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

Related Reading

• Natural Language Processing Techniques

15 Fine-Tuning Methods for Cutting Inference Costs

1. Instruction fine-tuning

One strategy used to improve a model's performance on various tasks is fine-tuning the instructions. It involves training the machine learning model using examples that demonstrate how the model should respond to a query. The dataset you use for fine-tuning large language models has to serve the purpose of your instruction.

Crafting Effective Instruction-Based Datasets

For example, suppose you fine-tune your model to improve its summarization skills. In that case, you should build up a dataset of examples that begin with the instruction to summarize, followed by text or a similar phrase. In the case of translation, you should include instructions like “translate this text.”

These prompt completion pairs allow your model to "think" in a new, niche way and serve the given specific task.

2. Full fine-tuning

Artificial intelligence (AI) practitioners differentiate fine-tuning techniques based on the extent to which they modify the pre-trained model. Full-model tuning is an exhaustive fine-tuning method where developers adjust every layer of an LLM’s neural network to accommodate new training data.

This differs from parameter-efficient fine-tuning (PEFT), in which developers only adjust some layers and preserve most of the model’s original parameters.

Unlocking Deeper Behavioral Changes

Because full-model tuning trains more of the model, it enables more profound behavioral changes. As a result, it can deliver more accurate, context-rich, and reliable results for domain-intensive tasks. The tradeoff is cost. Due to the extent of training required, full-model tuning is computationally intensive, time-consuming, and costly compared to other methods.

Ideal Use Cases for Comprehensive Model Adaptation

It is also prone to catastrophic forgetting, in which the model may lose its ability to perform previously trained tasks. Full-model tuning is ideal for use cases that:

• Have an abundance of high-quality training data available.
• Require significant adaptations in a model’s behavior or level of domain expertise.
• Differ substantially from the pre-trained model’s domain and therefore require more in-depth training.

For example, full-model tuning may be appropriate in a legal setting that requires:

• Drafting contracts
• Conducting legal research
• Performing compliance checks

All of which necessitate a deep understanding of legal nuances and language.

3. Knowledge distillation

Knowledge distillation describes transferring an LLM’s key knowledge into a second, compressed model. Developers train a ​secondary model (the “student” network, which is also pre-trained) to mimic the behavior of the foundational model (the “teacher” network) on a smaller scale. Practitioners then train the student model on a target task.

Feature-Based Knowledge Transfer in Distillation

There are three primary forms of knowledge distillation:

• Response-based
• Feature-based
• Relation-based

Distillation Approaches

With response-based distillation, a student network learns from the teacher network’s predictions or logits. Feature-based distillation transfers the teacher’s parameter weights and activations to the student network. In relation-based distillation, a student network learns from the broader connections between a teacher network’s parameters and layers.

Student Model Efficiency

Although the resulting student model is smaller than its teacher, it retains most of the predictive power and output quality of a much larger neural network trained on billions of parameters. Nonetheless, this process requires significantly fewer computational resources than full-model tuning and enables more efficient inference, all without sacrificing performance.

Knowledge distillation is ideal for use cases that:

• Require inference on edge devices or other hardware with limited computational power and memory capacity, such as mobile devices and lower-priced cloud setups.
• Are supported by experts in building compatible student-teacher architectures, which is crucial for output reliability.
• Offer enterprises more efficient and cost-effective fine-tuning processes.

For example, knowledge distillation could be used for a model that supports a sales or support team that needs quick answers on their mobile devices while they work in the field.

4. Parameter-efficient fine-tuning

Training a language model is a computationally intensive task. For a full LLM fine-tuning, you need memory not only to store the model, but also the parameters that are necessary for the training process. Your computer might be able to handle the model weights, but allocating memory for optimizing states, gradients, and forward activations during the training process is a challenging task.

PEFT's Core Principle

Simple hardware cannot handle this number of hurdles. This is where PEFT is crucial. While full LLM fine-tuning updates every model's weight during the supervised learning process, PEFT methods only update a small set of parameters. This transfer learning technique selects specific model components and "freezes" the remaining parameters.

Efficiency and Forgetting Prevention

The result is logically having a much smaller number of parameters than in the original model (in some cases, just 15-20% of the original weights); LoRA can reduce the number of trainable parameters by up to 10,000 times. This makes memory requirements much more manageable. Not only that, but PEFT is also dealing with catastrophic forgetting.

Storage Advantages of PEFT

Since it's not touching the original LLM, the model does not forget the previously learned information. Full fine-tuning results in a new version of the model for every task you train on. Each of these is the same size as the original model, so it can create an expensive storage problem if you're fine-tuning for multiple tasks.

5. Prompt tuning

Unlike full-model tuning or knowledge distillation, prompt tuning avoids changing a model’s parameters or training data altogether. Instead, this technique designs prompts that give a model more context for the target domain.

Soft Prompting with AI

Prompt tuning is similar to the concept of prompt engineering, but it utilizes AI to create prompts (“soft” prompting) rather than human engineers (“hard” prompting). Soft prompts manifest as embeddings or number sequences rather than plain language.

Efficiency Through Optimization

Prompt tuning isn’t an accurate fine-tuning technique because it optimizes prompts rather than altering a model’s architectural components like weights and biases. This means it can be even more computationally efficient and cost-effective than strategies like knowledge distillation, which still require some training.

Nevertheless, outputs may not be as accurate or context-rich with prompt tuning compared to more resource-intensive techniques, such as full-model tuning. Prompt tuning is ideal for use cases that:

• Require flexibility. Enterprises may need models to task-switch or adapt to new domain expertise, but don’t have the time or resources to fine-tune regularly.
• Need to retain a model’s foundational knowledge. Prompt tuning helps avoid catastrophic forgetting because it doesn’t change a model’s original parameters.
• Lack of diverse and high-quality training datasets is needed for more intensive training processes.

For example, prompt tuning would be ideal for generating scientific research reports, since its flexibility allows developers to keep outputs up to date in evolving fields.

6. Adapter layers

Fine-tuning traditionally involves adjusting a model’s original parameters, to some degree, to accommodate new data or tasks. Alternatives, such as prompt tuning, avoid changes to the model architecture entirely. Adapter layers fall between the two, freezing a model’s existing parameters while injecting it with new ones.

Independent Domain Tuning

When using adapter layers, developers tune parameters for a target domain independently of the pre-trained model. Then, they insert these layers into the model while leaving its original neural network layers as-is. In inference, the model leverages both types of layers to make predictions.

According to researchers, this technique requires a fraction of the parameter adjustments of fine-tuning (3.6%) with similar performance outcomes.

Flexibility of Adapter Layers

Like prompt tuning, adapter layers are also highly flexible. Developers can add layers for new tasks without readjusting any of the model’s original or older adapter layers. Compared to standard fine-tuning methods, adapter layers are also more effective at avoiding issues such as catastrophic forgetting and overfitting

Nevertheless, adding new layers can slow inference speed and increase model complexity, which can impact AI interpretability and security. Adapter layers are ideal for use cases that:

• Tasks switch or require frequent updates to knowledge and capabilities.
• Have limited datasets, allowing for only a small number of parameter adjustments.
• Help enterprises save on fine-tuning resources and costs without sacrificing model performance

For example, adapter layers could benefit insurance companies that provide customers with AI-powered self-service assessment tools.

7. Transfer learning

Transfer learning involves taking a model that has been trained on general-purpose, massive datasets and fine-tuning it on distinct, task-specific data. This dataset may include labeled examples related to that domain. Transfer learning is used when there is insufficient data or a lack of time to train the data; the main advantage of it is that it offers a higher learning rate and accuracy after training.

You can take existing LLMs that are pre-trained on vast amounts of data, like GPT ¾ and BERT, and customize them for your use case.

8. Task-specific fine-tuning

Task-specific fine-tuning is a method in which a pre-trained model is fine-tuned on a specific task or domain using a dataset tailored to that domain. This method requires more data and time than transfer learning, but can result in higher performance on the specific task.

Efficient Translation Fine-Tuning

For example, translation using a dataset of examples for that task. Interestingly, good results can be achieved with relatively few examples. Often, just a few hundred or thousand examples can yield good performance compared to the billions of pieces of text the model encountered during its pre-training phase.

Nonetheless, there is a potential downside to fine-tuning on a single task. The process may lead to a phenomenon called catastrophic forgetting.

Catastrophic forgetting happens because the complete fine-tuning process modifies the weights of the original LLM. While this leads to excellent performance on a single fine-tuning task, it can degrade performance on other tasks. For example, while fine-tuning can improve a model's ability to perform specific natural language processing (NLP) tasks, such as sentiment analysis, it may also result in improved quality of completion.

Nonetheless, the model may also forget how to perform other tasks. This model knew how to carry out named entity recognition before fine-tuning, correctly identifying.

9. Multi-task learning

Multi-task fine-tuning is an extension of single-task fine-tuning, where the training dataset consists of example inputs and outputs for multiple tasks. Here, the dataset contains examples that instruct the model to carry out a variety of functions, including:

• Summarization
• Review rating
• Code translation
• Entity recognition

Multi-Task Training Benefits

You train the model on this mixed dataset so that it can simultaneously improve the performance of the model on all tasks, thereby avoiding the issue of catastrophic forgetting. Over many epochs of training, the calculated losses across examples are used to update the model's weights, resulting in a fine-tuned model that can excel at multiple tasks simultaneously.

Data Demands and Rewards

One drawback of multi-task fine-tuned models is that they require a lot of data. You may need as many as 50,000 to 100,000 examples in your training set. Nevertheless, assembling this data can be worthwhile and worth the effort. The resulting models are often competent and suitable for use in situations where good performance at many tasks is desirable.

10. Sequential fine-tuning

Sequential fine-tuning involves adapting a pre-trained model to multiple related tasks in sequence. After the initial transfer to a general domain, the LLM might be fine-tuned on a more specific subset. For instance, it can be fine-tuned from general language to medical language and then further to pediatric cardiology.

11. 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-Context Guidance

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.

12. 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.

Integrating Human Judgment with Reward Modeling

Reward modeling offers a practical approach to incorporating human judgment into the learning process, enabling the model to learn complex tasks that are challenging to define using a simple function. This method allows for the model to learn and adapt based on human-provided incentives, thereby enhancing its capabilities.

13. 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, significant 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.

14. 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

Comparative Ranking for Nuanced Feedback

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's subtleties, thereby improving the results.

15. 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 practical when it is difficult to quantify the output quality with a numerical reward, but easier to express a choice 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.

Note that there are other fine-tuning examples, including adaptive, behavioral, and instructional, which reinforce the fine-tuning of large language models. These cover some important specific cases for training language models.

Accessibility of SLM Fine-Tuning for Businesses and Developers

Fine-tuning approaches are now also being widely adapted for small language models (SLMs), which have become one of the biggest GenAI trends of 2024. Fine-tuning a small language model is a lot handier and easier to implement, especially if you’re a small business or a developer looking to improve your model's performance

Related Reading

• Best AI APIs
• Best Multimodal Models
• RAG Use Cases

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.

Fine-tuning Best Practices

Prioritize Data Quality and Quantity for Effective Fine-Tuning

Your fine-tuning dataset directly impacts the performance of your model. Poor quality or insufficient data can lead to poor outcomes, so always look for ways to enhance your dataset before and during the fine-tuning process.

Core Principles of Data Quality Improvement

Standard practices for improving data quality include removing irrelevant or duplicate examples, ensuring diverse coverage of input variations, and eliminating errors. Increasing the quantity of examples can also improve performance by providing the model with more opportunities to learn and generalize.

Regular Evaluation Is Critical for Tracking Progress

Fine-tuning can be a long and arduous process. Therefore, it’s essential to evaluate your model’s performance during the training process regularly. Establish a validation dataset at the outset and assess the model’s performance on this dataset throughout fine-tuning.

Doing so will help you identify signs of overfitting, underfitting, and other issues so that you can make adjustments as needed to improve the model’s performance on the task at hand.

Hyperparameter Tuning: Finding the Right Settings for Your Model

Hyperparameter tuning is vital for effective fine-tuning. It involves identifying the optimal settings for your model to ensure the most efficient learning process. Common hyperparameters to tune during fine-tuning include learning rates, batch sizes, and the number of training epochs.

Each of these settings can dramatically affect how well your model performs both during training and on unseen data, so explore different values for each to avoid issues like overfitting and underfitting.

Be Wary of Overfitting and Underfitting

Fine-tuning can sometimes lead to suboptimal outcomes. Be wary of the following pitfalls:

Overfitting

Occurs when a small dataset is used for training or when the number of epochs is excessively extended. This is usually characterized by the model showing high accuracy on our training dataset but failing to generalize to new data.

Underfitting

Conversely, insufficient training or a low learning rate can result in underfitting, where the model fails to learn the task adequately.

Catastrophic Forgetting: Avoid Losing the Model's Knowledge

In the process of fine-tuning for a particular task, there’s a risk that the model might lose the broad knowledge it initially acquired. This issue, referred to as catastrophic forgetting, can diminish the model’s ability to perform well across a variety of tasks using natural language processing.

Data Leakage: Ensure Training and Validation Sets Are Separate

Always ensure that training and validation datasets are separate and that there is no overlap, as this can yield misleadingly high performance metrics.

RAG: An Effective Alternative to Fine-Tuning

Retrieval-augmented generation (RAG) is a well-established alternative to fine-tuning, which combines natural language generation and information retrieval. RAG ensures that language models are grounded in external, up-to-date knowledge sources and relevant documents, and provides these sources.

This technique bridges the gap between the vast knowledge of general-purpose models and the need for precise, up-to-date information with rich context. Thus, RAG is an essential technique for situations where facts are constantly evolving. Grok, a recent invention of xAI, utilizes RAG techniques to ensure its information remains fresh and current.

Fine-Tuning vs. RAG: Understanding the Differences

When deciding whether to use fine-tuning or RAG, consider the following factors:

Nature of the task

For tasks that benefit from highly specialized models (e.g., domain-specific applications), fine-tuning is often the preferred approach. RAG is ideal for tasks that require integration of external knowledge or real-time information retrieval.

Data availability

Fine-tuning requires a substantial amount of labeled data specific to the task. If such data is scarce, RAG’s retrieval component can compensate by providing relevant information from external sources.

Resource constraints

Fine-tuning can be computationally intensive, whereas RAG leverages existing databases to supplement the generative model, potentially reducing the need for extensive training.

Start Building with $10 in Free API Credits Today!

Inference delivers OpenAI-compatible serverless inference APIs for top open-source LLM models, offering developers the highest performance at the lowest cost in the market. Beyond standard inference, Inference provides specialized batch processing for large-scale async AI workloads and document extraction capabilities designed explicitly for RAG applications.

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

Related Reading

• Best Vector Databases