---
title: "Hire LoRA and QLoRA Fine-tuning Specialists, Offshore Team"
description: "LoRA vs QLoRA? Our LoRA and QLoRA fine-tuning specialists adapt models affordably on modest GPUs and tell you which fits. Dedicated AI team, managed by Azendo."
url: "https://azendo.co/skills/lora-and-qlora-fine-tuning/"
---

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3. LoRA and QLoRA fine-tuning

# LoRA and QLoRA fine-tuning specialists for your dedicated team.

LoRA fine-tunes a model by training small low-rank adapter matrices while the base weights stay frozen. QLoRA adds quantisation of the base model, allowing large models to be adapted on a single GPU.

Our LoRA and QLoRA fine-tuning specialists adapt an open-weight model to your data on hardware you can afford. They measure it against a good prompt first, so you only train when training wins.

## When LoRA or QLoRA is the right tool.

Fine-tuning pays off when prompting has hit a ceiling. The model has to answer in a strict format every time, or write in your domain's vocabulary and house style. Examples in the prompt only half teach that. A LoRA adapter trained on a few thousand good examples can close that gap.

It also helps when cost or privacy rules out a large hosted model. Take a small open-weight model such as Llama, Mistral or Qwen and tune it for one narrow task. It can then do that task as well as a much larger general model. It runs in your cloud account, where the data already is.

LoRA vs QLoRA is mostly a memory question. LoRA trains small adapter matrices on top of a frozen model. QLoRA does the same on a base model loaded in 4-bit precision, so a 7-billion-parameter model can be tuned on one 24 GB GPU.

## A fine-tuning sprint, task by task.

* Dataset building Turning support tickets, documents or logged model outputs into a few thousand clean instruction and response pairs. A test set is held back before any training starts.
* Baseline Scoring a well-written prompt, and retrieval where it fits, on that test set. The fine-tune now has a number to beat.
* Training runs Training adapters with Hugging Face PEFT and TRL, with bitsandbytes for 4-bit loading. Rank, alpha, target modules and learning rate are compared across runs tracked in MLflow or Weights & Biases.
* Evaluation Checking each adapter on the held-back set for task accuracy and format errors, and on general questions the base model used to answer well.
* Serving Merging the adapter into the base weights for one fast model. Or loading several adapters on a single base in vLLM, so task variants share a GPU.
* Repeatable pipeline Scripting the whole run, so a new base model or fresh data means one command and a new evaluation report.

This work belongs to the LLM engineers we assign, who work full time in your cloud account and on your data. Companies that [hire AI engineers](https://azendo.co/services/dedicated-ai-engineering-team/) through us pay one fixed monthly fee for a managed AI engineering team, and the code they write is yours.

## Mistakes that waste a fine-tuning budget.

Skipping the baseline is the most common mistake. A team spends three weeks on a training pipeline and never learns that a better prompt would have reached the same score in two days. A careful engineer measures prompting and retrieval first and fine-tunes only for the gap that is left.

Leaks in the evaluation are harder to see. When near-copies of the test examples sit in the training data, the scores look excellent and the model fails on real input. Splitting the data by customer, document or date keeps the test set clean.

A prompt template that differs between training and serving is a small error with a large effect. One missing special token and a well-trained adapter answers like an untrained one. The fix is to use the tokeniser's chat template in both places and to test the served model as well as the checkpoint.

## Questions about LoRA and QLoRA.

* LoRA or QLoRA: which should we use? Start from your GPU. If the model fits in memory at 16-bit precision, plain LoRA trains faster and has one less thing to go wrong. If it doesn't fit, QLoRA loads the base in 4-bit and gets you training on a single card. Your evaluation set should confirm that quality held.
* How much training data do we need? Less than most teams expect. A few hundred clean examples can teach a format, and a few thousand usually cover a narrow task. Wrong labels do more harm than missing rows, so cleaning comes before collecting more.
* Where does our training data go? It stays where it is. The engineers on your team work inside your accounts, under the permissions you set. The data and the adapters never leave your environment. Check the licence of the base model too, because open-weight models come with their own terms for commercial use.

## Roles that use LoRA and QLoRA fine-tuning

* [LLM Engineer AI engineering](https://azendo.co/services/dedicated-ai-engineering-team/llm-engineer/)

## Service areas that use LoRA and QLoRA fine-tuning

* [AI engineering](https://azendo.co/services/dedicated-ai-engineering-team/)

Capacity is agreed across your whole team, with one monthly fee, not per skill.

## What it's used with

* [Hugging Face, a skill in our teams](https://azendo.co/skills/hugging-face/)
* [PyTorch, a skill in our teams](https://azendo.co/skills/pytorch/)
* [vLLM, a skill in our teams](https://azendo.co/skills/vllm/)
* [quantisation, a skill in our teams](https://azendo.co/skills/quantisation/)
* [CUDA, a skill in our teams](https://azendo.co/skills/cuda/)
* [MLflow, a skill in our teams](https://azendo.co/skills/mlflow/)
* [Weights & Biases, a skill in our teams](https://azendo.co/skills/weights-and-biases/)
* [Prompt evaluation, a skill in our teams](https://azendo.co/skills/prompt-evaluation/)
* [RAG, a skill in our teams](https://azendo.co/skills/rag/)

## Related in ai and machine learning

* [TensorFlow, a skill in our teams](https://azendo.co/skills/tensorflow/)
* [scikit-learn, a skill in our teams](https://azendo.co/skills/scikit-learn/)
* [Pandas, a skill in our teams](https://azendo.co/skills/pandas/)
* [NumPy, a skill in our teams](https://azendo.co/skills/numpy/)
* [embeddings, a skill in our teams](https://azendo.co/skills/embeddings/)
* [function calling, a skill in our teams](https://azendo.co/skills/function-calling/)
* [Kubeflow, a skill in our teams](https://azendo.co/skills/kubeflow/)
* [OpenAI, a skill in our teams](https://azendo.co/skills/openai/)

## Tell us what you need LoRA and QLoRA fine-tuning for.

A service delivery manager will reply with the specialists we'd suggest, the monthly capacity and what the first month looks like.

[Browse all skills](https://azendo.co/skills/)