Efficient Fine-Tuning of LLMs: LoRA and QLoRA in Enterprise AI LangGraph Workflows

Author(s): Samvardhan Singh

Originally published on Towards AI.

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Large Language Models (LLMs) like GPT-4, LLaMA, and Falcon have revolutionized enterprise AI. They power everything from intelligent chatbots to document summarization, but fine-tuning these models on enterprise-specific data is traditionally expensive and hardware-intensive.

That’s where LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation) come in. These methods make it possible to fine-tune huge LLMs faster and cheaper ,even on a single GPU without sacrificing much performance. This article explores how these techniques work, why they save so many resources, how they can be integrated into enterprise workflows using LangGraph, and includes real-world use cases, code, and comparisons.

Imagine you’re handed a massive cookbook with millions of recipes, but you only need to tweak it to make desserts for a specific bakery. Rewriting every recipe would be exhausting and expensive. Instead, what if you could add a few sticky notes with adjustments just for the desserts? That’s the essence of LoRA (Low-Rank Adaptation) and QLoRA (Quantized Low-Rank Adaptation), smart techniques that let enterprises fine-tune large language models (LLMs) quickly and affordably…. Read the full blog for free on Medium.

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Published via Towards AI