In 2023, large models have become an important topic, and every industry is exploring the application of large models and how they can help enterprises themselves. Although Microsoft, OpenAI, and other companies have been creating and iterating on large models and exploring more applications, for most enterprises, there is not enough cost to create a unique foundation model: tens of billions of data and super computing resources make the foundation model a "privilege" for some leading enterprises.
However, not being able to create your own base model doesn't mean that a large model can't be used by most companies: after a large number of basic models are shared open-source, companies can use fine tuning methods to train large models and applications that are suitable for their own industries and unique use cases.
In this article, we'll discuss the definition, importance, common methods, and processes of large model fine-tuning, and how Appen can help you implement your application with large models.
Fine-tuning refers to the use of a specific dataset to further train a pre-trained large language model to adapt the model to a specific task or domain.
The fundamental principle is that a machine learning model can only represent the logic and understanding of the dataset it receives, and it cannot recognize the understanding of the data samples it does not obtain, and it cannot answer the questions of specific scenarios well for large models.
For example, a general-purpose large model covers a lot of linguistic information and is capable of having a fluent conversation. However, if there is a need for applications in medicine that can answer patient questions well, a lot of new data needs to be provided for this general model to learn and understand. For example, can ibuprofen be taken at the same time as cold medicine? In order to be sure that the model can answer correctly, we need to fine-tune the base model.
A pre-trained model, or foundation model, can already accomplish many tasks, such as answering questions, summarizing data, and writing **. However, there is no single model that can solve all problems, especially professional Q&A in the industry, information about an organization itself, etc., which cannot be touched by the general model. In this case, it is necessary to use a specific data set and fine-tune the appropriate base model to accomplish a specific task, answer a specific question, and so on. In this case, fine-tuning becomes an important tool.
Now that we've discussed the definition and importance of fine-tuning, let's take a look at two main ones. Depending on how well fine-tuning adjusts the entire pre-trained model, fine-tuning can be divided into two methods: full fine-tuning and reuse
Full fine-tuning: Full fine-tuning refers to fine-tuning the entire pre-trained model, including all model parameters. In this approach, all layers and parameters of the pretrained model are updated and optimized to fit the needs of the target task. This fine-tuning approach is typically useful when there is a large difference between the task and the pretrained model, or where the task requires the model to be highly flexible and adaptive. Full fine-tuning requires more computing resources and time, but can achieve better performance.
Partial repurposing: Partial fine-tuning refers to updating only the top or a few layers of the model during the fine-tuning process, while keeping the underlying parameters of the pre-trained model unchanged. The purpose of this approach is to fine-tune the top layer to suit a specific task while retaining the general knowledge of the pre-trained model. Repurposing is usually used when there is some similarity between the target task and the pre-trained model, or when the task dataset is small. Since only a few layers are updated, repurposing requires less compute resources and time than full fine-tuning, but performance can be degraded in some cases.
The choice of full fine-tuning or repurposing depends on the characteristics of the task and the resources available. If there is a large difference between the task and the pre-trained model, or if the model needs to be highly adaptive, then full fine-tuning may be more suitable. If the task has a high similarity to the pre-trained model, or if resources are limited, then repurposing may be more appropriate. In practice, depending on the task requirements and experimental results, the appropriate fine-tuning method can be selected to achieve the best performance.
At the same time, according to the type of dataset used for fine-tuning, large-scale model fine-tuning can also be divided into two types: supervised fine-tuning and unsupervised fine-tuning
Supervised fine-tuning: Supervised fine-tuning refers to the use of labeled training datasets when fine-tuning. These labels provide the target output of the model during fine-tuning. In supervised fine-tuning, task-specific datasets with labels are often used, such as those for classification tasks, where each sample has a label associated with it. By using these labels to guide the fine-tuning of the model, you can make the model better adapted to a specific task.
Unsupervised fine-tuning: Unsupervised fine-tuning refers to the use of unlabeled training datasets when fine-tuning. This means that during the fine-tuning process, the model can only use the information from the input data itself, without a clear target output. These methods are fine-tuned by learning the intrinsic structure of the data or generating data to extract useful features or improve the representation of the model.
Supervised fine-tuning typically takes place on labeled, task-specific datasets, so the performance of the model can be optimized directly. Unsupervised fine-tuning focuses more on feature learning and representation learning using unlabeled data to extract more useful feature representations or improve the generalization ability of the model. These two fine-tuning methods can be used individually or in combination, depending on the task and the nature and amount of data available.
There are many ways to fine-tune large modelsAs mentioned above, there are different processes, methods, preparations, and cycles for each of them. However, most of the fine-tuning of large models has the following main steps and needs to be prepared:
Prepare the dataset: Collect and prepare the training dataset related to the target task. Ensure dataset quality and annotation accuracy, and perform necessary data cleansing and preprocessing.
Select a pre-trained model Basic model: Select a suitable pre-trained model based on the nature of the target task and the characteristics of the dataset.
Set a fine-tuning strategy: Select an appropriate fine-tuning strategy based on the task requirements and available resources. Consider whether to make full or partial fine-tuning, as well as the level and extent of the fine-tuning.
Set hyperparameters: Determine the hyperparameters in the fine-tuning process, such as learning rate, batch size, number of training rounds, etc. The choice of these hyperparameters has an important impact on the performance and convergence speed of fine-tuning.
Initialize model parameters: Initialize and fine-tune the parameters of the model based on the weights of the pretrained model. For full fine-tuning, all model parameters are initialized randomly; For partial fine-tuning, only the parameters of the top layer or a few layers are initialized randomly.
Fine-tuning training: Use the prepared dataset and fine-tuning strategy to train the model. During the training process, the model parameters are gradually adjusted to minimize the loss function according to the set hyperparameters and optimization algorithms.
Model evaluation and tuning: During the training process, the model is periodically evaluated using a validation set, and hyperparameters or fine-tuning strategies are adjusted based on the evaluation results. This helps to improve the performance and generalization of the model.
Test model performance: After the fine-tuning is complete, the final fine-tuning model is evaluated using the test set to obtain the final performance metrics. This helps to evaluate how well the model performs in real-world applications.
Model Deployment and Application: Deploy the fine-tuned model to the actual application, and make further optimizations and adjustments to meet the actual needs.
These steps provide a general model of the fine-tuning process, but the specific steps and details may vary depending on the task and requirements. Depending on the situation, appropriate adjustments and optimizations can be made.
However, although fine-tuning is already a time-saving and labor-saving method compared to training the basic model, fine-tuning itself still requires sufficient experience and technology, computing power, and management and development costs. That's why Appen has launched a range of customized services and products to help you embrace the big picture.