QLoRA: The Gen AI Breakthrough You Need to See скачать в хорошем качестве

QLoRA: The Gen AI Breakthrough You Need to See

Explore Quantized Low-Rank Adaptation (QLoRA), a cutting-edge method combining LoRA and quantization to optimize large language model (LLM) fine-tuning. This video covers how QLoRA reduces trainable parameters and memory needs by leveraging matrix decomposition and four-bit quantization. Learn how QLoRA achieves efficiency without sacrificing precision and why it’s a game-changer for AI model training. Course Link HERE: https://sds.courses/genAI You can also find us here: Website: https://www.superdatascience.com/ Facebook:   / superdatascience   Twitter:   / superdatasci   LinkedIn:   / superdatascience   Contact us at: support@superdatascience.com Chapters: 00:00 – Introduction to QLoRA 00:30 – LoRA Recap and Quantization Overview 01:08 – Floating Point Data Types: Float32 vs Float16 01:44 – Challenges of Memory Requirements for LLMs 02:28 – Basics of Quantization Explained 03:04 – Reducing Memory with 4-Bit Quantization 04:17 – Example of Quantization Binning Process 05:23 – Trade-Offs in Quantization: Precision vs Memory 05:54 – Adjusting Bins for Normal Distribution in LLMs 06:34 – Optimal Bin Adjustment for QLoRA 07:47 – Balancing Efficiency and Precision with QLoRA 08:21 – Theoretical Insights on QLoRA Efficiency 08:58 – Recommended Resources for Deeper Learning 09:38 – Conclusion and Additional Learning Opportunities #QLoRA #QuantizedLoRA #LoRA #AITraining #AIOptimization #LLMFineTuning #Quantization #EfficientAI #MemoryEfficientAI #MatrixDecomposition #AIModels #DeepLearning #TechTutorial #MachineLearning #AIResearch