LLM Fine-Tuning Explained

LLM Fine-tuning means adapting pre-trained general-purpose AI models to excel at specific tasks or domains. This is done by training the models on smaller datasets of <input, output> pairs that are examples of the desired behavior or output.

By updating the base model’s parameters during this process, fine-tuning closes the gap between the broad capabilities of general-purpose pre-trained AI models and the specific needs of the application. So the model performs much better and is more in line with human expectations for the task.

Fine-tuning, on the other hand, allows for further customization by adding new knowledge to the base model itself so that it learns – or adapts its learned knowledge – to master specific tasks. It is a supervised machine learning task based on labeled datasets to update the model’s weights. The demonstration datasets are usually prompt-response pairs that indicate the kind of refined knowledge needed for a particular task.

Fine-Tuning for Domain-Specific LLM

In cases where an out-of-the-box model is missing knowledge of domain or organization-specific terminology, a custom fine-tuned model, also called a domain-specific LLM might be an option for performing standard tasks in that domain or micro-domain.

BloombergGPT and Med-PaLM 2 are examples of LLMs that were trained from scratch to have a better understanding of highly specialized vocabulary found in financial and medical texts, respectively. In the same sense, domain-specificity can be addressed through fine-tuning at a smaller scale.

Fine-tuning is finally effective when cost or latency needs to be reduced during inference. A fine-tuned model may be able to achieve high-quality results in specific tasks with shorter instruction prompts. However, you have to be aware that interpreting or debugging the predictions of a fine-tuned model is not trivial. Different factors like data quality, data ordering, and the model’s hyperparameters may impact the performance.

Fine-tuning heavily relies on accurate, targeted labeled data. Before fine-tuning a model, one should first make sure that enough representative data is available so that the model does not overfit on limited data. Overfitting describes a model’s limited ability to generalize to new data.

While the concept of LLM fine-tuning is broad, the methodology employed is highly dependent on the model size, computational budget, and the specific task. Modern fine-tuning emphasizes Parameter-Efficient Fine-Tuning (PEFT), a breakthrough that makes the process accessible to enterprises.

Parameter-Efficient Fine-Tuning (PEFT)

PEFT is an umbrella term for methods that selectively modify only a small fraction of a pre-trained LLM’s parameters, drastically reducing computational and memory requirements compared to full-model tuning. This approach preserves the model’s vast, general knowledge while efficiently learning new, specific skills.

• LoRA (Low-Rank Adaptation): LoRA is the most popular PEFT method. It freezes the original model weights and injects small, trainable matrices (known as adapters) into each layer of the Transformer architecture. This allows for training with significantly less VRAM and faster convergence, making it practical for enterprise use cases where resource optimization is paramount.
• QLoRA (Quantized LoRA): An extension of LoRA, QLoRA further reduces memory usage by quantizing the pre-trained model to 4-bit precision while still using LoRA adapters for training. This technique enables the fine-tuning of multi-billion parameter models on single, less-expensive GPUs, democratizing access to state-of-the-art specialization.

Full Fine-Tuning (Supervised Fine-Tuning)

This traditional method updates all of the model’s weights on the task-specific dataset.

• When to use it: Full fine-tuning is typically reserved for cases where the target domain is drastically different from the pre-training data, or when the base model is smaller, justifying the higher computational cost for a deep, comprehensive adaptation.

Instruction Fine-Tuning and Alignment

This process trains the model on a dataset of high-quality (instruction, desired response) pairs to make the model better at following natural language commands, which is critical for conversational AI products.

• RLHF (Reinforcement Learning from Human Feedback): While not strictly a fine-tuning method, RLHF is the final step in model alignment. It trains a reward model based on human preferences for model outputs, which is then used to further fine-tune the LLM to be more helpful, harmless, and aligned with human values—a necessary process for operationalizing LLMs in a customer-facing enterprise environment.

In order to do so, one has to understand the exact use case that needs to be addressed and decide on the best way to ground the model’s responses to match the business expectations reliably. There are various ways to give context to a general-purpose generative model. Fine-tuning and RAG (Retrieval Augmented Generation) are two popular approaches.

Dataset preparation is a costly process. Automating parts of this process is a vital step towards offering a scalable solution for fine-tuning LLMs for enterprise use cases. Here’s an example: Suppose you want to customize a foundation model to be able to generate social media posts following your company’s marketing strategy and tone.

As an organization, you might already have a sufficient record of such posts, which you can use as golden outputs. These outputs form a Knowledge Base from which you can generate key content points using RAG. The generated content points paired with the corresponding outputs can form your dataset to fine-tune the model to master this new skill.

Fine-tuning and RAG are not mutually exclusive. On the contrary, combining both in a hybrid approach may improve the model’s accuracy, and it is definitely a direction worth investigating.

A recent study by Microsoft shows how capturing geographic-specific knowledge in an agricultural dataset generated using RAG resulted in a significant increase in the accuracy of the model that was fine-tuned on that dataset.

Making fine-tuning LLMs less of a black box and more accessible to the enterprise involves making each step in the workflow as simple and transparent as possible. A high-level workflow involves the following steps:

1. Experimenting with prompting different LLMs and selecting a baseline model that fits one’s needs.
2. Defining a precise use case for which a fine-tuned model is needed.
3. Applying automation techniques to the data preparation and fine-tuning process.
4. Training a model, preferably with some default values for the model’s hyperparameters.
5. Evaluating and comparing different fine-tuned models against a number of metrics.
6. Customizing the values for the model’s hyperparameters based on feedback from the evaluation step.
7. Testing the adapted AI model before deciding that it’s good enough to be used in actual applications.

There are several key factors that need to be considered before deciding on how to adapt a generic model to specific business needs. Fine-tuning usually comes into play when instructing the model to perform a specific task fails or does not produce the desired outputs consistently. Experimenting with prompts and setting the baseline for the Small Language Models performance is a first step toward understanding the problem or task.

While both LLM Fine-Tuning and Retrieval Augmented Generation (RAG) are used to customize an LLM for enterprise use but they address different limitations of the base model. To get the highest accuracy, especially in domain specific use cases a hybrid approach is often the gold standard.

The Synergy of Fine-Tuning and RAG

Fine-tuning and RAG are not competitive, they are complementary. A powerful hybrid approach is to use fine-tuning to perfect the LLM’s underlying skill (ability to understand complex instructions, maintain brand voice or structure a technical output) and then use RAG to ground that output in current, verified, external facts.

For example, Aisera uses this hybrid model: an LLM is fine-tuned on a company’s conversational data to master their specific customer support style (the skill). Then at runtime, RAG retrieves the latest product specs or return policies from the company’s knowledge base (the facts. This combination ensures the response is both accurate (due to RAG) and on-brand (due to fine-tuning) to get the reliability required for enterprise-grade generative AI.

Leveraging a full-stack Generative AI platform to address the workflow above essentially means connecting the dots between:

• Prompt engineering and evaluating prompts for different models;
• Using knowledge retrieval to generate demonstration data;
• Fine-tuning a model with generated data.

This is the future for operationalizing LLMs for the enterprise and reducing time, effort, and cost for maximizing their adaptability to specific skills. Book an AI demo with Aisera to find out more about our Generative AI capabilities.