With the rapid advancements in AI technology, it has become easier for individuals to build their own GPT chatbots. OpenAI’s pre-trained generative transformer models – the engine behind ChatGPT – have become a go-to resource for those looking to build their own AI agents and software.
Learning how to customize your own GPT agent allows you to leverage today’s most advanced technology for your specific use case. So let’s get started.
What is a GPT model?
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| What is a GPT model? |
A GPT (Generative Pre-trained Transformer) model is a type of advanced language model developed by OpenAI. It uses deep learning techniques to understand and generate human-like text.
GPT models are trained with large amounts of text data to predict the next word in a sequence, allowing them to perform tasks like answering questions, writing content, and even generating code. These models are widely used in applications like AI chatbots, content creation, and translation.
GPT has been used in the real world as the engine behind customer support chatbots, lead generation agents, and research tools across a variety of disciplines. These AI chatbots can be found everywhere online, from healthcare and e-commerce to hotels and real estate.
Read: How Accurate is ChatGPT?
Can I train my own ChatGPT model?
Training a GPT model is a labor-intensive and resource-intensive task. Typically, you need to have a well-funded team behind you — such as a research institute, a well-funded company, or even a university — to have the resources needed to train a GPT model.
However, it’s much easier for individuals or companies to train their own GPT chatbots. By training a GPT chatbot instead of a model, you get all the powerful capabilities of a GPT model, but can easily customize it to your own needs.
How are GPT models trained?
To train your own GPT model, you need to be prepared – financially and otherwise – to use powerful hardware and invest a lot of time in fine-tuning the algorithm.
GPT models are born from pre-training, and can be further customized with fine-tuning. However, you can also create custom GPT chatbots that don’t involve fine-tuning, which is an intensive process that can quickly become expensive.
1. Pre-training
Pre-training is a time- and resource-intensive process that – for now – can only be accomplished by well-funded companies. If you’re building your own GPT chatbot, you won’t be doing any pre-training.
Pre-training occurs when a development team trains a model to accurately predict the next word in a human-sounding sentence. Once a model is trained on a large amount of text, it can more accurately predict which word should follow which word in a sentence.
A team starts by collecting a very large dataset. The model is then trained to break down the data by dividing the text into words or subwords, known as tokens. This is where the ‘T’ in GPT comes into play: this text processing and deciphering is done by a neural network architecture called a transformer.
At the end of the pre-training phase, the model understands language broadly, but is not specialized in any particular domain.
2. Fine-tuning
If you’re a company with a very large dataset at your fingertips, fine-tuning may be in order. Fine-tuning is training a model on a specific dataset, so that it becomes a specialist in a particular function.
You can train it:
- Medical texts, so it can better diagnose complex conditions
- Legal texts, so it can write better legal briefs in a particular jurisdiction
- Customer service scripts, so it knows what types of issues your customers tend to have
After fine-tuning, your GPT chatbot is powered by the language skills it acquired in pre-training, but also specialized for your specific use case. But fine-tuning is not the right process for many GPT chatbot projects. You don’t need to do fine-tuning if you’re trying to customize your chatbot.
In fact, you can only fine-tune a GPT chatbot if you have a very large dataset with relevant information (such as customer service call transcripts for a large company). If your dataset isn’t big enough, there’s no point in spending time or money on fine-tuning it.
Fortunately, advanced hints and Retrieval-Augmented Generation (RAG) are almost always enough to fine-tune a GPT chatbot — even if you deploy it to thousands of customers.
How to customize LLM models?
Whether it’s a GPT engine or not, customizing an LLM has many benefits. It can keep your data private, reduce costs for specific tasks, and improve the quality of answers in your use cases. Botpress software engineer Patrick explains the ins and outs of customizing an LLM in this article. Here are his top tips for customizing an LLM:
1. Fine-tuning
Fine-tuning involves training a model with specific examples to make it excel at a specific task, such as answering questions about your product.
While open-source models require engineering capacity for fine-tuning, closed-source models like GPT-4 or Claude can be fine-tuned via APIs, although this increases costs. Fine-tuning is useful for static knowledge but is not ideal for real-time information updates.
2. RAG
Retrieval-Augmented Generation (RAG) refers to using external information, such as HR policy documents, to answer specific questions. This is ideal for accessing real-time information, such as a chatbot checking a product catalog for stock, and avoids the need to fine-tune the model.
RAGs are often easier and more cost-effective to manage for knowledge-based chatbots, as you can request the latest data without constantly updating the model.
3. N-shot hinting
N-shot learning refers to providing examples in a single LLM API call to improve the quality of the response. A providing a single example (one-shot) significantly improves the answer compared to providing no example (zero-shot), while using multiple examples (n-shot) further improves accuracy without changing the model.
However, this approach is limited by the size of the model’s context, and frequent use can increase costs; fine-tuning can eliminate the need for an nth shot example, but requires more setup time.
4. Fast engineering
There are other fast engineering techniques, such as chaining of reasoning, which forces the model to think hard before providing an answer. This improves the quality of the response, but at the expense of response length, cost, and speed.
Conclusion
Generative Pre-trained Transformer (GPT) itself is a transformer model that is quite powerful in managing Natural Language Processing (NLP). It turns out that Google is trying to develop Bidirectional Encoder Representations from Transformers (BERT) which is no less great.
GPT models are a significant milestone in the history of AI development, which is a part of a larger LLM trend that will grow in the future. Furthermore, OpenAI’s groundbreaking move to provide API access is part of its model-as-a-service business scheme.
Additionally, GPT’s language-based capabilities allow for creating innovative products as it excels at tasks such as text summarization, classification, and interaction. GPT models are expected to shape the future internet and how we use technology and software. Building a GPT model may be challenging, but with the right approach and tools, it becomes a rewarding experience that opens up new opportunities for NLP applications.
