LoRA (Low-rank Adaption of AI Large Language Models) for fine-tuning LLM models

What is LoRA? How does LoRA work? Low-Rank Adaptation (LoRA) for Parameter-Efficient LLM Finetuning explained right from Rank Decomposition to how LoRA is suitable for transformers. LoRA is fast becoming (already is?) the go to approach to fine-tuning transformers based models in budget! RELATED LINKS Paper Title: LoRA: Low-Rank Adaptation of Large Language Models LoRA Paper: https://arxiv.org/abs/2106.09685 QLoRA Paper: https://arxiv.org/abs/2305.14314 LoRA official code: https://github.com/microsoft/LoRA Parameter-Efficient Fine-Tuning (PEFT) Adapters paper: https://arxiv.org/abs/1902.00751 Parameter-Efficient Fine-Tuning (PEFT) library: https://github.com/huggingface/peft HuggingFace LoRA training: https://huggingface.co/docs/diffusers/training/lora HuggingFace LoRA notes: https://huggingface.co/docs/peft/conceptual_guides/lora ⌚️ ⌚️ ⌚️ TIMESTAMPS ⌚️ ⌚️ ⌚️ 0:00 - Intro 0:58 - Adapters 1:48 - Twitter (https://twitter.com/ai_bites) 2:13 - What is LoRA 3:17 - Rank Decomposition 4:28 - Motivation Paper 5:02 - LoRA Training 6:53 - LoRA Inference 8:24 - LoRA in Transformers 9:20 - Choosing the rank 9:50 - Implementations MY KEY LINKS YouTube: https://www.youtube.com/@AIBites Twitter: https://twitter.com/ai_bites​ Patreon: https://www.patreon.com/ai_bites​ Github: https://github.com/ai-bites​