Certificate of Completion
Practical Quantization for Large Language ModelsPresented To
Reshal Dahima
Certificate ID
qyqf-8zf9-ydce-qd5q
Issued Date
June 22, 2026
Description
Learn techniques to reduce the size and computational cost of Large Language Models (LLMs) through quantization. This course provides practical methods for applying quantization, covering popular techniques like Post-Training Quantization (PTQ) and Quantization-Aware Training (QAT), common formats such as GPTQ and GGUF, and evaluating performance trade-offs. Gain hands-on experience quantizing LLMs for efficient deployment.
Certificate Details
Course Level:
Professional
Issued Date:
June 22, 2026
Certificate ID:
qyqf-8zf9-ydce-qd5q
Learning Outcomes
- Understand the fundamental concepts behind model quantization and its benefits for LLMs.
- Implement various PTQ techniques, including calibration and handling outliers.
- Apply advanced PTQ algorithms like GPTQ and understand methods like AWQ.
- Understand the concepts of QAT and how to simulate quantization during training.
- Work with common quantization formats (GGUF, GPTQ) and libraries (Hugging Face Optimum, bitsandbytes).
- Evaluate the performance and accuracy trade-offs of quantized LLMs and understand deployment considerations.