QLoRA PEFT Walkthrough! Hyperparameters Explained, Dataset Requirements, and Comparing Repo's.
About this lesson
Today explore two different applications for fine-tuning large language models using QLoRAs: the Alpaca QLoRA and the official QLoRA. We delve into the setup and installation process, highlighting the ease of the Alpaca QLoRA compared to the more powerful but complex official QLoRA. Walkthroughs of each setup, troubleshooting advice, as well as an explanation of the functionalities and differences between both are provided. We also look into creating custom repositories to simplify the process for WSL and Windows users. The video offers a deep dive into hyperparameters and their impacts, explains the merging process of the LoRAs back into the model, and presents the application running process. Toward the end, we compare the official QLoRA with a more user-friendly tool. Whether you're a seasoned developer or a novice, this video provides comprehensive coverage to help you leverage QLoRA's in fine-tuning your large language models. 0:00 Intro 0:42 QLoRA - BitsAndBytes Issues 2:38 QLoRA - Adding Custom Datasets 3:21 Datasets That QLoRA Can Use 4:42 Hyperparameters For QLoRA 8:15 Finetuning With QLoRA 9:40 Merging The QLoRA's 11:34 Finetuning With Alpaca-QLoRA 12:24 Launch Alpaca-QLoRA 13:31 Alpaca-QLoRA UI 14:35 Outro QLoRA Collab (credit to ankleBowl): https://colab.research.google.com/drive/1UPor1Z7BjiSZoneF-GZ8Ao-G7--UpXp_?usp=sharing Custom BitsAndBytes: git clone https://github.com/Aemon-Algiz/bitsandbytes.git Installation: cd bitsandbytes export CUDA_VERSION=11{your_version} make cuda11x pip uninstall bitsandbytes python setup.py install Custom QLoRA Repository: git clone https://github.com/Aemon-Algiz/qlora.git Alpaca-QLoRA Repsitory: https://github.com/vihangd/alpaca-qlora #AI #PEFT #QLoRA #LargeLanguageModels #FineTuning #LLM #AlpacaQLoRA
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