jypi
  • Explore
ChatWays to LearnMind mapAbout

jypi

  • About Us
  • Our Mission
  • Team
  • Careers

Resources

  • Ways to Learn
  • Mind map
  • Blog
  • Help Center
  • Community Guidelines
  • Contributor Guide

Legal

  • Terms of Service
  • Privacy Policy
  • Cookie Policy
  • Content Policy

Connect

  • Twitter
  • Discord
  • Instagram
  • Contact Us
jypi

© 2026 jypi. All rights reserved.

Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)
Chapters

1Foundations of Fine-Tuning

2Performance and Resource Optimization

3Parameter-Efficient Fine-Tuning Methods

4Data Efficiency and Curation

5Quantization, Pruning, and Compression

5.1 Quantization Basics for LLMs5.2 Post-Training Quantization vs Quantization-Aware Training5.3 8-bit, 4-bit and Beyond5.4 Calibration Techniques for Quantization5.5 Structured vs Unstructured Pruning5.6 Pruning During Fine-Tuning5.7 Knowledge Distillation for Efficiency5.8 Weight Sharing and Parameter Tying5.9 Quantization-Aware Fine-Tuning (QAT-Fine-Tune)5.10 Inference Acceleration with Quantized Weights5.11 Storage Reductions and Bandwidth5.12 Accuracy and Latency Impacts5.13 Hardware Support and Deployment Implications5.14 Mixed-Precision Safety Guidelines5.15 End-to-End Quantization Pipelines

6Scaling and Distributed Fine-Tuning (DeepSpeed, FSDP, ZeRO)

7Evaluation, Validation, and Monitoring

8Real-World Applications and Deployment

9Future of Fine-Tuning (Mixture of Experts, Retrieval-Augmented Fine-Tuning, Continual Learning)

10Practical Verification, Debugging, and Validation Pipelines

11Cost Modeling, Budgeting, and Operational Efficiency

12Bonus Labs: Hands-on with Hugging Face PEFT and QLoRA on Llama/Mistral

Courses/Performance-Efficient Fine-Tuning: Mastering Scalable and Cost-Effective LLM Training (How to Tame and Train Your Draconian Language Model)/Quantization, Pruning, and Compression

Quantization, Pruning, and Compression

535 views

Techniques to shrink models and accelerate inference—quantization, pruning, distillation, and end-to-end compression pipelines with attention to accuracy, latency, and hardware support.

Content

4 of 15

5.4 Calibration Techniques for Quantization

Original version
4 views

Versions:

Version 17277

Watch & Learn

AI-discovered learning video

Sign in to watch the learning video for this topic.

Sign inSign up free

Start learning for free

Sign up to save progress, unlock study materials, and track your learning.

  • Bookmark content and pick up later
  • AI-generated study materials
  • Flashcards, timelines, and more
  • Progress tracking and certificates

Free to join · No credit card required

Unlock this content

Sign up free to view this chapter, save your progress, and unlock study modes.

  • Full chapters & explanations
  • Flashcards & practice
  • Track progress
Sign inCreate free account
Flashcards
Mind Map
Speed Challenge

Comments (0)

Please sign in to leave a comment.

No comments yet. Be the first to comment!

Ready to practice?

Sign up now to study with flashcards, practice questions, and more — and track your progress on this topic.

Study with flashcards, timelines, and more
Earn certificates for completed courses
Bookmark content for later reference
Track your progress across all topics