How to beginner · 3 min read

How to use OpenAI fine-tuning UI

Quick answer

Use the OpenAI fine-tuning UI by uploading your training data in JSONL format, then create and monitor fine-tuning jobs directly on the platform. You can also use the OpenAI Python SDK to upload files, start fine-tuning jobs, and query your custom fine-tuned models programmatically.

PREREQUISITES

Python 3.8+
OpenAI API key (free tier works)
pip install openai>=1.0

Setup

Install the official openai Python package and set your API key as an environment variable.

bash

pip install openai>=1.0

output

Collecting openai
  Downloading openai-1.x.x-py3-none-any.whl (xx kB)
Installing collected packages: openai
Successfully installed openai-1.x.x

Step by step

This example shows how to upload a training file, create a fine-tuning job, monitor its status, and query the fine-tuned model using the OpenAI Python SDK.

python

import os
from openai import OpenAI
import time

client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])

# Step 1: Upload training data (JSONL format)
with open("training_data.jsonl", "rb") as f:
    training_file = client.files.create(file=f, purpose="fine-tune")
print(f"Uploaded file ID: {training_file.id}")

# Step 2: Create fine-tuning job
job = client.fine_tuning.jobs.create(training_file=training_file.id, model="gpt-4o-mini-2024-07-18")
print(f"Created fine-tuning job ID: {job.id}")

# Step 3: Poll job status until done
while True:
    status = client.fine_tuning.jobs.retrieve(job.id)
    print(f"Job status: {status.status}")
    if status.status in ["succeeded", "failed"]:
        break
    time.sleep(10)

if status.status == "succeeded":
    fine_tuned_model = status.fine_tuned_model
    print(f"Fine-tuned model ready: {fine_tuned_model}")

    # Step 4: Query the fine-tuned model
    response = client.chat.completions.create(
        model=fine_tuned_model,
        messages=[{"role": "user", "content": "Hello, how are you?"}]
    )
    print("Response:", response.choices[0].message.content)
else:
    print("Fine-tuning job failed.")

output

Uploaded file ID: file-abc123xyz
Created fine-tuning job ID: ftjob-xyz789abc
Job status: running
Job status: running
Job status: succeeded
Fine-tuned model ready: gpt-4o-mini-2024-07-18-ft-abc123
Response: Hello! I'm your fine-tuned assistant. How can I help you today?

Common variations

You can use asynchronous calls with asyncio for non-blocking status polling. Also, you can fine-tune different base models by changing the model parameter. The OpenAI UI also supports drag-and-drop file uploads and job monitoring without code.

python

import asyncio
import os
from openai import OpenAI

async def monitor_fine_tune(job_id: str):
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    while True:
        status = client.fine_tuning.jobs.retrieve(job_id)
        print(f"Job status: {status.status}")
        if status.status in ["succeeded", "failed"]:
            return status
        await asyncio.sleep(10)

async def main():
    client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
    with open("training_data.jsonl", "rb") as f:
        training_file = client.files.create(file=f, purpose="fine-tune")
    job = client.fine_tuning.jobs.create(training_file=training_file.id, model="gpt-4o")
    print(f"Started job {job.id}")
    status = await monitor_fine_tune(job.id)
    if status.status == "succeeded":
        print(f"Fine-tuned model: {status.fine_tuned_model}")

import asyncio
asyncio.run(main())

output

Started job ftjob-xyz789abc
Job status: running
Job status: running
Job status: succeeded
Fine-tuned model: gpt-4o-ft-xyz789abc

Troubleshooting

If you see Invalid file format, ensure your training data is in valid JSONL with {"messages": [...]} structure.
If the job fails, check the error message in the job details on the UI or via API.
API rate limits can cause errors; retry with exponential backoff.

✅

Key Takeaways

Use the OpenAI fine-tuning UI to upload JSONL training data and manage jobs visually.
Programmatically upload files and create fine-tuning jobs with the OpenAI Python SDK using client.files.create and client.fine_tuning.jobs.create.
Poll job status until completion before querying your custom fine-tuned model.
Async polling and different base models are supported for flexible workflows.
Validate your training data format to avoid common errors during fine-tuning.

Verified 2026-04 · gpt-4o-mini-2024-07-18, gpt-4o

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