Fine-tuning Pre-Trained Models for Generative AI Applications

FINE­TUNING PRE­TRAINED MODELS FOR GENERATIVE AI APPLICATIONS

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Generative AI has been gaining huge traction recently thanks to its ability to autonomously generate high-quality text, images, audio and other forms of content. It has various applications in di몭erent domains, from content creation and marketing to healthcare, software development and 몭nance.

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Applications powered by generative AI models can automate tedious and repetitive tasks in a business environment, showcasing intelligent decisionmaking skills. Whether chatbots, virtual assistants, or predictive analytics apps, generative AI revolutionizes businesses’ operations.

It is, however, challenging to create models that can produce output that is both coherent and contextually relevant in generative AI applications Pretrained models emerge as a powerful solution to this issue. Because they are trained on massive amounts of data, pre-trained language models can generate text similar to human language. But there may be situations where pre-trained models do not perform optimally for a particular application or domain. A pre-trained model needs to be 몭ne-tuned in this situation.

The 몭ne-tuning process involves updating pre-trained models with new information or data to help them adapt to speci몭c tasks or domains. During the process of 몭ne-tuning, the model is trained on a speci몭c set of data to customize it to a particular use case. As generative AI applications have grown in popularity, 몭ne-tuning has become an increasingly popular technique to enhance pre-trained models’ performance.

What are pre-trained models?

Popular pre-trained models for generative AI applications

What does 몭ne-tuning a pre-trained model mean?

How does 몭ne-tuning pre-trained models work?

Understanding 몭ne-tuning with an example

How to 몭ne-tune a pre-trained model?

Best practices to follow when 몭ne-tuning a pre-trained model

Bene몭ts of 몭ne-tuning pre-trained models for generative AI applications

What generative AI development services does LeewayHertz o몭er?

What are pre­trained models?

The term “pre-trained models” refers to models that are trained on large amounts of data to perform a speci몭c task, such as natural language

processing, image recognition, or speech recognition. Developers and researchers can use these models without having to train their own models from scratch since the models have already learned features and patterns from the data.

In order to achieve high accuracy, pre-trained models are typically trained on large, high-quality datasets using state-of-the-art techniques When compared to training a model from scratch, these pre-trained models can save developers and researchers time and money It enables smaller organizations or individuals with limited resources to achieve impressive performance levels without requiring much data

Popular pre­trained models for generative AI applications

Some of the popular pre-trained models include:

GPT-3 – Generative Pre-trained Transformer 3 is a cutting-edge model developed by OpenAI. It has been pre-trained on a large amount of text dataset to comprehend prompts entered in human language and generate human-like text. They can be e몭ciently 몭ne-tuned for language-related tasks like translation, question-answering and summarization.

DALL-E – DALL-E is a language model developed by OpenAI for generating images from textual descriptions. Having been trained on a large dataset of images and descriptions, it can generate images that match the input descriptions.

BERT – Bidirectional Encoder Representations from Transformers or BERT is a language model developed by Google and can be used for various tasks, including question answering, sentiment analysis, and language translation. It has been trained on a large amount of text data and can be 몭ne-tuned to handle speci몭c language tasks.

StyleGAN – Style Generative Adversarial Network is another generative model developed by NVIDIA that generates high-quality images of animals, faces and other objects.

VQGAN + CLIP – This generative model, developed by EleutherAI, combines a generative model (VQGAN) and a language model (CLIP) to generate images based on textual prompts. With the help of a large dataset of images and textual descriptions, it can produce high-quality images matching input prompts.

Whisper – Developed by OpenAI, Whisper is a versatile speech recognition model trained on a diverse range of audio data. It is a multi-task model capable of performing tasks such as multilingual speech recognition, speech translation, and language identi몭cation.

What does fine­tuning a pre­trained model mean?

The 몭ne-tuning technique is used to optimize a model’s performance on a new or di몭erent task It is used to tailor a model to meet a speci몭c need or domain, say cancer detection, in the 몭eld of healthcare. Pre-trained models are 몭ne-tuned by training them on large amounts of labeled data for a certain task, such as Natural Language Processing (NLP) or image classi몭cation Once trained, the model can be applied to similar new tasks or datasets with limited labeled data by 몭ne-tuning the pre-trained model.

The 몭ne-tuning process is commonly used in transfer learning, where a pretrained model is used as a starting point to train a new model for a contrasting but related task. A pre-trained model can signi몭cantly diminish the labeled data required to train a new model, making it an e몭ective tool for tasks where labeled data is scarce or expensive.

How does fine­tuning pre­trained models work?

Fine-tuning a pre-trained model works by updating the parameters utilizing the available labeled data instead of starting the training process from the ground up. The following are the generic steps involved in 몭ne-tuning:

1. Loading the pre-trained model: The initial phase in the process is to select

and load the right model, which has already been trained on a large amount of data, for a related task.

2. Modifying the model for the new task: Once a pre-trained model is loaded, its top layers must be replaced or retrained to customize it for the new task. Adapting the pre-trained model to new data is necessary because the top layers are often task speci몭c.

3. Freezing particular layers: The earlier layers facilitating low-level feature extraction are usually frozen in a pre-trained model. Since these layers have already learned general features that are useful for various tasks, freezing them may allow the model to preserve these features, avoiding over몭tting the limited labeled data available in the new task.

4. Training the new layers: With the labeled data available for the new task, the newly created layers are then trained, all the while keeping the weights of the earlier layers constant. As a result, the model’s parameters can be adapted to the new task, and its feature representations can be re몭ned.

5. Fine-tuning the model: Once the new layers are trained, you can 몭ne-tune the entire model on the new task using the available limited data.

Understanding fine­tuning with an example

Suppose you have a pre-trained model trained on a wide range of medical data or images that can detect abnormalities like tumors and want to adapt the model for a speci몭c use case, say identifying a rare type of cancer, but you have a limited set of labeled data available. In such a case, you must 몭netune the model by adding new layers on top of the pre-trained model and training the newly added layers with the available data. Typically, the earlier layers of a pre-trained model, which extract low-level features, are frozen to prevent over몭tting.

How to fine­tune a pre­trained model?

Fine-tuning a pre-trained model involves the following steps:

Choosing a pre-trained model

The 몭rst step in 몭ne-tuning a pre-trained model involves selecting the right model. While choosing the model, ensure the pre-trained model you opt for suits the generative AI task you intend to perform. Here, we would be moving forward with OpenAI base models (Ada, Babbage, Curie and Davinci) to 몭netune and incorporate them into our application. If you are confused about which OpenAI model to select for your right use case, you can refer to the comparison table below:

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Tasks it can perform

well if given

Apt for less nuanced tasks, like reformatting and parsing text or simple classi몭cation tasks

Unique features Fastest model

Most suited for semantic search tasks. It can also do moderate classi몭cation tasks

Handle complex classi몭cation tasks, sentiment analysis, summarization, chatbot applications and Q&A

Perform straightforward tasks

Balances speed and power

Once you 몭gure out the right model for your speci몭c use case, start installing the dependencies and preparing the data

Installation

It is suggested to use OpenAI’s Command-line Interface (CLI). Run the following command to install it:

pip install ­­upgrade openai

To set your OPENAI API KEY environment variable, you can add the following line to your shell initialization script (such as .bashrc, zshrc, etc.) or run it in the command line before executing the 몭ne-tuning command.

export OPENAI API KEY="<OPENAI API KEY>"

Data preparation

Before 몭ne-tuning the model, preparing the data corresponding to your particular use case is crucial. The raw data cannot be directly fed into the model as it requires 몭ltering, formatting and pre-processing into a speci몭c format. The data needs to be organized and arranged systemically so the model can interpret and analyze the data easily

For our application, the data must be transformed into JSONL format, where each line represents a training example of a prompt-completion pair. You can utilize OpenAI’s CLI data preparation tool to convert your data into this 몭le format e몭ciently.

{"prompt": "<prompt text>", "completion": "<ideal generated text>"}

{"prompt": "<prompt text>", "completion": "<ideal generated text>"}

{"prompt": "<prompt text>", "completion": "<ideal generated text>"} ...

CLI data preparation tool

OpenAI’s CLI data preparation tool validates, provides suggestions and reformats your data. Run the following command:

openai tools fine tunes.prepare data ­f <LOCAL FILE>

A JSONL 몭le can be generated from any type of 몭le, whether a CSV, JSON, XLSX, TSV or JSONL 몭le. Ensure that a prompt and completion key/column is included.

Develop a 몭ne-tuned model

Once you have prepared and pre-processed the training data and converted it into JSONL 몭le, you can begin the 몭ne-tuning process utilizing the OpenAI CLI:

openai api fine tunes.create ­t <TRAIN FILE ID OR PATH> ­m <BASE MODEL

In the above command, the ‘BASE MODEL’ should be the name of the base model you chose, be it Babbage, Curie, Ada or Davinci. The above commands result in the following things:

Develops a 몭ne-tuned job

Uploads the 몭le utilizing the 몭les API or avails an already-uploaded 몭le. Events are streamed until the task is complete

Once you begin the 몭ne-tuning job, it takes some time to complete

Depending on the size of your dataset and model, your job may be queued behind other jobs in our system If the streaming of the event is interrupted due to any reason, you can run the following command to resume it:

openai api fine tunes.follow ­i <YOUR FINE TUNE JOB ID>

After the job is completed, it will display the name of the 몭ne-tuned model.

Use a 몭ne-tuned model

When you successfully develop a 몭ne-tuned model, the 몭eld,

When you successfully develop a 몭ne-tuned model, the 몭eld, ‘FINE TUNED MODEL’, will print the name of your customized model, for example, “curie:ft-personal-2023-03-01-11-00-50.” You can specify this model as a parameter for OpenAI’s Completions API and utilize Playground to submit requests.

The model may not be ready to handle requests immediately after your job completes. It is likely that your model is still being loaded if completion requests time out In this case, try again later

To begin making requests, include the model’s name as the ‘model’ parameter in a completion request. Here’s an example using OpenAI CLI:

openai api completions.create ­m <FINE TUNED MODEL> ­p <YOUR PROMPT>

The code snippet using Python may look like this: import openai openai.Completion.create( model=FINE TUNED MODEL, prompt=YOUR PROMPT)

In the above code, other than the ‘model’ and ‘prompt,’ you can also use other completion parameters like ‘frequency penalty,’ ‘max tokens,’ ‘temperature,’ ‘presence penalty,’ and so on.

Validation

Once the model is 몭ne-tuned, run the 몭ne-tuned model on a separate validation dataset to assess its performance To perform validation, you must reserve some data before 몭ne-tuning the model. The reserved data should have the same format as the training data and be mutually exclusive Including a validation 몭le in the 몭ne-tuning process allows for periodic evaluations of the model’s performance against the validation data

By performing this step, you can identify any potential issues and 몭ne-tune the model further to make it more accurate.

Best practices to follow when fine­tuning a pre­trained model

While 몭ne-tuning a pre-trained model, several best practices can help ensure successful outcomes. Here are some key practices to follow:

1. Understand the pre-trained model: Gain a comprehensive understanding of the pre-trained model architecture, its strengths, limitations, and the task it was initially trained on. This knowledge can enhance the 몭ne-tuning process and help make appropriate modi몭cations.

2. Select a relevant pre-trained model: Choose a pre-trained model that aligns closely with the target task or domain. A model trained on similar data or a related task will provide a better starting point for 몭ne-tuning.

3. Freeze early layers: Typically, the lower layers of a pre-trained model capture generic features and patterns. Freeze these early layers during 몭netuning to preserve the learned representations. This practice helps prevent catastrophic forgetting and lets the model focus on task-speci몭c 몭ne-tuning.

4. Adjust learning rate: Experiment with di몭erent learning rates during 몭netuning. It is typical to use a smaller learning rate compared to the initial pretraining phase. A lower learning rate allows the model to adapt more gradually and prevent drastic changes that could lead to over몭tting.

5. Utilize transfer learning techniques: Transfer learning methods can enhance 몭ne-tuning performance. Techniques like feature extraction, where pre-trained layers are used as 몭xed feature extractors, or gradual unfreezing, where layers are unfrozen gradually during training, can help preserve and transfer valuable knowledge.

6. Regularize the model: Apply regularization techniques, such as dropout or weight decay, during 몭ne-tuning to prevent over몭tting Regularization helps the model generalize better and reduces the risk of memorizing speci몭c training examples

7. Monitor and evaluate performance: Continuously monitor and evaluate the performance of the 몭ne-tuned model on validation or holdout datasets Use appropriate evaluation metrics to assess the model’s progress and make informed decisions on further 몭ne-tuning adjustments

8. Data augmentation: Augment the training data by applying transformations, perturbations, or adding noise Data augmentation can increase the diversity and generalizability of the training data, leading to better 몭ne-tuning results

9. Consider domain adaptation: If the target task or domain signi몭cantly di몭ers from the pre-training data, consider domain adaptation techniques These methods aim to bridge the gap between the pre-training data and the target data, improving the model’s performance on the speci몭c task

10. Regularly backup and save checkpoints: Save model checkpoints at regular intervals during 몭ne-tuning to ensure progress is saved and prevent data loss. This practice allows for easy recovery and enables the exploration of di몭erent 몭ne-tuning strategies

Benefits of fine­tuning pre­trained models for generative AI applications

Fine-tuning a pre-trained model for generative AI applications promises the following bene몭ts:

As pre-trained models are already trained on a large amount of data, it eliminates the need to train a model from scratch, saving time and resources

Fine-tuning facilitates customization of the pre-trained model to industryspeci몭c use cases, which improves performance and accuracy It is especially useful for niche applications that require domain-speci몭c data or

specialized knowledge.

As pre-trained models have already learned the underlying patterns in the data, 몭ne-tuning them can make them easier to identify and interpret the output.

What generative AI development services does LeewayHertz offer?

LeewayHertz is an expert generative AI development company with over 15 years of experience and a team of 250+ full-stack developers. With expertise in multiple AI models, including GPT-3, Midjourney, DALL-E, and Stable Di몭usion, our AI experts specialize in developing and deploying generative model-based applications. We have profound knowledge of AI technologies such as Machine Learning, Deep Learning, Computer Vision, Natural Language Processing (NLP), Transfer Learning, and other ML subsets. We o몭er the following generative AI development services:

Consulting and strategy building

Our AI developers assess your business goals, objectives, needs and other aspects to identify issues or shortcomings that can be resolved by integrating generative AI models We also design a meticulous blueprint of how generative AI can be implemented in your business and o몭er ongoing improvement suggestions once the solution is deployed

Fine-tuning pre-trained models

Our developers are experts in 몭ne-tuning models to adapt them for your business-speci몭c use case. We ful몭ll all the necessary steps required to 몭netune a pre-trained model, be it GPT-3, DALL.E, Codex, Stable Di몭usion or Midjourney.

Custom generative AI model-powered solution development

From 몭nding the right AI model for your business and training the model to evaluating the performance and integrating it into your custom generative AI model-powered solution for your system, our developers undertake all the steps involved in building a business-speci몭c solution.

steps involved in building a business-speci몭c solution.

Model integration and deployment

At LeewayHertz, we prioritize evaluating and understanding our clients’ requirements to e몭ciently integrate generative AI model-powered solutions and applications into their business environment.

Prompt engineering services

Our team of prompt engineers is skilled in understanding the capabilities and limitations of a wide range of generative models, identifying the type and format of the prompt apt for the model and customizing the prompt to suit the project’s requirement using advanced NLP and NLG techniques

Endnote

Fine-tuning pre-trained models is a reliable technique for creating highperforming generative AI applications. It enables developers to create custom models for business-speci몭c use cases based on the knowledge encoded in pre-existing models. Using this approach saves time and resources and ensures that the models 몭ne-tuned are accurate and robust However, it is imperative to remember that 몭ne-tuning is not a one-size-몭tsall solution and must be approached with care and consideration But the right approach to 몭ne-tuning pre-trained models can unlock generative AI’s full potential for your business

Looking for generative AI developers Look no further than LeewayHertz. Our team of experienced developers and AI experts can help you 몭ne-tune pre-trained models to meet your speci몭c needs and create innovative applications.

Author’s Bio

Akash Takyar

CEO LeewayHertz

Akash Takyar is the founder and CEO at LeewayHertz. The experience of building over 100+ platforms for startups and enterprises allows Akash to rapidly architect and design solutions that are scalable and beautiful.

Akash's ability to build enterprise-grade technology solutions has attracted over 30 Fortune 500 companies, including Siemens, 3M, P&G and Hershey’s. Akash is an early adopter of new technology, a passionate technology enthusiast, and an investor in AI and IoT startups.

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