How to train GPT-4 on custom datasets using LlamaIndex

When working with large language models (LLMs) like GPT-4, one of the challenges is to make the model aware of our specific data or context. While fine-tuning the model on a custom dataset would be the ideal solution, OpenAI does not provide public access to the weights of GPT-4 for fine-tuning. However, there’s an alternative approach that enables us to apply our custom datasets to shape the responses generated by GPT-4, and it involves using a tool known as LlamaIndex.

What is LlamaIndex?

LlamaIndex is a Python library engineered to format and index your data in a manner that’s easily digestible for LLMs. It offers features for absorbing your data, converting it into an intermediate representation, indexing our data, and querying this index to extract relevant details.

How to use LlamaIndex with GPT-4

Though LlamaIndex doesn’t offer a direct way to fine-tune GPT-4, it can be partnered with the GPT-4 API to create a sort of pseudo-fine-tuningUnconventional fine-tuning. Here's a high-level overview of how this could work:

Data preparation: Utilize LlamaIndex’s data connectors to intake your custom dataset and arrange it into an intermediate representation that’s easily consumable for LLMs.
Indexing: Deploy LlamaIndex to generate an index of your data. This index can be used to pull out pertinent information based on a query.
Querying: When you aim to generate a response from GPT-4, you can initially use LlamaIndex to query your index and extract relevant information from your custom dataset.
Response generation: Use the retrieved information to construct a context or prompt for GPT-4. This could involve including the retrieved information in the prompt or using it to steer the generation process in some other manner.
Post-processing: Once GPT-4 produces a response, you can utilize LlamaIndex’s post-processing modules to further refine or structure the response.

This method doesn’t involve traditional GPT-4 fine-tuning, as it doesn’t include updating the model’s parameters. However, it does allow you to use your custom dataset to shape the responses produced by GPT-4, which could be seen as a type of “pseudo-fine-tuning”.

Example

Let’s take a quick tour of how you might employ LlamaIndex in combination with the GPT-4 API.

from llama_index import LlamaIndex
from llama_index.node_parser import SimpleNodeParser
from llama_index import SimpleDirectoryReader
from llama_index import VectorStoreIndex
import openai
# Initialize LlamaIndex
llama = LlamaIndex()
# Connect to your data source
llama.connect_data_source("your_data_source")
# Load in the Documents
documents = SimpleDirectoryReader('./data').load_data()
# Parse the Documents objects into Node objects
parser = SimpleNodeParser()
nodes = parser.get_nodes_from_documents(documents)
# Construct Index from Nodes
index = VectorStoreIndex(nodes)
# Define your query
query = "your_query"
# Query the index
query_engine = index.as_query_engine()
retrieved_nodes = query_engine.query(query)
# Construct a context or prompt for GPT-4 based on the retrieved nodes
prompt = "In a world where " + ' '.join([node.text for node in retrieved_nodes])
# Initialize OpenAI API
openai.api_key = 'your-api-key'
# Generate a response from GPT-4 based on the prompt
response = openai.Completion.create(
  engine="text-davinci-002",
  prompt=prompt,
  temperature=0.5,
  max_tokens=60
)
# Print the response
print(response.choices[0].text.strip())

Note: In this piece of code, replace your_data_source, your_query, and your-api-key with your actual data source, query, and OpenAI API key, respectively.

Code explanation

Lines 1–5: Import necessary classes and modules for data management, parsing, indexing, and OpenAI API interaction.
Lines 7–11: Initialize the data management class and connect to a data source.
Lines 13–18: Load, parse, and transform documents from a specified directory into queryable nodes.
Lines 21–28: Create an index from these nodes and query the index for relevant nodes.
Line 31: Construct a GPT-4 model prompt from these nodes.
Line 34: Set the OpenAI API key.
Lines 37-42: Generate a response from the GPT-4 model using the constructed prompt.
Line 45: Print the response, removing extra whitespace.

Initially, this code uses LlamaIndex to extract relevant information from your custom dataset based on a query. It then forms a prompt for GPT-4 using the retrieved information and employs GPT-4 to create a response based on this prompt. This allows you to apply your custom dataset to influence the responses produced by GPT-4.

Note: Be aware that this is a conceptual example and does not function as-is. This is because the actual implementation will depend on your specific needs and the characteristics of your custom dataset.

Conclusion

While fine-tuning GPT-4 on a custom dataset is not directly achievable, tools like LlamaIndex provide an alternative that allows us to apply our custom datasets to shape the responses generated by GPT-4. This strategy can be especially beneficial when aiming to develop more context-sensitive and data-driven applications with GPT-4.

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