In-depth guide to fine-tuning LLMs with LoRA and QLoRA

Language Models like GPT-4 have become the de facto standard in the NLP industry for building products and applications. These models are capable of performing a plethora of tasks and can easily adapt to new tasks using Prompt Engineering Techniques. But these models also present a massive challenge around training. Massive models like GPT-4 cost millions of dollars to train, hence we use smaller models in production settings. ‍ But smaller models on the other hand cannot generalize to multiple tasks, and we end up having multiple models for multiple tasks of multiple users. This is where PEFT techniques like LoRA come in, these techniques allow you to train large models much more efficiently compared to fully finetuning them. In this blog, we will walk through LoRA, QLoRA, and other popular techniques that emerged specifically from LoRA. PEFT Finetuning is Parameter Efficient Fine Tuning, a set of fine-tuning techniques that allows you to fine-tune and train models much more efficient

Language Models like GPT-4 have become the de facto standard in the NLP industry for building products and applications. These models are capable of performing a plethora of tasks and can easily adapt to new tasks using Prompt Engineering Techniques . But these models also present a massive challenge around training. Massive models like GPT-4 cost millions of dollars to train, hence we use smaller models in production settings. But smaller models on the other hand cannot generalize to multiple tasks, and we end up having multiple models for multiple tasks of multiple users. This is where PEFT

related reading

• LoRA Without Regret - Thinking Machines Labthinkingmachines.ai
• A Guide to Quantization in LLMs | Symbl.aisymbl.ai
• Finetune LLMs on your own consumer hardware using tools from PyTorch and Hugging Face ecosystem – PyTorchpytorch.org
• Efficient LLM Finetuning with Unsloth | Modal Docsmodal.com
• [2106.09685] LoRA: Low-Rank Adaptation of Large Language Modelsarxiv.org
• LoRA vs Full Fine-tuning: An Illusion of Equivalencearxiv.org
• Parameter-Efficient LLM Finetuning With Low-Rank Adaptation (LoRA) - Lightning AIlightning.ai
• Fine-tuning LLMs Guide | Unsloth Documentationdocs.unsloth.ai
• The Llama Hitchiking Guide to Local LLMs – hackerllamaosanseviero.github.io
• Mastering LLM Techniques: Customization | NVIDIA Technical Blogdeveloper.nvidia.com
• Fine-tuning a LLM on my blog posts | Didier Lopesdidierlopes.com
• What Makes Low-Bit Quantization-Aware Training Work for Reasoning LLMs? A Systematic Studyarxiv.org