YouTube video (LARRUZ, Aug 27, 2023) announcing and explaining OpenAI's Fine-Tuning API for GPT-3.5 Turbo. The presenter describes what fine-tuning is, its benefits (custom style/tone, shorter prompts, cost and latency savings), practical steps to fine-tune (prepare dataset, upload, set hyperparameters, run training, use model ID), and caveats (need quality data, possible regressions, cost). The video gives examples of domain-specific chatbots (travel, health, music) and compares GPT-3.5 Turbo fine-tuning with GPT-4 (token limits, multimodality, current lack of fine-tuning at time of recording). It highlights concrete numeric claims from the talk (e.g., ~90% prompt size reduction reported by early testers; fine-tuned models handling up to 4,000 tokens; GPT-4 handling up to 8k tokens; GPT-4 unveiled March 14, 2023).
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OpenAI has just released the Fine Tuning API for GPT 3.5 Turbo, the model that powers the free version of ChatGPT. This means that you can now train this model on your own data and make it perform better for your specific use cases. In this video, I'm going to give you a deep dive into the GPT 3.5 turbo fine tuning update and show you why this is such a big deal for chatbot developers and enthusiasts. I I'll also compare it with the upcoming GPT4 fine tuning, which is expected to be available later this year. And of course, I'll share some tips and tricks on how to fine tune your own ChatGPT models and create amazing chatbot experiences for your users. But before we get into that, make sure you hit the subscribe button and the bell icon to get notified whenever I upload a new video, alright? So fine tuning adjusts a neural network like GPT 3.5 Turbo to perform better on specific tasks. If you're building a health chatbot, you'd train GPT 3.5 Turbo on medical data to make its answers more precise. By fine tuning, you can adjust the model's style, tone or format. It can even make the model respond only in German if trained with German data. This process also lets you use shorter prompts to instruct the model. Instead of saying Generate Python code that prints hello World to the console, you could just say Python hello World and get the same result. But now you have saved tokens in your prompt, which means faster API calls and lower costs. OpenAI charges users for the amount of tokens they process in the input prompt as well as the output. A token is a portion of a word. For English words, four or so characters equals one token. So by reducing the prompt size by up to 90%, as some early testers have done, you can save a lot of money and time. Fine tuning also increases the capacity of the model, which means how much text it can handle at once. The fine tuned models can handle up to 4,000 tokens, twice the previous model's capacity, which means that you can provide longer inputs and get longer outputs from the model. Now, fine tuning works best with techniques like crafting good prompts, getting info from outside sources, and using built in tools. Crafting prompts helps the model understand what you want. Getting info means pulling details from places like Bing or Wikipedia. Using tools lets the model do things like making art or searching the web. When you mix all these with fine tuning, your chatbot gets smarter. For instance, it can pull facts from Wikipedia or make A picture based on what you ask. With fine tuning, ChatGPT can sound exactly like your brand, cater directly to user preferences, or zero in on any specific niche you're aiming for. It's like tailoring a suit to fit perfectly. Now think about the power of GPT 3.5 Turbo, which by itself is already a technological marvel. But throw in some fine tuning magic and suddenly we're touching the edges of what GPT4 can do. And for those who might not know, GPT4, unveiled on March 14, 2023, is arguably the most powerful generative AI model out there. It can handle both images and text and powers the premium ChatGPT Plus. But here's the real kick this added power doesn't mean added cost. In fact, it's the opposite. Fine tuning gives quicker responses, lets chat GPT manage bigger chunks of text and navigate through more complex tasks. And the best part? You might find that with a fine tuned GPT 3.5 turbo, there's no immediate need to jump onto the GPT 4 bandwagon. Now isn't that something? So how do you fine tune your Chat GPT models? Well, it's actually quite simple and straightforward. First, get a data set. This should have examples of what you want the chatbot to learn, like customer questions and chatbot answers. Make sure it's in the right format that OpenAI requires. Next, you'll upload this data set to OpenAI's platform. They allow you to upload quite a bit of data, then set up a fine tuning job. This tells OpenAI which model you're working on, which dataset to use, and some settings about how the model should learn. These settings, called Hyper parameters, can be left as they are, or you can adjust them. After setting everything up, OpenAI will start the training. This might be quick or might take some hours, depending on your data and what you're asking the model to do. You can watch how it's doing through OpenAI's tools. Once it's done, your model is ready to go. Use it by referencing its unique ID. And there you have it. Your ChatGPT model is now tailored for your specific needs. But fine tuning your models can be tricky as well. While it's great for enhancing your model for specific tasks, it can also introduce mistakes or weaken its overall performance. Always test thoroughly before going live. Having enough quality data is crucial for the process. And don't forget OpenAI does charge for it. Consider fine tuning when you're clear about your chatbot's purpose, have the right data and aim to give your chatbot a unique identity, but if you're still experimenting, lack the right data, or don't want to invest resources, it might not be the best move. Now, looking at the successes of others, there's a travel chatbot out there trained specifically on travel data. It's fantastic at finding deals and pulling information from various sites. Another chatbot trained on health data offers tailored fitness advice, while a musical one crafts lyrics and even provides artwork for songs. With the right approach, fine tuning can revolutionize the capabilities of chatbots in numerous fields, limited only by what you can imagine. But what about GPT4? How does it compare with GPT 3.5 Turbo in terms of fine tuning? Well, as I mentioned earlier, GPT4 is the most powerful language model that OpenAI released in March this year. It is a multimodal, large language model that can accept image and text inputs and emit text outputs. It is supposed to be more powerful than GPT 3.5 in terms of size, speed and generality. It can process up to 8k tokens at once, twice as much as GPT 3.5, and it can also handle more complex tasks such as image captioning, visual question answering, and text to image generation. And GPT4 has areas that need refining. As of now, it's not available for fine tuning, though OpenAI intends to release this capability later this year. Given that GPT4 can produce inconsistent results and leans more towards recognizing patterns than applying real logic, combined with its high cost, one might wonder, is it worth paying for GPT4 when you can use a fine tuned GPT 3.5 for much less? I'll leave that for you to decide. Regardless, we're eager to see the enhancements when GPT4's fine tuning is introduced, because fine tuning will allow users to adjust GPT4 to their needs like they could with GPT 3.5 Turbo, making it perform better for various tasks. With this, it will be cost effective. By training GPT4 on smaller, specific datasets, users will save money and won't need to seek other expensive alternatives. While we're still learning about the full benefits and potential challenges of fine tuning GPT4, it's evident that fine tuning will shape the future of AI. It will offer a personalized approach to AI, making powerful models more accessible to all. And that's why I'm so excited about the GPT 3.5 turbo fine tuning update and why you should be too. It is a game changer for ChatGPT and for chatbot development in general. It gives you more control, more flexibility, more functionality, more performance, more efficiency and more creativity with your chatbots. So go ahead and try out the fine tuning API for ChatGPT 3.5 Turbo today. You can find all the details and documentation on OpenAI's website. And that's it for this video. I hope you enjoyed it and learned something new. If you did, please give it a thumbs up and leave a comment below. And don't forget to subscribe to my channel for more videos on AI and chatbots. Thank you so much for watching and I'll see you in the next one.
