How can I fine-tune GPT-3.5-Turbo using 10,000 Documents for a Q&A System?

Below is a summary addressing your questions:

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1. Is it possible to fine-tune gpt-3.5-turbo?

Yes. OpenAI supports fine-tuning for certain versions of gpt-3.5-turbo (for example, gpt-3.5-turbo-0125, gpt-3.5-turbo-1106, and gpt-3.5-turbo-0613). You can create a fine-tuned model from these versions using our fine-tuning API.

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2. Using Fine-Tuning to Answer Questions from 10,000 Documents

While you can fine-tune a model on custom data (including examples derived from your documents), the approach matters:

• Fine-Tuning Approach:
  Fine-tuning is usually effective when you have well-defined prompt–completion pairs. For instance, you could derive Q&A pairs from your documents. However, if you aim to recreate a system where the model “knows” the content of 10,000 documents to answer arbitrary queries, fine-tuning alone might not be ideal. Fine-tuning is best suited when the training data represents the input–output mapping you want the model to learn.

• Retrieval-Augmented Generation (RAG):
  Often, for large document collections, a better approach is to use a retrieval system (i.e., build an index/embedding-based search) over the documents and then feed relevant passages as context to the model at inference time. This is known as retrieval-augmented generation and avoids the need to “memorize” in the model via fine-tuning.

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3. How Do You Feed the Documents? Using the Fine-Tuning API?

If you choose to fine-tune, you must prepare your documents as training examples. This means:

• Data Curation:
  You need to convert your documents into a set of examples that have a clear prompt and desired output. For Q&A tasks, that might mean generating a prompt (e.g., a question derived from a document) and a completion (the answer).

• Formatting Requirements:
  The training data must be formatted as a JSONL file where each line is a training example. Every example should follow the guidelines for input and output formatting.

• Uploading and Starting a Fine-tuning Job:
  Once your examples are ready (no matter how many you have), you upload the file and kick off a fine-tuning job via the OpenAI CLI or one of our SDKs. For example:

PYTHONCopied
from openai import OpenAI

client = OpenAI()

# Step 1: Upload your training data file
file = client.files.create(
  file=open("my-training-data.jsonl", "rb"),
  purpose="fine-tune"
)

# Step 2: Create a fine-tuning job
client.fine_tuning.jobs.create(
  training_file=file.id,
  model="gpt-3.5-turbo-0125"  # or another supported version
)

Remember, the quality and format of your examples play a crucial role. With 10,000 documents, it may be more practical to extract relevant Q&A pairs or summaries rather than feeding entire documents directly into the fine-tuning process.

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Summary

• Fine-Tuning gpt-3.5-turbo is possible.
• For a Q&A system spanning 10,000 documents, consider whether you need pure fine-tuning or a hybrid approach with retrieval augmentation.
• If using fine-tuning, prepare your data as prompt–completion pairs in a JSONL file and start a fine-tuning job via the provided API methods.

By carefully curating your training examples (or combining them with retrieval approaches), you can create a model that better addresses your specific use cases.