Fine-Tuning GPT-4o Mini

When to Fine-Tune

Fine-tune when:

Need consistent output format (JSON schema, specific style)

Domain knowledge not in base model

Millions of API calls (85% cost reduction vs GPT-4o)

Need faster inference with specialized model

Do NOT fine-tune when:

Good system prompt already works

Fewer than 50 quality training examples

Cost

Training: $0.003 per 1K tokens Inference: same as gpt-4o-mini base rate Break-even vs GPT-4o: 85% cost reduction at inference

Training Data (JSONL)

Each line is one JSON object with messages array:

system: your custom instructions

user: example input

assistant: ideal output

Rules: minimum 10 examples (ideally 50-100+), vary inputs, show exact behavior you want.

Training Code

python
from openai import OpenAI
client = OpenAI(api_key="your-key")
Upload file
with open("training.jsonl", "rb") as f:
    upload = client.files.create(file=f, purpose="fine-tune")
Create job
job = client.fine_tuning.jobs.create(
    training_file=upload.id,
    model="gpt-4o-mini-2024-07-18",
    hyperparameters={"n_epochs": 3}
)
Check status
status = client.fine_tuning.jobs.retrieve(job.id)
print(status.fine_tuned_model)  # Use this model ID when ready

Real Benchmarks

Customer service (100 examples):

Tone compliance: 60% to 95%

Required elements: 45% to 88%

JSON extraction (50 examples):

Schema compliance: 70% to 99%

Both: 85% cost reduction vs GPT-4o