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The Quantization of Large Language Model course at Koenig Solutions is a comprehensive one-day (8 hours) training designed to arm participants with the skills needed to make advanced generative AI models more efficient and accessible. Through practical exercises, learners will master linear quantization using the Quanto library, understand and implement downcasting with the Transformers library, and explore both asymmetric and symmetric methods in quantization. By building custom quantization functions in PyTorch, participants will not only reduce the computational demands of models but also ensure they run effectively on devices ranging from smartphones to edge devices. This course bridges the gap between theoretical knowledge and real-world application, making it crucial for anyone looking to enhance model performance while managing resource use efficiently.
Purchase This Course
USD
View Fees Breakdown
Flexi Video | 16,449 |
Official E-coursebook | |
Exam Voucher (optional) | |
Hands-On-Labs2 | 4,159 |
+ GST 18% | 4,259 |
Total Fees (without exam & Labs) |
22,359 (INR) |
Total Fees (with exam & Labs) |
28,359 (INR) |
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
♱ Excluding VAT/GST
You can request classroom training in any city on any date by Requesting More Information
Certainly! Here are the minimum prerequisites required for successfully undertaking training in the Quantization of Large Language Models course:
These prerequisites are designed to ensure that participants can effectively grasp the concepts and practical applications covered in the course.
The "Quantization of Large Language Model" course optimizes AI model efficiency on various devices, tailored for professionals enhancing computing performance and AI application development.
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: