This article provides an in-depth exploration of fine-tuning pre-trained models, including the process, benefits, and more.

1. 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.
 

2. 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 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

3. 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.

4. 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 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 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

5. 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.

6. 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ne-
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 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ne-
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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project
Fine-tune pre-trained generative AI models to build
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7. Ada Babbage Curie
Pre-trained
dataset
Internet text Internet text Internet text
Parameters 1.2 billion 6 billion 13 billion
Released
date
2020 2020 2021
Cost Least costly
Lower cost
than Curie
Lower cost than
Davinci
Capability
Can perform
well if given
more
context
More capable
than Ada, but
less e몭cient
Can execute
tasks that Ada
or Babbage can
do
Tasks it can
perform
Apt for less
nuanced
tasks, like
reformatting
and parsing
text or
simple
classi몭cation
tasks
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
Unique
features
Fastest
model
Perform
straightforward
tasks
Balances speed
and power

8. 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

9. 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,

10. 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.

11. openai api fine_tunes.create ­t <TRAIN_FILE_ID_OR_PATH>
­v <VALIDATION_FILE_ID_OR_PATH>
­m <MODEL>
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ne-
tuning 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ne-
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 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.

12. 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 industry-
speci몭c use cases, which improves performance and accuracy. It is
especially useful for niche applications that require domain-speci몭c data or

13. 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ne-
tune 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.

14. 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 high-
performing 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ts-
all 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

15. 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.
Write to Akash
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