How to beginner · 3 min read

How to fine-tune on Google Colab

Quick answer

To fine-tune a model on Google Colab, set up your environment with Python and the OpenAI SDK, upload your training dataset, then use the OpenAI fine-tuning API by calling client.fine_tunes.create() with your dataset. Google Colab provides free GPU resources to accelerate training and testing your fine-tuned model.

PREREQUISITES

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

Setup environment

Start by installing the OpenAI Python SDK and setting your API key as an environment variable in Google Colab.

python

import os
!pip install --upgrade openai
os.environ['OPENAI_API_KEY'] = os.environ.get('OPENAI_API_KEY', '')  # Set your key securely

output

Requirement already satisfied: openai in /usr/local/lib/python3.10/dist-packages (x.x.x)
Requirement already satisfied: requests>=2.20 in /usr/local/lib/python3.10/dist-packages (x.x.x)
...

Step by step fine-tuning

Upload your training data JSONL file to Colab, then run the fine-tuning process using the OpenAI SDK. The example below shows a minimal runnable script.

python

from openai import OpenAI
import os

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

# Upload your training file to Colab, e.g., 'training_data.jsonl'
training_file_path = 'training_data.jsonl'

# Create a file upload to OpenAI
upload_response = client.files.create(
    file=open(training_file_path, 'rb'),
    purpose='fine-tune'
)

file_id = upload_response.id

# Start fine-tuning
fine_tune_response = client.fine_tunes.create(
    training_file=file_id,
    model='gpt-4o',
    n_epochs=4
)

print('Fine-tune job started:', fine_tune_response.id)

output

Fine-tune job started: ft-abc123xyz

Common variations

Use n_epochs to control training iterations.
Switch model to other base models like gpt-4o-mini for faster runs.
Use async calls or polling to check fine-tune status.

python

import time

# Poll fine-tune status
fine_tune_id = fine_tune_response.id
while True:
    status = client.fine_tunes.get(id=fine_tune_id)
    print('Status:', status.status)
    if status.status in ['succeeded', 'failed']:
        break
    time.sleep(30)

output

Status: running
Status: running
Status: succeeded

Troubleshooting tips

If you get quota errors, check your OpenAI usage dashboard.
Ensure your training data is valid JSONL with prompt-completion pairs.
Use smaller datasets or fewer epochs to reduce cost and runtime.

✅

Key Takeaways

Use Google Colab's free GPU to accelerate fine-tuning with OpenAI's API.
Upload your training data as JSONL and create a fine-tune job via client.fine_tunes.create().
Poll the fine-tune job status to know when training completes.
Adjust n_epochs and base model for cost and speed trade-offs.
Validate your dataset format to avoid common errors during fine-tuning.

Verified 2026-04 · gpt-4o, gpt-4o-mini

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