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Fine-tuning in machine learning and artificial intelligence (AI) is the process of taking an already trained or pre-trained model and training it on other datasets so it can perform specialized tasks. It allows large language models (LLMs) and other generative AI tools to perform specific tasks, like image processing. Fine-tuning enables developers to save time and money by building on existing models instead of creating new ones.

Explore fine-tuning, how you can fine-tune a model, what it does, who uses it, its challenges and limitations, and how you can start fine-tuning your AI models.听

Types of fine-tuning

There are typically two types of fine-tuning for LLMs and other machine learning models: feature extraction and full fine-tuning. The best option depends on the practitioner's particular use case. Each type uses various methods to achieve its final model鈥檚 goal. Read on to learn more about these two types of fine-tuning and the techniques associated with them below.

Feature extraction fine-tuning

Feature extraction fine-tuning, sometimes referred to as parameter-efficient fine-tuning, 鈥渇reezes鈥� the top layers of the model while the final layers train on the specified data set. This keeps most of the existing model intact, making it more computationally efficient. Three kinds of feature extraction methods exist:

• Partial fine-tuning: This method follows the basic principles of feature extraction by exclusively training the outer layers of the network. When only the outer layers are updated, the model鈥檚 core stays the same, preserving its ability to recognize general features. However, the prediction becomes more accurate as the outer layers learn how to analyze the finer details.

• Additive fine-tuning: In this method, the model's pre-trained parameters are frozen or kept the same, and new parameters are added to the model. All of the original model weights remain the same. The new functions added to the model save processing power.

• Reparameterization: This method, known as low rank adaptation (LoRA), uses different matrices to update the model weights separately without directly affecting the existing model. Instead, the new matrices are lower-rank matrices representing weight updates broken into smaller chunks. This allows for swapping various iterations of fine-tuned updates in and out of the model as needed.

Full model fine-tuning

Full model fine-tuning is similar to pre-training in that the entire model trains on a data set and updates the weights accordingly. This process updates the whole model, potentially leading to stability issues. To stabilize the model, you can tune hyperparameters like the learning rate to encourage more balanced growth.

This fine-tuning method is more computationally intensive than feature extraction fine-tuning. It鈥檚 best suited to situations where the new data set is both sizable and distinct from the original training data set.

Reinforcement learning from human feedback聽

Reinforcement learning from human feedback (RLHF) is a fine-tuning method that helps models learn about complex human behaviors like comedy or humor, which are not easily translated into straightforward mathematical data to train the model. This approach involves humans stepping in to correct the model at various stages in its development. It's best suited for situations where the model eventually has to interact with users directly, such as a chatbot.

What does fine-tuning do?

Fine-tuning a model requires using a specialized data set smaller than the original pre-training. Using one of the methods outlined above, the model develops new capabilities depending on which aspects the developer decides are essential. While fine-tuning specializes the model鈥檚 abilities, developers must avoid drastically changing the pre-trained model's weights. They can reduce this by choosing a slower learning rate.

Many neural networks initially train on a specific task, such as image processing. Fine-tuning gives these models specialized abilities within that field. For example, a model that already specializes in image processing may be adjusted to identify bees more accurately in an image.

Fine-tuning also makes the implementation of LLMs more useful. For example, a pre-trained model might have a general grasp of English, but a law firm may fine-tune it to understand legal jargon specifically.

Who uses fine-tuning?

Organizations and AI developers use fine-tuning to adapt a model to their use case. For example, a business may want to develop an application that identifies plants. That organization can work with developers to fine-tune an image identification model for identifying plants. Below are some other industries that could use fine-tuning:

• Customer service: Many LLM chatbots like ChatGPT have conversational abilities. However, a customer service company may want to fine-tune a model鈥檚 ability to help customers with their company鈥檚 products and questions specific to their business.

• Recommendations: A retail, e-commerce, or media company may have a recommendation algorithm already in place. They could then utilize fine-tuning to tailor that model to their customers using browsing data, purchase history, and liked content.

• Health care: Health care professionals may want an image processing model to learn about specific disease markers in medical scans. They could use fine-tuning to create a model that specializes in identifying those disease markers on the scans.

Some other jobs that may implement fine-tuning include:

• AI engineers

• Software developers

• Data scientists

Learn more about each job, such as salary and job outlook.

AI engineer

Median annual base pay (Glassdoor): $103,561 []

Job outlook (projected growth from 2023 to 2033): 26 percent []

AI engineers develop models using machine learning and deep learning. They may work with businesses, developers, data scientists, and others to build new AI models and manage existing models. They develop the entire infrastructure and environment that goes into the model's training.

Software developers

Median annual base pay (Glassdoor): $95,234 []

Job outlook (projected growth from 2023 to 2033): 17 percent []

Software developers create, design, and implement new computer programs to perform specific computing tasks. They design how each piece of the software works, then work with the team to implement the new software or upgrade older software.

Data scientists

Median annual base pay (Glassdoor): $113,620 []

Job outlook (project growth from 2023 to 2033): 36 percent []

Data scientists develop algorithms that aid in implementing machine learning models that make predictions. They test these algorithms and use or develop additional datasets to create more accurate models.

Benefits and challenges of fine-tuning

Fine-tuning can be beneficial when creating specialized AI models because it can save time and money, but the process comes with some challenges. Explore more about the benefits and challenges of fine-tuning.

Benefits

Fine-tuning is an effective process that optimizes pre-trained models to perform better in specific use cases, depending on your needs. Some additional benefits of fine-tuning include:

• Cost savings: Fine-tuning is more affordable than developing a model from scratch. It reduces time, computing requirements, and the need for new hardware.

• Improved accuracy for niche applications: Fine-tuning enhances performance for specialized tasks by refining a pre-trained model with a smaller, domain-specific data set.

• Greater accessibility: Smaller organizations can leverage pre-trained models and fine-tune them with their own data, making advanced machine learning more attainable.

Challenges

While it has many benefits, fine-tuning comes with some challenges, especially in keeping the learning of the pre-trained model intact.

• Due to the small data sets used in fine-tuning, you can risk overfitting your model. An overfitted model is so attuned to the training data that it doesn鈥檛 perform well with new data.

• Fine-tuning a model can risk having the pre-trained model 鈥渇orget鈥� some of its original training. This happens when you target specific layers too heavily or use a data set that's too different from the training set.

• If the original pre-trained model has certain biases, flaws, or security issues, your fine-tuned model will also have those issues.

How to get started with fine-tuning

Fine-tuning a model is an advanced machine learning technique that requires deep learning and AI skills. Before you learn about fine-tuning, it's crucial to understand the machine learning process. Learn the basics before you start tuning:

• Explore the fundamentals of machine learning in the Supervised Machine Learning: Regression and Classification course from Stanford and DeepLearning.AI on 糖心vlog官网观看.

• Learn about some of the popular pre-trained models, such as GPT-4, BERT, RoBERTa, and PaLM, and the kinds of fine-tuning you can do on them.

• Discover communities around machine learning and fine-tuning like TensorFlow and Hugging Face, which have extensive libraries of pre-trained models you can tune.

• When you're ready to fine-tune your first pre-trained model, try a guided project like the Transfer Learning for NLP with TensorFlow Hub on 糖心vlog官网观看.

Best practices for fine-tuning

Here are some best practices associated with fine-tuning:

1. Data preparation: When preparing your data set, you must find quality data that fits your desired task. Then you clean the data, standardize and format it for your model, and properly label it if you鈥檙e using supervised learning.听

2. Model selection: Selecting your pre-trained model is key to creating your fine-tuned model. Select the model most closely associated with your task. For example, an LLM wouldn't be a good choice for an image processing task. Research the task鈥檚 probable data and computational requirements.

3. Parameter selection: Explore the parameters you need to tune and the layers you want to freeze. Identify which methods will most likely work best for your desired outcome.

4. Fine-tuning: Run your fine-tuning by optimizing your hyperparameters, choosing a gradient descent algorithm, watching for biases, and identifying metrics you want to use to evaluate your model performance.

5. Iteration: Continue to refine your model as you evaluate your results based on the metrics you set. You can adjust hyperparameters such as batch size, learning rate, and regularization until the model performs optimally.

6. Deployment: Once the model meets your set standard, deploy it into the environment in which it鈥檚 meant to work. As you continually monitor its real-world performance, consider its computational requirements, security, and scalability.

Getting started with fine-tuning on 糖心vlog官网观看

Fine-tuning is a core component of making deep learning applications more accessible for specialized use cases. To learn more about fine-tuning and deep learning, try the Finetuning Large Language Models project from DeepLearning.AI on 糖心vlog官网观看. Gain fine-tuning and deep learning skills in the Generative AI Advance Fine-Tuning for LLMs from IBM, or the Deep Learning Specialization from DeepLearning.AI, both on 糖心vlog官网观看.