LoRA, Explained With Spreadsheets: How to Train a Model Without Touching Its Weights

2026-07-26 · Source: Artificial Intelligence on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering · Depth: Intermediate, long

Summary

LoRA (Low-Rank Adaptation) is a technique enabling the training of large language models on resource-constrained hardware, such as a 6GB laptop GPU. It achieves this by freezing the majority of a model's original weights and instead training a small, additive set of new weights. For instance, a model with 494,032,768 parameters can be trained by updating as little as 0.055% of its weights (at r=4), compared to 100% in traditional fine-tuning. LoRA works by creating two small matrices, A and B, whose product forms a correction layer added to the frozen original weights. Only these A and B matrices are updated during training. The article demonstrates this by training a model to append a specific sign-off phrase, showing how it learns new patterns while highlighting that narrow training can inadvertently alter existing knowledge.

Key takeaway

For AI Engineers or ML Engineers facing GPU memory constraints, LoRA offers a critical solution for fine-tuning large models. You should implement LoRA to train models on hardware like a 6GB RTX 4050 by updating only a fraction of parameters. Be mindful that training with small, narrow datasets can inadvertently alter existing model knowledge, requiring careful dataset curation and evaluation.

Key insights

LoRA enables efficient large model training by updating only a tiny fraction of parameters, significantly reducing memory needs.

Principles

• Freeze original model weights.
• Train small, low-rank adapter matrices.
• Scale correction with "lora_alpha".

Method

Attach "LoraConfig" with "r", "lora_alpha", "target_modules", and "lora_dropout" to a pre-trained model using "get_peft_model". Train only the new A and B matrices, which apply a correction layer to the frozen base model.

In practice

• Fine-tune LLMs on 6GB GPUs.
• Adjust "r" for detail vs. cost.
• Save small, task-specific adapters.

Topics

• LoRA
• Parameter-Efficient Fine-Tuning
• Large Language Models
• GPU Memory Optimization
• Model Training
• Quantization

Code references

• NehaKhann/ai-engineering-journey

Best for: AI Engineer, Machine Learning Engineer, AI Student

Related on AIssential

• LoRA, Explained With Spreadsheets: How to Train a Model Without Touching Its Weights — LoRA enables efficient model training by adapting a small, low-rank matrix instead of modifying all original weights.
• What is LoRA actually? — LoRA efficiently adapts large models by learning low-rank weight updates, drastically reducing parameters without sacrificing quality or adding latency.
• Fine-Tuning a 7B LLM Required 4 A100s. LoRA Did It on One GPU. Here Is the Math Behind Why. — Fine-tuning LLMs involves low-dimensional weight updates, enabling efficient adaptation via LoRA and QLoRA.
• What Is Fine Tuning AI Models And When Should You Actually Do It? — Fine-tuning specializes pre-trained AI models for consistent, domain-specific behavior, bridging the gap between general capability and business utility.
• What Happens When You Use LoRA On a CNN — LoRA can facilitate some transfer learning in CNNs, but its effectiveness varies significantly with dataset complexity.
• Fine-Tuning NVIDIA Cosmos Predict 2.5 with LoRA/DoRA for Robot Video Generation — LoRA/DoRA fine-tuning effectively adapts large world models like Cosmos Predict 2.5 for domain-specific robot video generation.

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Editorial summary, takeaway, and curation by AIssential. Original article published by Artificial Intelligence on Medium.