OpenAI Platform vs Google AI Studio for Finetuning LLM

Advantageous-tuning giant language fashions (LLMs) is a necessary approach for customizing LLMs for particular wants, akin to adopting a specific writing fashion or specializing in a selected area. OpenAI and Google AI Studio are two main platforms providing instruments for this function, every with distinct options and workflows. On this article, we are going to study how these platforms carry out in fine-tuning duties, utilizing my beforehand written articles as coaching knowledge. We are going to consider the flexibility of OpenAI Platform and Google AI Studio to fine-tune LLMs to generate content material that mirrors my writing fashion.

OpenAI Platform

The OpenAI platform gives a complete resolution for fine-tuning fashions, permitting customers to customise and optimize them for particular duties. This platform helps quite a lot of fashions, together with GPT-4o and GPT-4o-mini. OpenAI additionally supplies steering for knowledge preparation, mannequin coaching, and analysis. By leveraging the OpenAI platform, customers can improve the efficiency of fashions in particular domains. This makes them simpler and environment friendly for focused purposes.

The price of fine-tuning is as follows:

The price of inference for fine-tuned fashions is double that of pre-existing fashions.

Knowledge Preparation

LLMs want knowledge to be in a selected format for fine-tuning. Right here’s an instance format for GPT-4o and GPT-4o-mini fashions.

{"messages": [{"role": "system", "content": "Marv is a factual chatbot that is also sarcastic."}, {"role": "user", "content": "What's the capital of France?"}, {"role": "assistant", "content": "Paris, as if everyone doesn't know that already."}]}

After we fine-tune the mannequin, it is going to study to supply responses just like assistant position content material for a given person position content material.

Since we wish the mannequin to supply content material just like ours, we must always use our content material instead of assistant content material.

Let’s think about this content material from one among my articles:

“So, relatively than traversing all of the nodes utilizing a graph utilizing HNSW, we’re localising the search area utilizing dynamic vantage level tree on this algorithm.

This mixture of utilizing each tree and graph makes it one of many quickest and most correct algorithms.”

Since we wish the LLM to generate content material just like the one above, it must be added because the assistant content material like this:

{"position": "assistant", "content material": "So, relatively than traversing all of the nodes utilizing a graph utilizing HNSW, we're localising the search area utilizing dynamic vantage level tree on this algorithm.

This mixture of utilizing each tree and graph makes it one of many quickest and most correct algorithms."}

For the LLM to generate content material like this, it wants person content material (i.e. immediate). As we don’t have it, we will generate the person content material utilizing an LLM. The person content material could be as follows:

{"position": "person", "content material": "Are you able to clarify how the mixture of dynamic vantage level bushes and hierarchical navigable small world (HNSW) graphs improves search effectivity and accuracy in algorithms?"}

We additionally embody an acceptable system immediate the instance for our fine-tuning shall be as follows

{"messages": [{"role": "system", "content": "You are an assistant who writes in a clear, informative, and engaging style."}, {"role": "user", "content": "Can you explain how the combination of dynamic vantage point trees and hierarchical navigable small world (HNSW) graphs improves search efficiency and accuracy in algorithms?"}, {"role": "assistant", "content": "So, rather than traversing all the nodes using a graph using HNSW, we are localising the search space using dynamic vantage point tree in this algorithm.n This combination of using both tree and graph makes it one of the fastest and most accurate algorithms."}]}

If we use a small dimension of content material, it’s potential that the mannequin may miss the context. We have to maintain this in thoughts whereas knowledge preparation. I’m solely utilizing 38 examples right here, however 100 to 500 can be significantly better. I’ve created a ‘my content material.csv’ file the place every row has the content material written by me beneath the column identify ‘content material’.

Now let’s undergo the code to create the required knowledge in the appropriate format.

Code Implementation

OpenAI model 1.57.0 is used right here.

1. Import the libraries.

from dotenv import load_dotenv
load_dotenv('/.env')
import pandas as pd
import tiktoken
from openai import OpenAI

# Initialize the openai shopper
shopper = OpenAI()

2. Examine token dimension.

df = pd.read_csv('my content material.csv')
encoding = tiktoken.get_encoding('o200k_base')

total_token_count = 0
for i in df['content']:
    token_count = len(encoding.encode(i))
    total_token_count += token_count

Counting tokens will assist us to estimate the price of fine-tuning.

3. Generate person content material for the LLM.

def generate_user_content(assistant_response):
    
    # system_message = {"position": "system", "content material": "You're a useful assistant. Your job is to generate person question primarily based on the assistant's response."}
    
    system_message = {"position": "system", "content material": """Given the assistant's response, create a person question or 
    assertion that might logically result in that response. 
    The person content material could be within the type of a query or a request for clarification that prompts the 
    assistant to present the supplied reply"""}
    
    
    assistant_message = {"position": "assistant", "content material": assistant_response}
    messages = [system_message, assistant_message]

    response = shopper.chat.completions.create(
        messages=messages,
        mannequin="gpt-4o-mini",
        temperature=1
    )

    user_content = response.selections[0].message.content material
    return user_content

As we will see, I’ve supplied the content material I wrote as assistant content material and requested the LLM to generate person content material.

user_contents = []

for i in df['content']:
    user_content = generate_user_content(i)
    user_contents.append(user_content)
    
df['user_content'] = user_contents

We will add the generated person content material to the dataframe as a column. The info will appear to be this:

Right here, content material is written by me and user_content is generated by the LLM to make use of as a person position content material (immediate) whereas fine-tuning.

We will save the file now.

df.to_csv('user_content.csv', index=False)

4. Create Jsonl file.

Now we will use the above csv file to create jsonl file as wanted for fine-tuning.

messages = pd.read_csv('user_content.csv')
messages.rename(columns={'content material': 'assistant_content'}, inplace=True)

with open('messages_dataset.jsonl', 'w', encoding='utf-8') as jsonl_file:
    
    for _, row in messages.iterrows():
        
        user_content = row['user_content']
        assistant_content = row['assistant_content']

        jsonl_entry = {
            "messages": [
                {"role": "system", "content": "You are an assistant who writes in a clear, informative, and engaging style."},
                {"role": "user", "content": user_content},
                {"role": "assistant", "content": assistant_content}]
        }

        jsonl_file.write(json.dumps(jsonl_entry) + 'n')

As proven above, we will iterate by the dataframe to create the jsonl file.

Advantageous-tuning in OpenAI Platform

Now, we will use ‘messages_dataset.jsonl’ to fine-tune OpenAI LLMs.

Go to the web site and check in if not signed in already.

If there aren’t any fine-tuning jobs, the interface shall be as follows:

We will click on on ‘Study extra’ to study all of the particulars wanted for fine-tuning, together with the tuneable hyper-parameters.

Now let’s discover ways to fine-tune a mannequin on OpenAI Platform.

1. Click on on ‘Create’. A small window will open.

2. Choose the tactic as ‘Supervised’
3. Choose the Base Mannequin as both ‘gpt-4o’ or ‘gpt-4o-mini’. I bought an error whereas utilizing gpt-4o-mini so I’ve used gpt-4o.
4. Add the jsonl file.
5. Add ‘Suffix’ which is related to the fine-tuning job
6. Use any quantity as ‘Seed’ for reproducibility.
7. Select the hyper-parameters and go away them to make use of the default values. Check with the above-mentioned documentation for pointers on selecting them.

Now, we will click on on ‘Create’ to start out the fine-tuning.

As soon as the effective tuning is accomplished, it is going to be displayed as follows:

We will evaluate the fine-tuned mannequin to pre-existing mannequin responses within the playground by clicking the button on the right-bottom nook.

Right here’s an instance of responses evaluating each fashions:

As we will see, there may be important distinction between the responses of each fashions.

If we use extra examples, then the outcomes may enhance.

Now let’s study Google AI Studio.

Google AI Studio

Google AI Studio is a web-based device for constructing purposes utilizing Gemini LLMs. It additionally permits customers to fine-tune LLMs utilizing their very own knowledge. This customization enhances the mannequin’s efficiency for particular duties or industries, making it extra related and efficient. Advantageous-tuning function for Gemini fashions is newly launched and presently obtainable for Gemini 1.5 Flash solely. The tuning is freed from cost as of January 2025 and the price of inference is identical as pre-existing fashions.

Study Extra: Google’s AI Studio: Your Gateway to Gemini’s Artistic Universe!

Knowledge Add

For Gemini fashions, the info format must be as follows:

training_data = [
{"text_input": "1", "output": "2"},
{"text_input": "3", "output": "4"},]

Google AI Studio supplies a GUI (Graphical Person Interface) to add the info from a csv file. To do that:

1. Open https://aistudio.google.com/prompts/new_data
2. Click on on ‘Actions’, then ‘Import examples’.
3. Then add the csv file. The display will appear to be this:

4. Assign user_content as enter column and content material as output column.
5. Then,  import the examples. We will delete any pointless columns after which save the info utilizing the ‘Save’ button within the top-right nook.

Advantageous-tuning in AI Studio

To fine-tune a mannequin, go to https://aistudio.google.com/tune.

The display will appear to be this:

Now, observe the beneath steps:

1. Choose the imported knowledge from the dropdown menu.
2. Give the tuned mannequin a reputation.
3. To study extra about superior settings, confer with https://ai.google.dev/gemini-api/docs/model-tuning.
4. As soon as carried out, click on on ‘Tune’.

You could find the tuned fashions within the ‘Library’ as follows:

We will additionally use the mannequin within the chat as proven within the above picture.

Conclusion

Advantageous-tuning giant language fashions utilizing OpenAI Platform and Google AI Studio allows customers to tailor fashions to particular wants. This may very well be to make the LLM undertake distinctive writing types or enhance its domain-specific efficiency. Each platforms present intuitive workflows for knowledge preparation and coaching, supporting structured codecs to optimize mannequin habits. With accessible instruments and clear documentation, they empower customers to unlock the total potential of LLMs by aligning them carefully with desired duties and goals.

Steadily Requested Questions

I’m working as an Affiliate Knowledge Scientist at Analytics Vidhya, a platform devoted to constructing the Knowledge Science ecosystem. My pursuits lie within the fields of Pure Language Processing (NLP), Deep Studying, and AI Brokers.