README.md

opentrain

🚂 Fine-tune OpenAI models for text classification, question answering, and more

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opentrain is a simple Python package to fine-tune OpenAI models for task-specific purposes such as text classification, token classification, or question answering.

More information about OpenAI Fine-tuning at https://platform.openai.com/docs/guides/fine-tuning.

💻 Usage

📦 Data management

import openai
from opentrain import Dataset

openai.api_key = "<ADD_OPENAI_API_KEY_HERE>"

dataset = Dataset.from_file("data.jsonl")
dataset.info
dataset.download(output_path="downloaded-data.jsonl")

🦾 Fine-tune

import openai
from opentrain import Train

openai.api_key = "<ADD_OPENAI_API_KEY_HERE>"

trainer = Train(model="ada")
trainer.train(
    [
        {
            "prompt": "I love to play soccer ->",
            "completion": " soccer",
        },
        {
            "prompt": "I love to play basketball ->",
            "completion": " basketball",
        },
    ],
)

🤖 Predict

import openai
from opentrain import Inference

openai.api_key = "<ADD_OPENAI_API_KEY_HERE>"

predict = Inference(model="ada:ft-personal-2021-03-01-00-00-01")
predict.predict("I love to play ->")

⚠️ Warning

Fine-tuning OpenAI models via their API may take too long, so please be patient. Also, bear in mind that in some cases you just won't need to fine-tune an OpenAI model for your task.

To keep track of all the models you fine-tuned, you should visit https://platform.openai.com/account/usage, and then in the "Daily usage breakdown (UTC)" you'll need to select the date where you triggered the fine-tuning and click on "Fine-tune training" to see all the fine-tune training requests that you sent.

Besides that, in the OpenAI Playground at https://platform.openai.com/playground, you'll see a dropdown menu for all the available models, both the default ones and the ones you fine-tuned. Usually, in the following format <MODEL>:ft-personal-<DATE>, e.g. ada:ft-personal-2021-03-01-00-00-01.