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Hands-on LoRa: Practical Fine-tuning LLMs using LoRa

Deep Learning is an experimental science. If your hands aren't dirty, how can your mind be nifty?

Introduction

: "LoRA, which freezes the pretrained model weights and injects trainable rank decomposition matrices into each layer of the Transformer architecture, greatly reducing the number of trainable parameters for downstream tasks."

Trainable A & B (only)

Examples

LLM	No. Parameters	Task	LoRa/QLoRa	Code
Gemma-IT	2B	Text-to-text Generation	QLoRa	Link
Qwen 2	1.5B	Named Entity Recognition	QLoRa	Link
Llama 3	8B	Cross-Linguistic Adaptation	LoRa	Link

Note

LoRa is an elegant technique, yet fine-tuning LLMs with it demands considerable engineering effort. Optimal performance requires thorough optimization. In our repository, we provide foundational examples—consider them your starting point. There are numerous steps to achieve excellence. We encourage you to leverage your talents and creativity to achieve more outstanding results.