial to gain hands-on experience with downcasting using the Transformers library.
Show steps
- Access the official Transformers library documentation.
- Follow the step-by-step downcasting tutorial.
- Implement downcasting in your own generative AI projects.
Review 'Deep Learning' by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
Show steps
Understand the fundamentals of generative AI and large language models.
View
Deep Learning
on Amazon
Show steps
- Read the first four chapters of the book.
- Summarize the key concepts in each chapter.
- Identify the connections between these concepts and the course material.
Quantization Calculations Practice
Show steps
Practice applying quantization calculations to optimize model efficiency.
Show steps
- Review quantization equations and formulas.
- Solve practice problems involving quantization calculations.
- Apply quantization calculations to real-world scenarios.
Practice linear quantization with the Quanto library
Show steps
Develop hands-on experience with linear quantization techniques.
Browse courses on
Quantization
Show steps
- Install the Quanto library.
- Load a pre-trained model into Quanto.
- Quantize the model using linear quantization.
- Evaluate the accuracy of the quantized model.
Follow tutorials on downcasting with the Transformers library
Show steps
Gain practical experience in applying downcasting for model optimization.
Browse courses on
Quantization
Show steps
- Find tutorials on downcasting with the Transformers library.
- Follow the tutorials to learn how to downcast a model to BFloat16.
- Experiment with different downcasting techniques to optimize model performance.
BFloat16 Downcasting Exercises
Show steps
Gain proficiency in applying downcasting to reduce model size and optimize performance.
Show steps
- Understand the concept of downcasting.
- Practice downcasting on sample models.
- Implement downcasting in your own generative AI projects.
Quantization Study Group
Show steps
Engage with peers to discuss and explore quantization techniques in depth.
Show steps
- Form a study group with other students in the course.
- Choose specific quantization topics to focus on.
- Prepare presentations and lead discussions.
- Collaborate on projects and share knowledge.
- Provide feedback and support to group members.
Attend a conference or workshop on quantization
Show steps
Expand knowledge and connect with experts in the field of quantization.
Browse courses on
Quantization
Show steps
- Find a conference or workshop on quantization that aligns with your interests.
- Register for the event and attend the sessions related to quantization.
- Network with other attendees and speakers to learn about their work and perspectives.
Quantization Tutorial Video
Show steps
Create a video tutorial explaining linear quantization and its benefits for generative AI models.
Show steps
- Plan and outline the tutorial content.
- Record the video tutorial using screen capture software.
- Edit and polish the video, adding visuals and narration.
- Publish the video tutorial on a platform like YouTube or Vimeo.
- Promote the video tutorial to relevant audiences.
Create a presentation on the applications of quantization
Show steps
Demonstrate understanding of the practical uses of quantization.
Browse courses on
Quantization
Show steps
- Research the various applications of quantization in different industries.
- Identify the advantages and disadvantages of quantization for each application.
- Create a presentation that summarizes your findings.
Write a blog post on the benefits of quantization for generative AI models
Show steps
Demonstrate a deep understanding of the advantages of quantization in the context of generative AI.
Browse courses on
Quantization
Show steps
- Research the benefits of quantization for generative AI models.
- Write a blog post that explains these benefits to a technical audience.
- Publish the blog post on a relevant platform.
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