QLoRA 4bit Quantization for memory efficient fine-tuning of LLMs explained in detailed. 4-bit quantization QLoRA for beginners, theory and code. PEFT - parameter efficient fine-tuning methods.
Based on my first videos on the theory of LoRA and other PEFT methods (https://youtu.be/YVU5wAA6Txo) and the detailed code implementation of LoRA in my video (https://youtu.be/A-a-l_sFtYM) now my third video on 4-bit quantization and QLoRA.
An additional Colab NB with code to fine-tune FALCON 7B with QLoRA 4-bit quantization and Transformer Reinforcement Learning (TLR).
Huggingface Accelerate now supports 4-bit QLoRA LLM models.
https://github.com/huggingface/accelerate
QLoRA 4-bit Colab NB:
(all rights with Author Artidoro Pagnoni)
https://colab.research.google.com/drive/1BiQiw31DT7-cDp1-0ySXvvhzqomTdI-o?usp=sharing
#4bit
#4bits
#quantization
#languagemodel
#largelanguagemodels