How To Choose The Best Pre-Trained Model For Fine-Tuning?

The release of the "Attention Is All You Need" paper in 2017 introduced the world to a new neural network architecture, Transformers. Since then the Large Language Models started making rounds in the tech world.

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Fast forward to 2023, LLMs are one of the most advanced technologies that is set to redefine our future. Thanks to its NLP capabilities, businesses are actively integrating LLMs into their applications to increase efficiency and elevate user experience.

From chatbots, sales, and content creation to virtual assistants and HR assistants, LLMs are making business applications smarter.

One of the most common ways to implement LLMs into your business model is to finetune a pre-trained model.

While finetuning can give you exceptional results, if you have not selected the right model, everything goes in vain.

With hundreds of pre-trained models in the market, it is not surprising for anyone to get confused.

You just cannot guess and pick any model from the basket and train it. If the model is not compatible with your target task and framework, you won’t get the results you intended.

So, how do you select the right pre-trained model for your business?

What are the factors that one should consider?

Read on to find out…

How selecting the right pre-trained model is important for your business?

Selecting the right pre-trained model is necessary for better results and application efficiency.

Here are some of the reasons why choosing the right pre-trained model is important.

Alignment with the target task

It is crucial that the pre-trained model closely resembles your target task or domain. It is because it gives a better starting point and acts as a foundation for further finetuning the model to achieve higher efficiency.

Understanding pre-trained model

Before you settle on any model, it's better to understand its architecture, strengths, limitations and the task it was trained on.

Without a proper understanding of the model, your finetuning efforts will not yield the results you expected.

Availability and compatibility

Picking any model without carefully checking its documentation and license could land you in trouble.

Also, if you don’t pay attention to how the model is maintained and check if it's regularly updated, your application could suffer.

Model architecture

Not all models are best at every task. Every model architecture has some strengths and weaknesses.

If you use a model whose architecture does not have your task as its strength point, the end output will not be favourable.

Customizations

Customization is the key to aligning the LLM towards your business needs. Selecting an LLM that’s not flexible enough will not allow you to make modifications to fulfil your business goals.

These reasons prove why you should select your LLM based on its popularity. Instead, you should first analyze your business needs and goals. Then find a model that suits your business requirements and target domain.

How to choose the best pre-trained model for fine-tuning?

If your foundation is strong, you will never fail. Similarly, the foundation of your LLM application is the model that you will be training on your data.

That is why I stress so much about carefully choosing your pre-trained model. If your model is robust and as per your objectives, the further processes will go smoothly.

Here is a step-by-step process to find the right pre-trained model.

Task definition

The first thing you should do is to identify the task you intend to do.

·       What is your target task?

·       What do you want your LLM application to do?

·       Is it content generation? Or image recognition? Or customer service?

·       Clearly outline your task, objectives and requirements.

Understanding model architecture

Now that you know what task you want to train your model to do, you have to find the model that does it best.

For this, you will first have to understand different model architecture. Only then you will know which model is best suitable for your task.

·       Learn about different model architectures like BERT, GPT, RoBERTa and others.

·       Analyze their strengths and weaknesses

·       Read about their features and characteristics.

·       While reviewing each model architecture, keep in mind your objectives.

Model strength and weakness analysis

The majority of the models claim that they are best in all NLP tasks. But at what percentage?

Not all model is 100% good at all tasks. That is why it's important that you closely analyze each model's efficiency in doing specific tasks.

·       See how good a model is in content generation, summarizations, image processing, handling lengthy documents and other NLP tasks.

·       Analyze their processing time, memory capacity and efficiency rate.

Match with task requirements

Once you know your objectives and have analyzed models, it's time to match your requirements.

·       Outline the requirements for your target task.

·       Now match your target task and its requirements with different models.

·       Select the one that can closely align with all your prerequisites.

Tips to find the right pre-trained model

Have a look at some additional tips to select the right model.

Model Size: Assess the parameter count to gauge the model's size. Larger models possess a higher capacity to capture intricate patterns but require more computational resources.

Available Checkpoints: Look for reliable sources offering pre-trained model checkpoints. Official checkpoints from developers or well-vetted community-contributed versions are preferred.

Domain and Language: Ensure that the pre-trained model is compatible with your task's domain or language. Fine-tuning on a similar domain or language can boost performance, especially for tasks involving domain-specific terminology.

Pre-training Datasets: Examine the datasets used for the model's pre-training. Models trained on extensive and diverse datasets typically demonstrate a more comprehensive understanding of language.

Transfer Learning Capability: Evaluate the model's proficiency in transfer learning. Some models excel in adapting to a wide range of tasks, while others specialize in specific domains.

Resource Constraints: Take into account the computational resources at your disposal. Larger models demand more memory and processing power, both during fine-tuning and inference.

Fine-Tuning Documentation: Prioritize models for which clear guidelines or tutorials on fine-tuning are available for your specific task. Well-documented models streamline the fine-tuning process.

Bias Awareness: Exercise vigilance regarding potential biases in pre-trained models. If your task requires unbiased predictions, opt for models that have been thoroughly tested and verified for bias and fairness.

Evaluation Metrics: Select appropriate evaluation metrics tailored to your task. For classification, accuracy may be relevant, while language generation tasks might benefit from metrics like BLEU or ROUGE.

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