How to train GPT-4 on custom datasets using LlamaIndex

When working with large language models like GPT-4, one of the biggest challenges is making the model understand our specific context or data. You must be thinking of fine-tuning the model, which is an ideal solution to adapt the model to custom data. But there’s a catch: OpenAI doesn’t provide public access to GPT-4 weights for fine-tuning. So, what’s the alternative? Fortunately, LlamaIndex comes to the rescue. LlamaIndex enables us to train GPT-4 on custom datasets to shape the generated responses. Let’s dive into the details of LlamaIndex and how to use it with GPT-4.

What is LlamaIndex?

LlamaIndex is an advanced framework primarily designed to develop LLM-based applications that utilize diverse data sources. It empowers LLMs like GPT-4 to use custom data, whether PDFs, APIs, or databases, to build task-specific applications like chatbots and agents.

To achieve this, LlamaIndex transforms the data into an intermediate representation that LLMs can easily process. It then creates indexes of the data and queries them to retrieve relevant information.

Fun fact: LlamaIndex, formerly GPT-Index, was founded by Jerry Liu, a renowned AI researcher, in November 2022. Initially developed as an open-source project, LlamaIndex has become a leading LLMs indexing and querying solution.

How to use LlamaIndex with GPT-4

Though LlamaIndex doesn’t offer a direct way to fine-tune GPT-4, it can be used alongside the GPT-4 API to achieve pseudo-fine-tuningUnconventional fine-tuning. Here’s an overview of how this process works:

• Data preparation: First, we use the LlamaIndex’s data connectors to load a custom dataset and convert it into an intermediate representation that LLMs can easily use.

• Indexing: Next, we leverage the LlamaIndex to create an index of the data. This index is then used to extract the relevant information against the query.

• Querying: To generate a response from GPT-4, we use the LlamaIndex to query the index, which extracts the relevant information from the custom dataset.

• Response generation: After extracting the relevant information, we use it to build context for GPT-4. This can be done by adding the information directly to the prompt or using it to guide the response generation process in some other way.

• Post-processing: Once GPT-4 generates the response, we can use LlamaIndex’s post-processing modules to further refine or structure it.

Note: This method doesn’t involve traditional GPT-4 fine-tuning, which updates the model’s parameters to adapt it to custom data. It uses the custom dataset to shape the responses generated by GPT-4, which can be seen as a “pseudo-fine-tuning.”

Example

Let’s use LlamaIndex with the GPT-4 API on a custom dataset. In this example, we’ll use a small dataset about “Haute Couture Atelier,” a brand offering fashion consultation services, to influence the responses generated by GPT-4.

Note: Replace OpenAI API key with your actual OpenAI API key in the code below.

Code explanation

• Lines 1–2: We import SimpleDirectoryReader and VectorStoreIndex classes from the llama_index.core library:

  • SimpleDirectoryReader: Read the documents from a specified directory.

  • VectorStoreIndex: Create an index from the documents for efficient querying.

• Line 3: We import openai for OpenAI API interaction.

• Line 6: We load the document from the data directory using SimpleDirectoryReader.

• Line 9: We initialize the OpenAI API key. Replace the OpenAI API key with your OpenAI API key to execute the code.

• Line 12: We create indexes from the document.

• Lines 15–19: We define the query “What services does Haute Couture Atelier offer?” and use it to extract relevant information from the index in the context variable.

• Lines 22–30: We compile a prompt along with the extracted context and pass it to GPT-4 model to generate final response

• Line 33: We print the response generated by GPT 4.

Conclusion

While fine-tuning GPT-4 on a custom dataset is not directly possible, tools like LlamaIndex offers a powerful alternative. It allow us to use our custom datasets to shape the responses generated by GPT-4. This strategy can be beneficial specially when aiming to create more context-sensitive and data-driven applications with GPT-4.