Fine-Tune & Deploy Llms With Qlora On Sagemaker + Streamlit
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 3.56 GB | Duration: 7h 12m
Master QLoRA Math, Mixed Precision Training, Double Quantization, Lambda functions, API Gateway & Streamlit deployment
What you’ll learn
Train/Fine Tune LLMs in AWS Sagemaker using QLoRA and advanced 4-bit quantization on your own dataset
Create an interactive Streamlit app to deploy your fine tuned LLM with Sagemaker, Lambda Functions, and API Gateway
Master QLoRA fine-tuning – including adapter injection, memory optimization, parameter freezing, and the mathematics behind it
Leverage bfloat16 compute types for faster and more efficient training on modern GPUs
Understand mixed precision training with qLoRA in Sagemaker
Use Parameter Efficient Fine Tuning(PEFT) to dynamically find and inject LoRA layers
Understand the entire low-level fine-tuning pipeline – from raw dataset to trained model
Use double quantization and nf4 precision to compress models without losing performance
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