![]() ![]() Now, let me show you some other jokes generated by GPT3 that may not be on my list of all-time favourite jokes but are worth a look: ![]() The whole code for fine-tuning is available here. You can also experiment with the temperature parameter. You can change max_tokens to the size of the generated text you want. # ft_model should have your model id ft_model = "ada:ft-personal-11-25-19" res = (model=ft_model, prompt="I hate. We will be using that id to call our model. The response message will contain the id of the fine-tuned model. Now that our model has been fine-tuned we can use it for inference. Once the fine-tuning is over, you will see the status changes to processed from processing in the response. Use that for retrieving the status on the fine-tuning. The creation of fine-tuning file will create an id. You can keep checking up on the fine-tuning process using the following command. The fine-tuning will take some time depending on the size of the dataset and the model that you will be using. By default, it runs 4 epochs to fine-tune a model. This can be done using: response = (training_file="YOUR FILE ID", model='ada')Ĭhange the model to babbage or curie if you want better results. Now that our data is in the required format and the file id has been created, the next task is to create a fine-tuning model. #write your file name instead of jokes_prepared.jsonl with open("joke_prepared.jsonl") as f: response = (file=f, purpose='fine-tune') print(response)Ī file id will be available to you. Once the jsonl file has been prepared our next step involves creating a file id from this file. Yes denotes that all options in fine-tuning should be set to true. !yes | openai tools fine_tunes.prepare_data -f 'joke.csv' However, OpenAI has a feature which converts CSV, TSV, XLSX, and JSON to JSONL files. I have tried both ways and I realised that providing some text to the prompt gives better results compared to completion. įor text completion we shall provide it with some prompt text however for generation of text we will leave prompt blank. Ideally, the dataset to fine-tune GPT3 should be a jsonl file that should look like this. I have used a random dataset that scraped some jokes off Reddit. import openai import pandas as pd import string openai.api_key = 'YOUR API KEY' Next, let us start making the required imports. Using the fine-tuned model for inference.The main steps involved in fine-tuning are: I will be using the OpenAI package because it is the easiest. ![]() There are many ways to fine-tune GPT3 : (i) using OpenAI CLI, (ii) using the OpenAI package, and (iii) using requests. Once you have created an account, the next step involves getting your API key. ![]() To start with fine-tuning we need first to create an account in OpenAI. Some other limitations include that one can fine-tune up to 10 models per month and each dataset can be up to 2.5M tokens or 80–100MB in size. The largest model Davinci is still not available for fine-tuning. We will use this feature to fine-tune any of the three models curie, babbage and ada. OpenAI provides $18 worth of free credits when you create an account to get access to GPT3. However, this discussion is for some other time. Some question OpenAI for its biasedness and for not open-sourcing the weights. Even though the model weights have yet not been open-sourced we can at least fine-tune it now based on our dataset. However, even then fine-tuning was not available. Later, the playground access was available for almost everyone. The beta version was available for very few people and organisations. When it was released back in 2020, it was hyped a lot. GPT3 is the new state-of-the-art language model. For more AI-generated jokes scroll to the end of the article where I write some of my favourite jokes generated by GPT3. This is one of the jokes generated by GPT3 after it was fine-tuned on some jokes from Reddit. ![]()
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