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In this lesson, you’ll learn how to fine-tune an OpenAI model using the CLI. We’ll cover:
  • Preparing your dataset
  • Uploading and formatting data
  • Launching and monitoring a fine-tune job
Although OpenAI is deprecating older fine-tuning models by January 4th and announcing GPT-3.5/GPT-4 support soon, the core workflow remains the same.
Most existing fine-tuning models (e.g., older Curie, Davinci) will retire by January 4th. It’s recommended to wait for GPT-3.5/GPT-4 fine-tuning, but you can continue experimenting with Ada, Babbage, Curie, and Davinci until then.
The image shows a webpage from OpenAI's documentation about fine-tuning models, detailing the deprecation of certain models and the benefits of fine-tuning. It includes a sidebar with navigation links and a highlighted announcement about model updates.
Pricing for fine-tuning varies by model. Ada remains the most cost-effective, while Davinci is the most expensive. Regardless of your budget, the process is identical across models.
The image shows a webpage from OpenAI detailing pricing for fine-tuning and embedding models, including costs for different models like Ada, Babbage, Curie, and Davinci.

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Use Case: State of the Union Q&A Chatbot

We’ll build a simple chatbot that answers questions about President Biden’s February 7, 2023 State of the Union address. Since GPT-3.5’s knowledge cutoff is 2021, it won’t know this speech. We’ll fine-tune using a publicly available summary from the European Parliament.

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1. Prepare the Raw JSONL Dataset

First, convert your prompt/completion pairs into JSON Lines format. Example qna.jsonl: 
{"prompt":"What did President Biden highlight about his interactions with Republicans?","completion":"President Biden highlighted his past interactions with Republicans as opportunities for bipartisan cooperation."}
{"prompt":"What did President Biden emphasize about finding common ground with Republicans?","completion":"President Biden emphasized the importance of finding common ground with Republicans to achieve legislative goals."}
{"prompt":"How did President Biden characterize the potential for compromise with Republicans?","completion":"President Biden characterized the potential for compromise with Republicans as achievable and essential for the country's progress."}
Save qna.jsonl alongside your PDF source: 
(venv) [Demo] > ls
SOTU.pdf  qna.jsonl

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2. Upload and Prepare the Dataset

Use the OpenAI CLI to validate, dedupe, and add separators: 
(venv) [Demo] > openai tools fine_tunes.prepare_data -f qna.jsonl
Analyzing...
- Your file contains 411 prompt-completion pairs
- 116 duplicated rows will be removed
- Prompts will get a suffix separator '\n\n###\n\n'
- Completions will get a suffix ending '***'
- Completions will start with a whitespace
Proceed? [Y/n] Y
> Wrote prepared file to qna_prepared.jsonl
Verify the new file: 
(venv) [Demo] > ls
SOTU.pdf  qna.jsonl  qna_prepared.jsonl

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Sample Entries in qna_prepared.jsonl

{"prompt":"When did Joe Biden deliver his State of the Union address? ->\n\n###\n\n","completion":" Joe Biden delivered his State of the Union address on 7 February 2023.\n END***"}
{"prompt":"To whom did Joe Biden deliver his State of the Union address? ->\n\n###\n\n","completion":" Joe Biden delivered his State of the Union address to a joint session of the 118th US Congress (2023–2024).\n END***"}
{"prompt":"What was the difference in the government's composition between 2022 and 2023? ->\n\n###\n\n","completion":" In 2022, the US had a 'united' government with Democrats holding a majority in both chambers; in 2023, it became 'divided' with a Republican-led House of Representatives.\n END***"}

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3. Create the Fine-Tuning Job

Select a base model (ada, babbage, curie, or davinci) and start fine-tuning: 
(venv) [Demo] > openai api fine_tunes.create \
  -t qna_prepared.jsonl \
  -m davinci
You’ll see upload progress and receive a fine-tune ID: 
Upload progress: 100%|████████████| 78.2k/78.2k [00:00<00:00]
Uploaded file: file-XXXXXXXXXXXX
Created fine-tune: ft-YYYYYYYYYYYY
Streaming events until fine-tuning is complete...

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Model Comparison

ModelCost (fine-tuning)Status
Ada$0.0008 / 1K tokensSupported until Jan 4
Babbage$0.0016 / 1K tokensSupported until Jan 4
Curie$0.0032 / 1K tokensSupported until Jan 4
Davinci$0.012 / 1K tokensSupported until Jan 4

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4. Monitor and Complete

Follow the job status: 
(venv) [Demo] > openai api fine_tunes.follow -i ft-YYYYYYYYYYYY
Example output: 
[2023-08-13 12:16:31] Fine-tune enqueued. Queue number: 0
[2023-08-13 12:16:32] Fine-tune started
[2023-08-13 12:20:49] Completed epoch 1/4
[2023-08-13 12:22:11] Completed epoch 2/4
[2023-08-13 12:23:53] Completed epoch 3/4
[2023-08-13 12:25:24] Completed epoch 4/4
[2023-08-13 12:25:30] Fine-tune succeeded
After completion, note the recommended openai api completions.create command in the CLI output to invoke your fine-tuned model.

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5. Invoke Your Fine-Tuned Model

Once the job succeeds, run: 
openai api completions.create \
  -m davinci:ft-your-org-2023-08-13-06-56-03 \
  -p "When did Joe Biden deliver his State of the Union address?"
The model will respond based on your custom SOTU Q&A dataset. Adjust the model choice and epochs to balance accuracy vs. cost.

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