AI models contain billions of parameters and are trained on trillions of data bytes before they can be operational. Training a model from scratch is expensive and incredibly time-consuming. Hence, most teams prefer customizing pre-trained models for their AI applications. Fine-tuning is one approach to customization that makes an already powerful AI smarter and more aligned with your specific needs.

Imagine a factory installing a new robotic arm. It arrives pre-programmed with general movement patterns but requires calibration to handle your specific assembly line, product sizes and materials. Fine-tuning an AI model is similar — you extend its capabilities so it performs optimally for your use case.

This article presents a seven-stage fine-tuning pipeline you can implement for any model.

What is AI model fine-tuning?What is AI model fine-tuning?

Fine-tuning is the process of using smaller, specific datasets to refine pre-trained AI model capabilities and improve their performance in a particular domain. For example, you fine-tune a large language model (LLM) with your organization’s human resource policy documents so it can power an internal chatbot that responds to employees’ HR queries.

Fine-tuning becomes necessary due to the following inherent limitations of AI models:

• Knowledge cutoff: AI models cannot access the latest information past their initial training date. For example, a model trained in 2024 does not have access to information about events that occur in 2025.

• Hallucinations: AI models may present false or made-up outputs in response to complex inquiries. Fine-tuning helps address knowledge gaps.

• Bias: AI models may be biased by their initial training data. Fine-tuning reduces bias by presenting more balanced datasets to the model.

You can use fine-tuning to improve and control LLM output for your use cases.

Fine-tuning in action. Source

Fine-tuning vs. training: What’s the difference?Fine-tuning vs. training: What’s the difference?

AI model training means building it from scratch using a very large dataset. The model learns patterns by adjusting parameters through multiple iterations. The process requires substantial computational resources and can take several months, if not years, before the model performs to the expected standard.

In contrast, fine-tuning takes a pre-trained model and trains it further on a smaller, task-specific dataset. It is faster, more cost-effective and ideal for cases with limited data.

Key differences are given below:

Why is fine-tuning important?Why is fine-tuning important?

Fine-tuning offers a strategic advantage by balancing performance, efficiency and cost-effectiveness. Organizations can customize AI models to meet unique business needs without the resource demands of training from scratch. This approach drives innovation across industries.

Data efficiencyData efficiency

Fine-tuning allows models to deliver high performance using smaller, domain-specific datasets. Instead of requiring vast amounts of general data, fine-tuned models learn from curated industry datasets, reducing training time and computational costs.

Improving performanceImproving performance

Fine-tuning enhances model accuracy by enabling AI systems to specialize in domain-specific tasks. Refinement ensures that models generate more relevant predictions, improving decision-making across industries.

AI model fine-tuning techniquesAI model fine-tuning techniques

Instruction fine-tuningInstruction fine-tuning

Instruction fine-tuning trains the model with examples explicitly showing how it should respond to different queries. For instance, if you want to improve your model’s summarization skills, your dataset should contain examples of passages and their corresponding summaries. Or, if you’re working on translation, your dataset might include input-output pairs of uncommon phrases.

Full fine-tuningFull fine-tuning

Instruction fine-tuning can be taken further with full fine-tuning, where all of the model’s weights are updated. This results in a brand-new version of the model but demands significant memory and computational power. Every new model version you create needs the same storage capacity as the original model, so storage costs add up quickly. Full fine-tuning can also result in catastrophic forgetting — a situation where the AI model performs well for the task at hand but not on other tasks it was originally good at.

Parameter-efficient fine-tuningParameter-efficient fine-tuning

Instead of updating every single weight, PEFT modifies only a small subset of parameters. It freezes most of the model’s parameters to drastically reduce memory requirements. PEFT techniques like LoRA (Low-Rank Adaptation) can cut down trainable parameters thousands of times, making fine-tuning feasible even on limited hardware.

Transfer learningTransfer learning

Transfer learning allows you to take a model already trained on massive general-purpose datasets and adapt it to a specialized domain. It is useful when you have limited task-specific data that is insufficient for training a brand-new model. For example, a general-purpose AI model can be fine-tuned with medical texts to specialize in healthcare applications.

Sequential fine-tuningSequential fine-tuning

Sequential fine-tuning gradually adapts a model to increasingly specialized tasks. For example, a general AI model could be fine-tuned for medical terminology and refined for pediatric cardiology. This structured approach helps retain knowledge while progressively improving task performance.

Multi-task learningMulti-task learning

Multi-task fine-tuning trains the AI model on datasets containing instructions for various tasks over multiple training cycles. The model learns to balance these different objectives without forgetting previous tasks.

How to fine-tune an AI modelHow to fine-tune an AI model

You can implement any fine-tuning techniques mentioned above as a seven-stage pipeline that moves your custom data to the AI model. The actual fine-tuning occurs in stage 4, but several steps are necessary before and after for project success.

Seven-stage fine-tuning pipeline. Source

Stage 1 — Data preparationStage 1 — Data preparation

This stage involves preparing the data that you will use to fine-tune the model. This stage involves data collection and preprocessing. Data can come from various sources, including:

• Structured datasets (e.g., SQL databases, CSV files)
• Unstructured text (e.g., web pages, PDFs, raw text files)
• Cloud storage

Each data source requires different tools for extraction. Once collected, raw data must be cleaned and formatted before use. This step includes removing noise, handling missing values, standardizing formats, etc. — typical to usual ML workflows.

A well-prepared dataset leads to a faster fine-tuning process, better model performance and fewer errors down the line. Resources like HelpSteer, H2O LLM Studio and No_Robots provide curated datasets across various domains.

Stage 2 — Model initializationStage 2 — Model initialization

Model initialization involves setting up the computational environment, selecting a pre-trained model and loading it into memory. The following table outlines the key steps in model initialization. Perform them sequentially.

Proper initialization minimizes technical issues and helps maintain stable training performance.

Stage 3 — Training setupStage 3 — Training setup

This stage involves setting up hardware or a cloud environment, defining hyperparameters, selecting optimization methods and initializing optimizers and loss functions. Proper environment configuration ensures efficient use of computational resources and optimizes training speed.

Set up the training environment

Configure a high-performance environment with GPUs or TPUs to accelerate training. You can reduce the complexity of this step by using a full-stack solution like the Nebius AI Cloud platform to run your fine-tuning workloads. You get all the required networking and orchestration technologies over a solid foundation of cutting-edge GPU hardware at incredibly low costs.

Define hyperparameters

Three key hyperparameters in fine-tuning are:

• Learning rate determines how quickly the model updates its parameters.
• Batch size defines the number of samples processed before updating the model’s parameters.
• Epochs refer to the number of passes through the training dataset.

Properly tuning these hyperparameters ensures that the model learns effectively without consuming unnecessary resources. Several tuning techniques are commonly used, such as random search, grid search and Bayesian optimization. You can learn more about them here.

Initialize optimizers and loss functions

Once hyperparameters are set, the next step is to select an optimizer and loss function.

Optimizers adjust the model’s parameters during training. Common choices include Adam (adaptive and efficient), SGD (stochastic gradient descent, helpful for large datasets) and RMSprop (suitable for handling non-stationary data).

Loss functions measure the model’s performance. Cross-entropy is often used for classification tasks, while mean squared error (MSE) is more appropriate for regression tasks.

Properly selecting optimizers and loss functions ensures the model converges efficiently and learns meaningful patterns from the dataset.

Stage 4 — Fine-tune the modelStage 4 — Fine-tune the model

Now, you can choose and implement your fine-tuning strategy from among the ones described in the previous section. Set up the training loop with the prepared dataset. A typical training loop includes:

1. Batching and loading data prepared in stage 1.
2. Measuring the model’s performance using the loss function identified in stage 3.
3. Adjusting model weights based on gradients.

Implementing PEFT techniques ensures that only relevant parameters are updated, reducing unnecessary computations. Strategies such as learning rate scheduling and early stopping optimize training.

If implementing multi-task learning, consider attaching specialized adapter models for different tasks while keeping the base model unchanged. The base model output goes to the adapter model before being passed to the end user. You can also use a collection of subnetworks where different “expert neural networks” handle different tasks dynamically.

Stage 5 — Evaluation and validationStage 5 — Evaluation and validation

Once the fine-tuning process is complete, assess the model’s performance on unseen data to ensure it generalizes well and meets the specified objectives. Evaluation typically involves calculating performance metrics like cross-entropy or accuracy to determine how well the model predicts outputs.

During validation, the model’s loss curves are closely monitored to detect signs of overfitting or underfitting. Overfitting may indicate that the model has become too specialized to the training data, while underfitting suggests that the model hasn’t learned enough from the data. Based on these insights, fine-tuning adjustments can be made to optimize the model for its intended task.

Stage 6 — DeploymentStage 6 — Deployment

Deployment is the stage where the fine-tuned model is made operational and integrated into real-world applications. You must set up your production environment and necessary integration points, such as linking the model with existing APIs or systems. During this phase, security is also a top priority — implement encryption and access control to protect data.

Stage 7 — Monitoring and maintenanceStage 7 — Monitoring and maintenance

More than a stage, this is an ongoing process throughout the model lifecycle. Continuously track the model’s performance, such as its accuracy and response times, to identify potential issues before they escalate. Updates become necessary if new data becomes available or if external conditions change. At this point, you may have to resume the fine-tuning process from:

• Stage 1 if data changes
• Stage 2 if model changes
• Stage 3 if environment changes
• Stage 4 if user requirements/expectations change

When to use fine-tuningWhen to use fine-tuning

Consider using fine-tuning in the following scenarios.

Industry-specific applicationsIndustry-specific applications

You can fine-tune your AI models using industry or domain-specific data. For example, you can fine-tune them on proprietary medical data to improve diagnosis and treatment plan generation. Some examples of applications in various industries:

• Healthcare: Fine-tune a general AI model with hospital-specific patient records to improve disease diagnosis. This approach minimizes data requirements while enhancing diagnostic accuracy.

• Finance: Fine-tune a language model on financial reports and regulatory filings to provide more accurate risk assessments and fraud detection, even with limited proprietary data.

• Media and entertainment: Fine-tune a general LLM to specific film studio scripts so that AI-generated content aligns with the brand’s tone and storytelling style.

Predetermined tasksPredetermined tasks

Sometimes, you may want to leverage the general language understanding of AI models for more traditional ML tasks — for example, sentiment analysis, fraud classification, etc. You can fine-tune the model so it performs better for such predetermined tasks.

Sensitive dataSensitive data

Heavily regulated use cases may limit your ability to access third-party AI models via APIs. In this case, you can fine-tune a model on your private infrastructure and limit data flow outside your network.

Continuous learningContinuous learning

Fine-tuning supports use cases where the model needs access to continuously changing data, such as real-time data streams. Thus, you can periodically refresh your model knowledge without starting from scratch.

Develop innovation through fine-tuning with NebiusDevelop innovation through fine-tuning with Nebius

Nebius provides comprehensive cloud infrastructure for running on-demand fine-tuning jobs of any size. It includes all the infrastructure necessary for data preparation, initialization, setup, deployment and monitoring. You can transform raw data into best-in-class datasets, track data lineage and model versioning for fine-tuning experiments directly in Nebius AI Cloud.

Get your MLOps/AIOps pipeline up and running in just a few clicks and scale workloads up or down as needed. Nebius AI Cloud can provide enough compute, storage and network capacity for tens to millions of tokens.

Get started with AI model fine-tuning on Nebius by creating a free account today.