Conversational AI is becoming increasingly popular in a variety of fields, from customer service to virtual assistants. However, fine tune ChatGPT with the underlying language model is required to obtain best performance in a specific use case.
In this article, we will look at how to use fine tune ChatGPT to improve its conversational abilities for a specific use case.
Why to Fine Tune ChatGPT?
Fine-tuning ChatGPT entails retraining the model on a custom dataset that is indicative of the desired task. This dataset can be obtained from a variety of ways, including live chats, consumer interactions, and simulated talks. The collected data is then pre-processed to ensure that it is clean, anonymized, and correctly formatted to maintain consistency and privacy.
Fine Tune ChatGPT Model
Create the use case: This requires a clear grasp of the desired objective and the goals to be met. ChatGPT can be customized for a variety of purposes, including sentiment analysis, language translation, question answering, and more. By identifying the specific use case, it is easier to modify ChatGPT’s capabilities and get best outcomes that we need.
Extract and preprocess data: This information should be pertinent to the use case you identified in Create the use case procedure. It should also be neatly formatted and clean.
Make the data ready for training: This is accomplished by dividing the data into training, validation, and test sets. The training set will be used to train the model, the validation set to evaluate the model’s performance during training, and the test set to evaluate the model’s performance when training is completed.
Fine-tune the model: This entails training the pre-trained ChatGPT model on the data collected in Extract and preprocess data. Because the hyperparameters you employ have an impact on the model’s performance, you may need to experiment to find the ideal collection of hyperparameters.
Research the model: After you’ve fine tuned chatgpt model, you may run it through its paces on the test set. This will assist you in determining whether the model is ready for deployment.
Install the model: You can deploy the model to production after you are pleased with its performance. This entails making the model accessible to user so they can interact with it.
- GPT-3 has received substantial pre-training on a big corpus of online text data. It can frequently grasp the underlying job and create realistic completions with only a few samples as a suggestion.
- This capability, referred to as “few-shot learning,” demonstrates the model’s excellent generalization skills.
- Fine-tuning, on the other hand, extends few-shot learning by training the model on a considerably greater number of examples that surpass the prompt’s capacity.
- This method improves performance across a wide range of tasks. There is no longer a need to provide examples within the prompt after fine-tuning, resulting in cost savings and speedier response times.
Set up Environment for fine tune ChatGPT
To fine tune ChatGPT, you must first generate an OpenAI API key. To generate an API key, follow these steps:
- If you haven’t already done so, go to https://platform.openai.com/signup to create a free account.
- If you already have an OpenAI account, go to https://platform.openai.com/account/api-keys to obtain your API credentials.
- On the API keys page, click the “Create new secret key” button to generate a new key.
Following these steps will allow you to generate an API key, which you will need to interface with the OpenAI API and use the ChatGPT capabilities.
Installation
Following the creation of an API key, the OpenAI Python library must be installed. This module makes it easy to send queries to the API and operate with the results. To install the OpenAI Python library, use the pip package manager using the following command:
pip install openai
Creating First API Request
You can begin performing API queries to ChatGPT once you have your API key. You will need to utilize a tool like Postman or curl to accomplish this.
Here’s an example of an API request to ChatGPT:
import openai # Set your API key openai.api_key = “YOUR_API_KEY” # Create a completion object completion = openai.Completion.create( engine=”text-davinci-003″, prompt=”What is the meaning of peaceful life?” ) # Get the response from the API response = completion.get(“choices”)[0].text # Print the response print(response)
How to fine tune ChatGPT for a Specific Use-Case?
Using the given code, you can ask the ChatGPT model any question or provide a prompt for it to complete or produce.
Let’s change the prompt to question the model, “What is the meaning of peaceful life?“
“prompt”: “<prompt text>”, “completion”: “<ideal generated text>”} {“prompt”: “<prompt text>”, “completion”: “<ideal generated text>”}
When this code is run, the model will be asked, “What is the meaning of peaceful life?” It will generate a response based on it’s training and expertise. The response will be printed up for you to see the model’s interpretation of life’s meaning.
Change the prompt to whatever text or question you want to ask the ChatGPT model. Simply substitute your desired text for the prompt variable, and the model will construct a completion or answer depending on that input.
You can also use this program to convert files in CSV, TSV, XLSX, JSON, or JSONL format into a fine-tuning ready dataset.
Fine Tune ChatGPT Model
Fine-tuning is an important stage in the development of ChatGPT models since it allows the pre-trained model to be adapted to specific use situations. Changing the parameters of the model to fit a smaller, more focused dataset allows it to acquire domain-specific expertise.
Start your fine-tuning job using the below command.
openai api fine_tunes.create -t <TRAIN_FILE_ID_OR_PATH> -m <BASE_MODEL>
The BASE_MODEL is some ChatGPT model that you are going to start from (ada, babbage, curie, or davinci).
Preparing your training dataset
When preparing your training data, keep the following points:
Quality: The quality of your training data is critical. You must use high-quality data if you want your fine-tuned model to operate properly. This means that the data should be accurate, complete, and relevant to the task for which ChatGPT is being trained.
Quantity: The amount of training data you have is also essential. In general, the more data you collect, the more accurate your model will be. However, there comes a threshold of diminishing returns where adding more data does not increase the model’s performance considerably.
Format: Your training data must be in a format that the OpenAI API accepts.
The JSON format is recommended by the Chat-GPT3 API docs:
“prompt”: “<prompt text>”, “completion”: “<ideal generated text>”} {“prompt”: “<prompt text>”, “completion”: “<ideal generated text>”}
ChatGPT is a versatile model that may be used for a variety of tasks like as answering queries, summarizing material, and constructing chatbots. To acquire the greatest results for each application, training data and fine-tuning approaches must be tailored accordingly.
Fine Tune ChatGPT Procedure
After preparing the training data, you can start the fine-tuning process by creating a new model with the OpenAI Python module. To get started, follow the steps below:
- The openai, json, and time modules are imported.
- The API key is set.
- The training data is loaded from a file.
- Creates a new model based on the base model you want to fine-tune.
- Waits for the model’s fine-tuning to be completed.
- When the fine-tuning is finished, you will see a completion message as mentioned in the below code.
- To run this code, replace the placeholder YOUR_API_KEY with your own API key.
import openai import json import time # Set your API key openai.api_key = “YOUR_API_KEY” # Load your training data with open(“training_data.json”, “r”) as f: training_data = json.load(f) # Initialize a new model with the base model you want to fine-tune model = “text-davinci-002” fine_tuned_model = openai.Model.create( model=model, fine_tune=True, training_data=training_data ) # Wait for the model to finish fine-tuning while fine_tuned_model.status()[“data”][“ready”] is False: time.sleep(30) fine_tuned_model = openai.Model.retrieve(fine_tuned_model.id) print(“Fine-tuning complete”)
Once the above code is executed, you will have a fine-tuned ChatGPT model to utilize for your own purposes.
Deploy Model
This code accomplishes the following:
- The openai module is imported.
- The API key is set.
- The ID of the fine-tuned model is used to create a new model object.
- To create text, sends an API request to the model.
- The response is printed.
# set your API key import openai # Create a new model object using the ID of the fine-tuned model model_id = “YOUR_FINE_TUNED_MODEL_ID” model = openai.Model(model_id) # Make an API request to the model to generate text prompt = “What is the meaning of life?” response = model.generate(prompt) # Print the response print(response.choices[0].text)
To run this code, replace the placeholder YOUR_FINE_TUNED_MODEL_ID with the model id you used before while creating.
You will see the response from the fine-tuned ChatGPT model after running the code.
Note: Please keep in mind that even after significant tuning, the model’s response may not be perfect. To achieve the desired results, you may need to keep fine-tuning the model or tweaking the hyperparameters.
Tips for deploying a fine-tuned ChatGPT model
- As certain that the model is hosted in a dependable environment.
- Keep unauthorized people out of the model.
- Keep an eye on the model’s performance and make changes as appropriate.
Also read: You might also find useful our guide on How to Use GPT-4 and ChatGPT on Azure OpenAI Service
Conclusion
Finally, fine-tuning ChatGPT for a specific use case is an excellent way to modify the model’s capabilities and improve its performance. By training on domain-specific data, the model can give more accurate and contextually relevant responses, improving user experience and enabling smooth interactions in specialized sectors.
Please feel free to share your thoughts and feedback in the comment section below.
Fine Tune ChatGPT for Specific Use Case