Hyperparameter Tuning for finetuning Large Language Models

Finetuning large language models is a powerful technique for harnessing full power of LLMs. However, finetuning requires careful selection and tuning of hyperparameters to achieve optimal performance.

With our no-code LLMOps platform for finetuning LLM models - EasyFinetuner, you have built-in features to do hyperparameter tuning for finetuning LLMs and you can finetune LLMs from multiple providers like OpenAI, Cohere and AI21 Studio.

In this article, we'll explore hyperparameter tuning and its methods, including random, grid, and Bayesian, and how it can be used for finetuning large language models.

What are Hyperparameters?

Imagine you're a cool DJ, spinning records at a party and making everyone dance to your tunes. Your mixing console has a bunch of adjustable equalizer buttons, each controlling various aspects of the music like bass, treble, and volume. Just like a DJ tweaks these equalizer buttons for the perfect sound that gets the crowd excited, in the world of machine learning, we have these magical buttons called "hyperparameters"!

Hyperparameters are the super cool buttons that control the groove of our machine learning models during training, affecting how well they can dance with the data. They're set before the party (training) starts, and adjusting them, you can strike that perfect balance so your model can be the life of the party (perform well).

Some examples of these DJ equalizer buttons (hyperparameters) in finetuning LLMs are the learning rate, batch size, and number of epochs.

Hyperparameter tuning

Just like how a DJ carefully adjusts the equalizer to find the best combination for a rocking party, in machine learning, we too experiment with different settings for these hyperparameters to find the best combination for our model. This process is called hyperparameter tuning.

By creating multiple models, each with a unique combination of hyperparameters, and train them on our data. We then compare their performance through some evaluation measure such as an accuracy score. The model with the highest score is selected as the best one for our task.

Hyperparameter tuning involves a lot of trial and error. Some people do this manually. But doing this manually is often time-consuming and not optimal. Instead, a more effective and systematic approach is using automated hyperparameter tuning methods.

Hyperparameter Tuning Methods

Hyperparameter tuning is the process of selecting the best hyperparameters for a given task. There are three common methods for hyperparameter tuning: random, grid, and Bayesian.

Random Search: This method randomly selects hyperparameters from a given range of values. It is a simple and efficient method that can quickly explore a large search space. However, it may not find the optimal hyperparameters and can be computationally expensive.
Grid Search: This method exhaustively searches all possible combinations of hyperparameters from a given range of values. It is a systematic method that guarantees finding the optimal hyperparameters. However, it can be computationally expensive and may not scale well for large search spaces.
Bayesian Optimization: This method uses a probabilistic model to predict the performance of different hyperparameters and selects the best ones based on the model's predictions. It is an efficient method that can handle large search spaces and is less computationally expensive than grid search. However, it requires more expertise to set up and may not always find the optimal hyperparameters.

Hyperparameters for finetuning Large Language Models

When finetuning large language models, several hyperparameters need to be tuned to achieve optimal performance. Let's take a look at some of the hyperparameters and their configurations:

Base LLM model: The model list is a list of base models that you can finetune. You can choose multiple models, and the chosen models will be used for finetuning. In OpenAI, you have GPT-3 models ada, babbage, curie and davinc. They come in different sizes and have different capabilities. For example, a small base model will perform on par with a large base model on simple tasks and cost less to train and run. A large base model will perform better than a small base model on complex tasks, but it will cost more to train and will be slower to run. In some cases, a small base model may be sufficient for your task, while in others, you may need a large base model.
Batch Size Configuration: The batch size configuration is the list of batch sizes used for finetuning. Larger batch sizes tend to work better for larger datasets.
Epoch Configuration: The epoch configuration is the list of epoch values used for finetuning. An epoch refers to one full cycle through the training dataset. Choosing a higher number of epochs can lead to better performance but will take longer to train. Sometimes, a lower number of epochs may be sufficient for your task.
Learning Rate Configuration: The learning rate configuration is the list of learning rates used for finetuning. The learning rate controls how much the model changes its parameters in response to the estimated error each time it updates them. A higher learning rate can lead to faster training but may cause the model to overfit. A lower learning rate can lead to slower training but may help the model generalize better.
Prompt Loss Weight Configuration: The prompt loss weight configuration is the list of prompt loss weights used for finetuning. This controls how much the model tries to learn to generate the prompt and can add a stabilizing effect to training when completions are short.

Conclusion

Hyperparameter tuning is a crucial step in finetuning large language models for natural language processing tasks. Random, grid, and Bayesian optimization are common methods for hyperparameter tuning. When finetuning large language models, several hyperparameters need to be tuned, including base LLM, batch size configuration, epoch configuration, learning rate configuration, and prompt loss weight configuration. By carefully selecting and tuning hyperparameters, we can achieve optimal performance in finetuning large language models.