16-01-2026, 13:20 -
[center]![[Obrazek: af6300aaaea7e89c01b6410fd2245c86.jpg]](https://i126.fastpic.org/big/2026/0116/86/af6300aaaea7e89c01b6410fd2245c86.jpg)
Advanced Quantization Techniques For Large Language Models
Released 1/2026
With Nayan Saxena
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 10m | Size: 130 MB [/center]
Master advanced quantization techniques for transformer models, from mathematical foundations to practical applications, maximizing efficiency while preserving model quality.
Course details
Discover cutting-edge quantization techniques for large language models, focusing on the algorithms and optimization strategies that deliver the best performance. Instructor Nayan Saxena begins by covering mathematical foundations, before progressing through advanced methods including GPTQ, AWQ, and SmoothQuant with hands-on examples in Google Colab. Along the way, gather quick tips to master critical concepts such as precision formats, calibration strategies, and evaluation methodologies. Leveraging both theoretical principles and practical applications, this course equips you with in-demand skills to significantly reduce model size and accelerate inference while maintaining performance quality.
Skills covered
Large Language Models (LLM), Model Training, Quantization Techniques
Advanced Quantization Techniques For Large Language Models
Released 1/2026
With Nayan Saxena
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz, 2 Ch
Skill level: Advanced | Genre: eLearning | Language: English + subtitle | Duration: 1h 10m | Size: 130 MB [/center]
Master advanced quantization techniques for transformer models, from mathematical foundations to practical applications, maximizing efficiency while preserving model quality.
Course details
Discover cutting-edge quantization techniques for large language models, focusing on the algorithms and optimization strategies that deliver the best performance. Instructor Nayan Saxena begins by covering mathematical foundations, before progressing through advanced methods including GPTQ, AWQ, and SmoothQuant with hands-on examples in Google Colab. Along the way, gather quick tips to master critical concepts such as precision formats, calibration strategies, and evaluation methodologies. Leveraging both theoretical principles and practical applications, this course equips you with in-demand skills to significantly reduce model size and accelerate inference while maintaining performance quality.
Skills covered
Large Language Models (LLM), Model Training, Quantization Techniques
Cytat:https://rapidgator.net/file/6846cf69b06e...s.rar.html
https://nitroflare.com/view/4C40AB3B30D5...Models.rar