Course Prerequisites
Certainly! Here are the minimum prerequisites required for successfully undertaking training in the Quantization of Large Language Models course:
- Understanding of Basic Machine Learning Concepts: Familiarity with fundamental machine learning concepts and how models like neural networks function.
- Proficiency in Python Programming: Ability to write and understand Python code, as the course involves hands-on programming exercises.
- Familiarity with Deep Learning Libraries: Basic knowledge of PyTorch or similar libraries will be beneficial since the course includes custom coding for quantization.
- Basic Knowledge of Neural Network Architectures: Understanding of different types of neural network architectures, especially transformers, as they are pertinent to large language models.
- Introductory Level of Data Types Knowledge: Awareness of different data types used in programming and their impact on memory and computation.
These prerequisites are designed to ensure that participants can effectively grasp the concepts and practical applications covered in the course.
Target Audience for Quantization of Large Language Model
The "Quantization of Large Language Model" course optimizes AI model efficiency on various devices, tailored for professionals enhancing computing performance and AI application development.
- AI/ML Engineers
- Data Scientists
- Embedded Systems Engineers
- Software Developers focusing on AI applications
- Technical Leads managing AI projects
- AI Research Scientists
- DevOps Engineers involved in AI deployment
- Technology Innovators and Entrepreneurs
- Hardware Engineers designing AI-enabled devices
- IT Professionals in charge of infrastructure optimization
Learning Objectives - What you will Learn in this Quantization of Large Language Model?
Introduction to Course Learning Outcomes:
This course aims to equip students with practical skills in quantizing large language models using various techniques, enhancing model efficiency and broadening deployment capabilities across devices.
Learning Objectives and Outcomes:
- Understand the fundamentals and applications of model quantization, specifically linear quantization, to make large models more computationally efficient.
- Utilize the Quanto library to apply linear quantization to open source models, transforming their operational demands to suit less powerful hardware.
- Gain insights into the implementation of linear quantization and its benefits across different types of AI models, including LLMs and vision models.
- Learn and apply "downcasting" using the Transformers library to reduce model size by loading models in the BFloat16 data type.
- Master the building and customization of linear quantization functions, learning to select between asymmetric and symmetric modes.
- Choose appropriate quantization granularities: per-tensor, per-channel, and per-group, to optimize model performance.
- Evaluate and measure the quantization error to understand the trade-offs between performance enhancement and space efficiency.
- Develop skills to build your custom quantizer in PyTorch, enabling quantization of dense layers from 32 bits to 8 bits.
- Explore advanced quantization strategies
Target Audience for Quantization of Large Language Model
The "Quantization of Large Language Model" course optimizes AI model efficiency on various devices, tailored for professionals enhancing computing performance and AI application development.
- AI/ML Engineers
- Data Scientists
- Embedded Systems Engineers
- Software Developers focusing on AI applications
- Technical Leads managing AI projects
- AI Research Scientists
- DevOps Engineers involved in AI deployment
- Technology Innovators and Entrepreneurs
- Hardware Engineers designing AI-enabled devices
- IT Professionals in charge of infrastructure optimization
Learning Objectives - What you will Learn in this Quantization of Large Language Model?
Introduction to Course Learning Outcomes:
This course aims to equip students with practical skills in quantizing large language models using various techniques, enhancing model efficiency and broadening deployment capabilities across devices.
Learning Objectives and Outcomes:
- Understand the fundamentals and applications of model quantization, specifically linear quantization, to make large models more computationally efficient.
- Utilize the Quanto library to apply linear quantization to open source models, transforming their operational demands to suit less powerful hardware.
- Gain insights into the implementation of linear quantization and its benefits across different types of AI models, including LLMs and vision models.
- Learn and apply "downcasting" using the Transformers library to reduce model size by loading models in the BFloat16 data type.
- Master the building and customization of linear quantization functions, learning to select between asymmetric and symmetric modes.
- Choose appropriate quantization granularities: per-tensor, per-channel, and per-group, to optimize model performance.
- Evaluate and measure the quantization error to understand the trade-offs between performance enhancement and space efficiency.
- Develop skills to build your custom quantizer in PyTorch, enabling quantization of dense layers from 32 bits to 8 bits.
- Explore advanced quantization strategies
