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How to fine-tune a pre-trained model 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 different domains, from content creation and marketing to healthcare, software development and finance. Applications powered by generative AI models can automate tedious and repetitive tasks in a business environment, showcasing intelligent decision-making 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. Pre-trained 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 fine-tuned in this situation.

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

What are pre-trained models?

The term “pre-trained models” refers to models that are trained on large amounts of data to perform a specific 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.

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 efficiently fine-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 fine-tuned to handle specific 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 identification.

What does fine-tuning a pre-trained model mean?

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

The fine-tuning process is commonly used in transfer learning, where a pre-trained model is used as a starting point to train a new model for a contrasting but related task. A pre-trained model can significantly diminish the labeled data required to train a new model, making it an effective tool for tasks where labeled data is scarce or expensive.

How does fine-tuning pre-trained 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 fine-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 specific.
  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 overfitting 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 refined.
  5. Fine-tuning the model: Once the new layers are trained, you can fine-tune the entire model on the new task using the available limited data.

How does fine-tuning pre-trained models work

Understanding fine-tuning 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 specific 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 fine-tune 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 overfitting.

How to fine-tune a pre-trained model?

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

Choosing a pre-trained model

The first step in fine-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 fine-tune 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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Fine-tune pre-trained generative AI models to build highly performant solutions

Ada Babbage Curie Davinci
Pre-trained dataset Internet text Internet text Internet text Internet text
Parameters 1.2 billion 6 billion 13 billion 175 billion
Released date 2020 2020 2021 2021
Cost Least costly Lower cost than Curie Lower cost than Davinci Most costly
Capability Can perform well if given more context More capable than Ada, but less efficient Can execute tasks that Ada or Babbage can do Can perform any tasks the other models do with fewer instructions
Tasks it can perform Apt for less nuanced tasks, like reformatting and parsing text or simple classification tasks Most suited for semantic search tasks. It can also do moderate classification tasks Handle complex classification tasks, sentiment analysis, summarization, chatbot applications and Q&A Solve logic problems, comprehend text intent, intuit cause and effect, manage complex summarization tasks etc.
Unique features Fastest model Perform straightforward tasks Balances speed and power Most powerful model

Once you figure out the right model for your specific use case, start installing the dependencies and preparing the data.


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 fine-tuning command.


Data preparation

Before fine-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 filtering, formatting and pre-processing into a specific 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 file format efficiently.

{"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 file can be generated from any type of file, whether a CSV, JSON, XLSX, TSV or JSONL file. Ensure that a prompt and completion key/column is included.

Develop a fine-tuned model

Once you have prepared and pre-processed the training data and converted it into JSONL file, you can begin the fine-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 fine-tuned job.
  • Uploads the file utilizing the files API or avails an already-uploaded file.
  • Events are streamed until the task is complete.

Once you begin the fine-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 fine-tuned model.

Use a fine-tuned model

When you successfully develop a fine-tuned model, the field, ‘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

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.


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

openai api fine_tunes.create -t <TRAIN_FILE_ID_OR_PATH> \
-m <MODEL>

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

Best practices to follow when fine-tuning a pre-trained model

While fine-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 fine-tuning process and help make appropriate modifications.
  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 fine-tuning.
  3. Freeze early layers: Typically, the lower layers of a pre-trained model capture generic features and patterns. Freeze these early layers during fine-tuning to preserve the learned representations. This practice helps prevent catastrophic forgetting and lets the model focus on task-specific fine-tuning.
  4. Adjust learning rate: Experiment with different learning rates during fine-tuning. It is typical to use a smaller learning rate compared to the initial pre-training phase. A lower learning rate allows the model to adapt more gradually and prevent drastic changes that could lead to overfitting.
  5. Utilize transfer learning techniques: Transfer learning methods can enhance fine-tuning performance. Techniques like feature extraction, where pre-trained layers are used as fixed 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 fine-tuning to prevent overfitting. Regularization helps the model generalize better and reduces the risk of memorizing specific training examples.
  7. Monitor and evaluate performance: Continuously monitor and evaluate the performance of the fine-tuned model on validation or holdout datasets. Use appropriate evaluation metrics to assess the model’s progress and make informed decisions on further fine-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 fine-tuning results.
  9. Consider domain adaptation: If the target task or domain significantly differs 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 specific task.
  10. Regularly backup and save checkpoints: Save model checkpoints at regular intervals during fine-tuning to ensure progress is saved and prevent data loss. This practice allows for easy recovery and enables the exploration of different fine-tuning strategies.

Benefits of fine-tuning pre-trained models for generative AI applications

Fine-tuning a pre-trained model for generative AI applications promises the following benefits:

  • 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 industry-specific use cases, which improves performance and accuracy. It is especially useful for niche applications that require domain-specific data or specialized knowledge.
  • As pre-trained models have already learned the underlying patterns in the data, fine-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 Diffusion, 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 offer 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 offer ongoing improvement suggestions once the solution is deployed.

Fine-tuning pre-trained models

Our developers are experts in fine-tuning models to adapt them for your business-specific use case. We fulfill all the necessary steps required to fine-tune a pre-trained model, be it GPT-3, DALL.E, Codex, Stable Diffusion or Midjourney.

Custom generative AI model-powered solution development

From finding 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-specific solution.

Model integration and deployment

At LeewayHertz, we prioritize evaluating and understanding our clients’ requirements to efficiently 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.


Fine-tuning pre-trained models is a reliable technique for creating high-performing generative AI applications. It enables developers to create custom models for business-specific use cases based on the knowledge encoded in pre-existing models. Using this approach saves time and resources and ensures that the models fine-tuned are accurate and robust. However, it is imperative to remember that fine-tuning is not a one-size-fits-all solution and must be approached with care and consideration. But the right approach to fine-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 fine-tune pre-trained models to meet your specific needs and create innovative applications.

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Author’s Bio


Akash Takyar

Akash Takyar LinkedIn
CEO LeewayHertz
Akash Takyar is the founder and CEO of LeewayHertz. With a proven track record of conceptualizing and architecting 100+ user-centric and scalable solutions for startups and enterprises, he brings a deep understanding of both technical and user experience aspects.
Akash's ability to build enterprise-grade technology solutions has garnered the trust of 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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