Master GPT 3.5 Turbo in Python

Master GPT 3.5 Turbo in Python

Table of Contents:

  1. Introduction
  2. Generating the Math Data Set
  3. Converting the Data Set for Fine-Tuning
  4. Uploading the Data Set
  5. Starting a Fine-Tuning Job
  6. Listing Fine-Tuning Jobs
  7. Retrieving the State of a Fine-Tuning Job
  8. Canceling a Fine-Tuning Job
  9. Retrieving Events from a Fine-Tuning Job
  10. Deleting a Fine-Tuned Model
  11. Tracking the Fine-Tuning Job
  12. Conclusion

Introduction

In this article, we will explore the process of fine-tuning GPT 3.5 turbo. We will start by generating a math data set consisting of multiplication problems. Then, we will convert the data set into a format suitable for fine-tuning. Next, we'll learn how to upload our data set and start a fine-tuning job. We'll also cover topics such as listing fine-tuning jobs, retrieving the state of a job, canceling a job, retrieving events, and deleting a fine-tuned model. Throughout the article, we'll track the progress of our fine-tuning job and Create visualizations of the training loss and accuracy.

Generating the Math Data Set

To begin fine-tuning GPT 3.5 turbo, we need a data set. For our demonstration, we will generate a math data set consisting of multiplication problems. We will create problems ranging from single-digit multiplications to four-digit multiplications. The data set will be in the form of a CSV file, containing 1,000 multiplication problems. Each problem will have two operands and the expected result.

To generate the data set, we can use a script or program that dynamically generates these multiplication problems. Once generated, the CSV file can be used for fine-tuning the model.

Converting the Data Set for Fine-Tuning

Before we can use our math data set for fine-tuning, we need to convert it into a format that the fine-tuning job can understand. The required format is a JSONL format, where each line represents a separate data point. We will create a list of messages, where each message consists of a system message indicating it is a multiplication calculator, a user message representing the multiplication question, and an assistant message providing the result.

The conversion process involves extracting the operands and the result from the CSV file and creating JSON objects for each multiplication problem. We will generate 1,000 JSON objects representing our multiplication problems in the required format.

Uploading the Data Set

Once we have our converted data set in the JSONL format, we can upload it to the OpenAI server. We will use the OpenAI API's openai.File.create() method to upload our file.

After uploading the file, it is recommended to save the file ID for future reference. This ID will be necessary for retrieving and managing the file in subsequent steps of the fine-tuning process.

Starting a Fine-Tuning Job

With the data set uploaded, we can now start a fine-tuning job. We will use the OpenAI API's openai.FineTuning.create() method to start the job. The method requires specifying the name of the training file, which is the file name on the server.

Once the fine-tuning job is started, it will begin training the GPT 3.5 turbo model for the specified number of epochs. In our case, we will train for three epochs. The training will occur in steps, with each step consisting of training for a certain number of tokens.

Listing Fine-Tuning Jobs

To keep track of all ongoing and completed fine-tuning jobs, we can list the fine-tuning jobs using the OpenAI API's openai.FineTuning.list() method. This method allows us to specify the number of jobs to retrieve.

The list of fine-tuning jobs will include information such as the job ID, training status, and completion status. By retrieving this list, we can monitor the progress of our fine-tuning jobs.

Retrieving the State of a Fine-Tuning Job

We can retrieve the state of a fine-tuning job using the OpenAI API's openai.FineTuning.retrieve() method. This method requires the job ID as input and returns the Current state of the specified job.

By retrieving the state of a fine-tuning job, we can check if it is currently running, completed, or has encountered any errors. This information allows us to monitor and manage our fine-tuning process effectively.

Canceling a Fine-Tuning Job

If necessary, we can cancel a fine-tuning job using the OpenAI API's openai.FineTuning.cancel() method. This method requires the job ID as input and cancels the specified job.

Canceling a fine-tuning job may be required in situations where the job needs to be stopped before completion. By canceling a job, we can free up resources and prevent unnecessary training.

Retrieving Events from a Fine-Tuning Job

We can retrieve events from a fine-tuning job using the OpenAI API's openai.FineTuning.list_events() method. This method requires the job ID as input and allows us to specify the number of events to retrieve.

The events from a fine-tuning job provide information about the progress and status of the training. By retrieving these events, we can track the steps completed, training loss, and token accuracy during the fine-tuning process.

Deleting a Fine-Tuned Model

Once we have completed fine-tuning our model and no longer need it, we can delete the fine-tuned model using the OpenAI API's openai.FineTuning.delete() method. This method requires the fine-tuning ID as input and deletes the specified fine-tuned model.

Deleting a fine-tuned model helps to free up storage space and resources. It is important to note that once a model is deleted, it cannot be recovered.

Tracking the Fine-Tuning Job

Throughout the fine-tuning process, it is important to track the progress of the job. We can implement the tracking using the OpenAI API's openai.FineTuning.retrieve() method and periodically check the state of the job.

By tracking the training loss and token accuracy, we can monitor the model's performance during the fine-tuning process. We can also save the progress information to a CSV file for future analysis and generate real-time graphs to Visualize the training progress.

Conclusion

In this article, we explored how to fine-tune GPT 3.5 turbo. We learned about generating a math data set, converting the data set for fine-tuning, uploading the data set, starting a fine-tuning job, listing fine-tuning jobs, retrieving the state of a job, canceling a job, retrieving events, and deleting a fine-tuned model. We also tracked the progress of our fine-tuning job and created visualizations of the training loss and accuracy.

Fine-tuning GPT 3.5 turbo opens up opportunities for customizing the model to specific tasks and domains. By following the steps outlined in this article, You can successfully fine-tune the model and utilize its enhanced capabilities.

Remember to experiment, iterate, and analyze the results to optimize the performance of your fine-tuned model for your particular use case.

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