A large language model (LLM) doesn't simply appear fully developed—it requires a collaborative effort from developers, vast datasets, and advanced technologies to bring it to life, especially for enterprise-level applications.

Model fine-tuning is just one critical part of this larger ecosystem, helping to tailor the model for specific tasks and industries within the broader development process.

LLMs undergo a pre-training phase in which they’re exposed to extensive datasets, allowing them to learn how to predict the best next word in a sentence and grasp the statistical relationships between words and phrases.

Usually, this dataset encompasses a significant portion of the internet, forming the basis of the foundational model. This pre-training equips the model to learn from and generalize across a vast array of data, enabling it to identify and capture underlying patterns and relationships effectively.

Once models have achieved a grasp of grammar, syntax, and even contextual understanding from a wide range of human-written content, they can be moved on to the next step. It’s time to make them more than just generic LLMs with predictability engines. It’s time to start fine-tuning models.

What is fine-tuning?

Fine-tuning, meaning the process of adjusting a pre-trained LLM to better suit a specific task, dataset, or use case, helps your model learn to adapt to nuances and context.

By showing the model what “good” looks like for its particular use case, fine-tuning helps to create greater accuracy, develop more accurate domain-specific knowledge, meet higher standards, and work towards mitigating hallucinations.

Fine-tuning vs. training

When we talk about ‘training’ AI, we’re referring to the process of teaching a large language model (LLM) from scratch by feeding it huge amounts of unlabelled data so it learns to detect patterns.

During training, an LLM develops its core ability to predict the next word in a sentence, allowing it to analyze and respond better to natural-language prompts.

What is AI fine-tuning, then? It’s a more specific process. It involves taking a pre-trained model and adjusting it to make it better suited to a specific task. An LLM that has been pre-trained on many types of data will be good at predicting the next word in a sentence and even producing sensible text, but without fine-tuning, the produced text is unlikely to be exactly what you want as a user. Fine-tuning can be used to incorporate domain-specific knowledge, such as for the financial or health sector, and to optimize the model for a specific use case, such as customer support or language translation.

Businesses that need an LLM to meet their specific or niche requirements can acquire a pre-trained model and fine-tune it to their needs. This can be a way to save time, money, and effort.

Why do you need fine-tuning?

Fine-tuning plays a critical role in maximizing the performance of LLMs in real-world applications. It helps an LLM to understand and complete specific tasks. This could be a simple use case, such as answering user queries or code completion, or improve the model's understanding of more complex techniques, such as retrieval-augmented generation.

By undergoing AI fine-tuning, the model’s ability to perform these specialized tasks improves. Fine-tuning can deliver time and resource efficiencies, adapt a model to perform better in specific types of tasks, and mitigate bias.

The process of fine-tuning can also help the model to incorporate domain-specific language and proprietary knowledge from your enterprise and industry. No matter the type of early data it’s exposed to, a pre-trained foundational model will still lack domain-specific knowledge. This is where it can be beneficial to undertake fine-tuning, exposing the model to a narrower dataset related to the desired application.

Common fine-tuning use cases

Common use cases in which fine-tuning can improve results include:

1. Setting style, tone, format, or other qualitative aspects

2. Improving reliability for a specified generated output

3. Correcting failures to follow complex prompts

4. Handling edge cases in specific ways

5. Performing a new skill or task that’s hard to articulate in a prompt

Benefits of fine-tuning your LLM

Fine-tuning helps to boost the capabilities of your large language model for your use cases. It allows you to tailor the knowledge base to your specific needs.

Through fine-tuning a model, businesses can achieve higher accuracy and better results for specific tasks. The process leverages the existing knowledge encoded in the model and refines it to closer fit the specific needs of the enterprise.

In order to adapt to changing business requirements, the fine-tuning process can be repeated with new data and this is a key part of reinforcement learning discussed below. For example, an LLM for healthcare trained in the latest techniques of 2023 will not know about any new developments in 2024 or later. This kind of adaptability is essential in modern, dynamic business environments.

General fine-tuning allows you to specialize a pre-trained LLM to a specific domain of expertise to make it inherently more adept in that particular area, providing more accurate and informative responses.

Emerging trends in small language models (SLMs)

Due to their efficiency and scalability, small language models (SLMs) are gaining traction as viable alternatives to LLMs. Once trained and fine-tuned, SLMs can deliver results similar to, or even better than an LLM in some cases, but require fewer resources. This results in faster response times, and reduced operational costs. Unlike general LLMs, which can be cumbersome, SLMs focus on domain-specific tasks.

The smaller size of SLMs enhances interpretability, making them less prone to black-box criticism. Additionally, SLMs can be a good fit in edge computing scenarios, where limited hardware capacity excludes the use of a suitable LLM. SLMs can align better with privacy concerns, by enabling the processing of sensitive data locally. The growing demand for agility and eco-conscious AI amplifies their appeal, positioning SLMs as lean, adaptable solutions in evolving landscapes

Prominent fine-tuning methods

When you fine-tune an LLM, you’re effectively adjusting the parameters of a pre-trained model on a task-specific dataset to improve its performance on that task. This can be done in a variety of ways, depending on business needs.

Full fine-tuning

Full fine-turning is done by unfreezing the model - i.e., allowing all the model weights to be updated - and training with more data.

Reinforcement learning

Just as people learn by making mistakes and incrementally getting better, LLMs can also adjust their approaches to specific tasks based on feedback. This is in keeping with the fundamental nature of language models; starting with randomness and slowly discovering which settings get the desired results. The mechanics of reinforcement learning generally revolve around the human evaluation of responses, resulting in either a reward or penalty being issued. In effect, the model is learning using trial and error.

Parameter-efficient fine-tuning

Parameter-efficient fine-tuning is a set of techniques that optimize AI models by making adjustments to only a small subset of parameters while keeping most pre-trained weights fixed. Reducing the number of parameters vastly reduces the amount of work required when fine-tuning, reducing cost.

This approach is effective for adapting a pre-trained model to incorporate domain-specific knowledge, reducing computational costs without sacrificing model performance on specialized tasks.

Common mechanisms include low-rank adaptation (LoRA) updates and gradient-based parameter selection to minimize resource overheads and training time.

How does model fine-tuning work?

Understanding how fine-tuning works can help ensure your model fine-tuning efforts are effective and efficient. There are essentially two steps in the process: it begins with selecting a pre-trained model, after which you must prepare a dataset that caters to the task you wish to optimize the model for.

It’s important to note that LLMs can have varying requirements for input; you’ll need to make sure the format of the data you want to use matches the input demands of your selected model. This might require reformatting of your data, which can add time and effort to the process.

Fine-tuning process and best practices

The five essential steps for fine-tuning a large language model are:

Select a pre-trained model. Cohere offers fine-tuning capabilities for its three model families (Command, Embed, and Rerank).
Prepare your targeted dataset by preprocessing and formatting it to match the input requirements of your pre-trained LLM.
Fine-tune the model through your preferred process with adjusted hyper-parameters to prevent over-training, also known as over-fitting, when a model is too specialized to the training data and has trouble generalizing new, unseen data.
Evaluate and validate your results, ensuring the model generalizes well to unseen data and produces reliable predictions on real production data.
Establish a system of iterative refinement—there will likely be multiple rounds of experimentation, adjusting parameters, trying different architectures, and considering if more training data is needed.

Adhering to these best practices will help you undertake efficient fine-tuning. This in turn can set you up to achieve superior LLM results while keeping your costs down.

Fine-tuning: Things to consider

Whether you’ve built your own model from scratch or chosen a pre-trained foundational model, fine-tuning will still require high-quality data that is relevant to your specific use case and industry jargon.

Plus, fine-tuning never really ends; as new data becomes available, the model must be fine-tuned again. This is because the data training leaves the model with a knowledge cut-off date of the moment the training ends; there’s no access to real-time information, so the LLM’s knowledge can become out of date.

The fine-tuning process must begin again, yet each round of fine-tuning risks the model forgetting its previously acquired knowledge. Potentially the best way to mitigate the limitations of fine-tuning is to pair it with retrieval-augmented generation. This connects your model to your domain-specific data and knowledge without needing to specifically train it using that data. For example, RAG allows your model to fetch external information, whether domain-specific data, or results from a web search, grounding the generative response with citable sources outside the original training set. An additional benefit of this approach is that as enterprise data changes, the model won’t need to be retrained.

Fine-tuning applications

Fine-tuning LLMs is applicable to a wide range of industries. By undertaking AI fine-tuning techniques, organizations can customize their chosen LLMs to perform specific tasks more effectively. Here are some popular applications of fine-tuned LLMs.

1. Healthcare: Fine-tuning embedding models can be very effective for rapidly analyzing medical records or research documents. These models can effectively capture the semantic connections between words and concepts.

Adding hyper-relevant training data covering specific medical disciplines can allow these models to deliver more accurate diagnostics.

2. Finance and compliance: What is fine-tuning a model in finance? It means teaching a model to handle large financial datasets and understand essential compliance protocols.

This can result in faster and more precise document review and risk assessment capabilities (with precision being particularly important in a highly-regulated industry).

3. Customer service: Fine-tuning models can help to make them better at handling specific customer queries, following brand guidelines for tone, and offering elements of personalization within conversational settings.

Additionally, training a model on technical product documentation can allow it to provide effective technical support.

By fine-tuning an LLM for specialized applications, a business can leverage the power of a pre-trained model while ensuring it meets the required standard.

Customize your LLMs with fine-tuning

If you’d like to customize pre-trained LLMs to suit your specific task, dataset, or use case, fine-tuning is exactly what you need.

It’s a way to scale capabilities, create greater accuracy, or meet higher security or compliance needs.

Depending on the additional capabilities that you want your model to handle, there are many ways to approach fine-tuning. However, these approaches require large, high-quality datasets.

To find out more, check out our guide to fine-tuning, which provides a wealth of information about fine-tuning.

FAQs about fine-tuning

Still curious about fine-tuning? Here’s some answers to a few of the most commonly asked questions. Got more questions, get in touch.

What is AI fine-tuning?

Fine-tuning, meaning the adjustment of a pre-trained model is a key process for turning a foundational LLM into a niche tool that can effectively perform a specific task. Taking an existing model and tweaking it as needed offers a strong combination of economy and efficacy.

How do you prepare data for fine-tuning?

To prepare data for fine-tuning, ensure your dataset is high-quality, broad, and correctly labeled. Fine-tuning only works with well-prepared data. To make your models as effective as possible, make sure your training data meets the input requirements of the model and reformat it as needed.

What is an example of fine-tuning an LLM?

An example of fine-tuning an LLM is customizing a pre-trained model for ecommerce. This involves exposing an existing model to customer interaction data and customer purchase histories so it can learn how to field tricky queries and make customers feel valued.

When should you choose fine-tuning LLMs?

Fine-tuning LLMs is ideal when the use case requires domain-specific language and industry knowledge that a foundational LLM might not get quite right for performing the tasks required or when running a large parameter, general model may be overkill for niche, domain-specific use cases. A smaller, fine-tuned model is likely to result in better performance at a fraction of the cost. Whether you have to parse input data that your current systems aren’t familiar with, you need a level of accuracy you can’t find, or a range of knowledge no existing model possesses, fine-tuning can get you the results you’re looking for.

What is the difference between fine-tuning and transfer learning?

The difference between fine-tuning and transfer learning is that fine-tuning adjusts an existing model's parameters to improve performance for a specific task, while transfer learning can be used to adapt the capabilities of an existing model to make it suitable for another use case.

As an example, adapting a pre-trained LLM to be used for sentiment analysis could be considered transfer learning. The required format of the output in a sentiment analysis use case would be different, although it would still leverage the base capabilities of the model.

Transfer learning is economical when an existing model is already well-suited for a task that’s very similar to yours. You will only need to add enough training data to cover the differences. However, if you’re using a general-purpose model for a more niche task, you’ll need fine-tuning to achieve the desired level of accuracy.

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What is fine-tuning? A guide to fine-tuning LLMs