Top 10 LLM Fine-Tuning Methods for Cutting Inference Costs

Large language models (LLMs) can be handy tools. But the challenge of fine-tuning them for specific tasks can be daunting, especially as they can be enormous, unwieldy, and computationally expensive. LLM fine-tuning methods can help address this challenge. This article will introduce you to various approaches to fine-tuning large language models so you can reduce their computational demands and deployment costs without sacrificing performance. This way, you’ll be able to scale AI efficiently and affordably with machine learning frameworks.

One solution that can help you achieve your goals is Inference’s AI inference APIs. These tools can help you streamline your processes and improve your bottom line as you fine-tune large language models.

What Does It Mean to Fine-Tune LLMs?

Fine-tuning LLMs is taking a pre-trained language model and training it on a specific dataset. This process customizes the model for particular tasks or domains. Fine-tuning saves time and resources by allowing organizations to adapt existing models instead of training them from scratch. Imagine you’re a talented novelist who writes in English. You’ve been asked to write a technical manual for new software. Although your writing skills are excellent, to complete the task, you need:

• Specialized knowledge of the software
• Industry-specific terminology

Targeted Adaptation

This specialized training, or fine-tuning, ensures that your writing meets the technical manual's specific needs, just as fine-tuning an LLM helps it excel in particular tasks or domains

.Fine-tuning large language models (LLMs) involves adjusting pre-trained models on specific datasets to enhance performance for particular tasks. This process begins after general training ends.

Data Adaptation

Users provide the model with a more focused dataset, which may include industry-specific terminology or task-focused interactions, to help the model generate more relevant responses for a specific use case.

Fine-tuning allows the model to adapt its preexisting weights and biases to fit specific problems better. This improves output accuracy and relevance, making LLMs more effective in practical, specialized applications than their broadly trained counterparts.

Efficient Adaptation

While fine-tuning can be highly computationally intensive, new techniques like Parameter-Efficient Fine-Tuning PEFT make running even on consumer hardware much more efficient.

Fine-tuning can be performed on open-source LLMs, such as Meta LLaMA and Mistral models, and on some commercial LLMs if the model’s developer offers this capability. OpenAI allows fine-tuning for:

• GPT-3.5
• GPT-4

What is the Difference Between LLM Pre-Training and LLM Fine-Tuning?

Pre-Training

Pre-training involves training a language model on a large corpus of general text data to learn:

• Language patterns
• Grammar
• General knowledge

This process creates a broad, versatile model capable of understanding and generating human-like text.

Fine-Tuning

Fine-tuning LLM adjusts this pre-trained model using specific, domain-related data to improve performance on specialized tasks. This process tailors the model to understand and generate text specific to a particular field or application.

Why Fine-Tune LLMs?

Fine-tuned LLMs provide a range of business benefits that help organizations achieve their unique goals.

Specificity and Relevance

A fine-tuned model excels in providing highly specific and relevant outputs tailored to your business’s unique needs. Unlike general models, which offer broad responses, fine-tuning adapts the model to understand industry-specific:

• Terminology
• Nuances

This can be particularly beneficial for specialized industries where precise language and contextual understanding are crucial, such as:

• Legal
• Medical
• Technical fields

Improved Accuracy

Fine-tuning significantly enhances the accuracy of a language model by allowing it to adapt to your business data’s:

• Specific patterns
• Requirements

When a model is fine-tuned, it learns from a curated dataset that mirrors the particular tasks and language your business encounters. This focused learning process refines the model’s ability to generate precise and contextually appropriate responses, reducing errors and increasing the reliability of the outputs.

Data Privacy and Security

In many industries, maintaining data privacy and security is paramount. By fine-tuning a language model on proprietary or sensitive data, businesses can ensure that their unique datasets are not exposed to third-party risks associated with general model training environments.

Fine-tuning can be conducted on-premises or within secure environments, keeping data control in-house.

Customized Interactions

Businesses that require highly personalized customer interactions can significantly benefit from fine-tuned models. These models can be trained to understand and respond to customer queries with a level of customization that aligns with the brand’s:

• Voice
• Customer service protocols

A fine-tuned model in a retail business can more effectively understand product-specific inquiries:

• Offer personalized recommendations
• Understand company policies
• Handle complex service issues than a general model

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Top 10 LLM Fine-Tuning Methods

Several fine-tuning methods and techniques adjust the model parameters to a given requirement. Broadly, we can classify these methods into two categories:

• Supervised fine-tuning
• Reinforcement learning from human feedback (RLHF)

Supervised Fine-Tuning

In this method, the model is trained on a task-specific labeled dataset, where each input data point is associated with a correct answer or label. The model learns to adjust its parameters to predict these labels as accurately as possible. This process guides the model to apply its pre-existing knowledge, gained from pre-training on a large dataset, to the specific task at hand.

Supervised fine-tuning can significantly improve the model's performance on the task, making it an effective and efficient method for customizing LLMs.

The most common supervised fine-tuning techniques are:

1. Basic Hyperparameter Tuning

Basic hyperparameter tuning is a simple approach that manually adjusts the model hyperparameters until you achieve the desired performance, such as:

• The learning rate
• Batch size
• The number of epochs

The goal is to find the set of hyperparameters that allows the model to learn most effectively from the data, balancing the trade-off between learning speed and the risk of overfitting. Optimal hyperparameters can enhance the model's performance on the specific task.

2. Transfer Learning

Transfer learning is a powerful technique beneficial when dealing with limited task-specific data. This approach uses a model pre-trained on a large, general dataset as a starting point.

The model is then fine-tuned on the task-specific data, allowing it to adapt its pre-existing knowledge to the new task. Compared to training a model from scratch, this process:

• Reduces the amount of data and training time required
• Often leads to superior performance

3. Multi-Task Learning

In multi-task learning, the model is fine-tuned on multiple related tasks simultaneously. The idea is to leverage the commonalities and differences across these tasks to improve the model's performance.

The model can develop a more robust and generalized understanding of the data by learning to perform multiple tasks simultaneously.

Shared Learning

This approach leads to improved performance, especially when the tasks it will perform are closely related or when there is limited data for individual tasks. Multi-task learning requires a labeled dataset for each task, making it an inherent component of supervised fine-tuning.

4. Few-Shot Learning

Few-shot learning enables a model to adapt to a new task with little task-specific data. The idea is to leverage the vast knowledge model already gained from pre-training to learn effectively from just a few examples of the new task. This approach is beneficial when the task-specific labeled data is scarce or expensive.

In this technique, the model is given a few examples or shots during inference time to learn a new task. The idea behind few-shot learning is to guide the model's predictions by providing context and examples directly in the prompt.

Guided Adaptation

Few-shot learning can also be integrated into the reinforcement learning from human feedback (RLHF) approach if the small amount of task-specific data includes human feedback that guides the model's learning process.

5. Task-Specific Fine-Tuning

This method allows the model to adapt its parameters to the nuances and requirements of the targeted task, enhancing its performance and relevance to that particular domain.

Task-specific fine-tuning is particularly valuable when optimizing the model's performance for a single, well-defined task, ensuring that the model generates task-specific content with:

• Precision
• Accuracy

Targeted Adaptation

Task-specific fine-tuning is closely related to transfer learning. Still, transfer learning is more about leveraging the general features learned by the model, while task-specific fine-tuning is about adapting the model to the specific requirements of the new task.

Reinforcement Learning From Human Feedback (RLHF)

Reinforcement learning from human feedback (RLHF) is an innovative approach that involves training language models through interactions with human feedback. By incorporating human input into the learning process, RLHF facilitates the continuous enhancement of language models so they:

• Produce more accurate
• Contextually appropriate responses

This approach leverages the expertise of human evaluators and enables the model to adapt and evolve based on real-world feedback, ultimately leading to more effective and refined capabilities. The most common RLHF techniques are:

6. Reward Modeling

In this technique, the model generates several possible outputs or actions, and human evaluators rank or rate these outputs based on their quality. The model then learns to predict these human-provided rewards and adjusts its behavior to maximize the predicted rewards.

Reward modeling provides a practical way to incorporate human judgment into the learning process, allowing the model to learn complex tasks that are difficult to define with a simple function. This method enables the model to learn and adapt based on human-provided incentives, ultimately enhancing its capabilities.

7. Proximal Policy Optimization

Proximal policy optimization (PPO) is an iterative algorithm that updates the language model's policy to maximize the expected reward. The core idea of PPO is to take actions that improve the policy while ensuring the changes are not too drastic from the previous policy.

This balance is achieved by introducing a constraint on the policy update that prevents harmful large updates while allowing beneficial minor updates.

Stable Optimization

This constraint is enforced by introducing a surrogate objective function with a clipped probability ratio that serves as a constraint. This approach makes the algorithm more stable and efficient than other reinforcement learning methods.

8. Comparative Ranking

Comparative ranking is similar to reward modeling, but in comparative ranking, the model learns from relative rankings of multiple outputs provided by human evaluators, focusing more on comparing different outputs.

In this approach, the model generates multiple outputs or actions, and human evaluators rank these outputs based on their:

• Quality
• Appropriateness

Ranked Outputs

The model then learns to adjust its behavior to produce outputs that are ranked higher by the evaluators.

Comparative ranking provides more nuanced and relative feedback to the model by comparing and ranking multiple outputs rather than evaluating each production in isolation. This method helps the model understand the task subtleties, improving results.

9. Preference Learning (Reinforcement Learning With Preference Feedback)

Preference learning, or reinforcement learning with preference feedback, focuses on training models to learn from human input in the form of preferences between:

• States
• Actions
• Trajectories

The model generates multiple outputs in this approach, and human evaluators indicate their preference between pairs of outputs.

Preference Alignment

The model then learns to adjust its behavior to produce outputs that align with the human evaluators' preferences. This method is useful when it is difficult to quantify the output quality with a numerical reward but easier to express a preference between two outputs.

Preference learning allows the model to learn complex tasks based on nuanced human judgment, making it an effective technique for fine-tuning the model on real-life applications.

10. Parameter-Efficient Fine-Tuning

Parameter-efficient fine-tuning (PEFT) is a technique for improving the performance of pre-trained LLMs on specific downstream tasks while minimizing the number of trainable parameters. It offers a more efficient approach by updating only a minor fraction of the model parameters during fine-tuning.

PEFT selectively modifies only a small subset of the LLM's parameters, by adding new layers or modifying existing ones in a task-specific manner. This approach reduces the computational and storage requirements while maintaining comparable performance to full fine-tuning.

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Steps to Fine-Tune an LLM

Fine-tuning an LLM is not a one-size-fits-all process. It requires careful planning and optimization to achieve the best results. Several factors influence the fine-tuning process:

• Efficiency
• Stability
• Success

Below are two key considerations that impact training time and performance:

Duration of Fine-Tuning

The time required to fine-tune an LLM varies based on:

• Dataset size
• Model complexity
• Computational resources
• The chosen learning rate

Using Low-Rank Adaptation (LoRA), a 13-billion-parameter model was fine-tuned in approximately 5 hours on a single A100 GPU. Fine-tuning larger models or using full fine-tuning methods without parameter-efficient techniques can extend the process to several days or weeks, depending on the available computational resources.

Learning Rate Selection

Choosing an appropriate learning rate is crucial. A high learning rate can lead to:

• Unstable training
• Convergence issues

Low learning rate may slow down training and result in suboptimal performance. Experimenting with different learning rates or using techniques like learning rate scheduling can help find the optimal value. By carefully considering these factors, organizations can:

• Optimize fine-tuning efficiency
• Reduce costs
• Improve model accuracy.

Laying the Groundwork: Preparing Your Data for LLM Fine-Tuning

Data preparation involves curating and preprocessing the dataset to ensure its relevance and quality for the specific task. This may include tasks such as:

• Cleaning the data
• Handling missing values
• Formatting the text to align with the model's input requirements

Data augmentation techniques can be employed to:

• Expand the training dataset
• Improve the model's robustness

Data Importance

Proper data preparation is essential for fine-tuning, as it directly impacts the model's ability to learn and generalize effectively. This ultimately leads to improved performance and accuracy in generating task-specific outputs.

Picking the Right Pre-Trained Model

It’s crucial to select a pre-trained model that aligns with the specific requirements of the:

• Target task
• Domain

Understanding the architecture, input/output specifications, and layers of the pre-trained model is essential for seamless integration into the fine-tuning workflow. Factors should be considered when making this choice. These include:

• The model size
• Training data
• Performance on relevant tasks

By selecting a pre-trained model that closely matches the characteristics of the target task, you can streamline the fine-tuning process and maximize the model's adaptability and effectiveness for the intended application.

Configuring Your Fine-Tuning Parameters for Success

Configuring the fine-tuning parameters is crucial for achieving optimal performance in the fine-tuning process. Parameters play a significant role in determining how the model adapts to the new task-specific data. These include:

• The learning rate
• Number of training epochs
• Batch size

Freezing specific layers (the earlier ones) while training the final layers is a common practice to prevent overfitting. By freezing early layers, the model retains the general knowledge gained during pre-training while allowing the final layers to adapt specifically to the new task.

Balanced Adaptation

This approach helps maintain the model's ability to generalize while effectively learning task-specific features, striking a balance between:

• Leveraging pre-existing knowledge
• Adapting to the new task

Evaluating Your Model Performance with Validation

Validation involves evaluating a fine-tuned model’s performance using a validation set. Monitoring metrics provide insights into the model's:

• Effectiveness
• Generalization capabilities

These metrics include:

• Accuracy
• Loss
• Precision
• Recall

Performance Evaluation

By assessing these metrics, you can gauge how well the fine-tuned model:

• Performs on the task-specific data
• Identify potential areas for improvement

This validation process allows for the refinement of fine-tuning parameters and model architecture, ultimately leading to an optimized model that generates accurate outputs for the intended application.

Iterating Your Model for Optimal Performance

Model iteration allows you to refine the model based on evaluation results. Upon assessing the model's performance, adjustments to fine-tuning parameters can be made to enhance the model's effectiveness. These parameters include:

• Learning rate
• Batch size
• The extent of layer freezing

Exploring different strategies, such as employing regularization techniques or adjusting the model architecture, enables you to improve the model's performance iteratively. This empowers engineers to fine-tune the model in a targeted manner, gradually refining its capabilities until the desired level of performance is achieved.

Transitioning Your Fine-Tuned Model to Production

Model deployment marks the transition from development to practical application, and it involves the integration of the fine-tuned model into the specific environment. This process encompasses considerations such as:

• The hardware and software requirements of the deployment environment
• Model integration into existing systems or applications

Aspects must be addressed to ensure a seamless and reliable deployment. These include:

• Scalability
• Real-time performance
• Security measures

Real-world Deployment

By successfully deploying the fine-tuned model into the specific environment, you can leverage its enhanced capabilities to address real-world challenges.

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Start building with $10 in free API credits and experience state-of-the-art language models that balance cost-efficiency with high performance.

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