> ## Documentation Index
> Fetch the complete documentation index at: https://notes.kodekloud.com/llms.txt
> Use this file to discover all available pages before exploring further.

# Steps Involved in Fine Tuning a Model

> This guide details the process of fine-tuning an OpenAI model, covering data preparation, validation, training, and model usage.

Fine-tuning an OpenAI model involves four core phases: preparing your data, uploading & validating it, launching a training job, and then calling your custom model. This guide walks through each step, from creating a JSONL dataset to selecting a cost-effective base model.

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  ![The image outlines four steps involved in fine-tuning: creating a JSONL dataset, uploading the dataset, starting a fine-tuning job through OpenAI CLI or SDK, and using the fine-tuned model for word/chat completion.](https://kodekloud.com/kk-media/image/upload/v1752881506/notes-assets/images/Mastering-Generative-AI-with-OpenAI-Steps-Involved-in-Fine-Tuning-a-Model/fine-tuning-steps-jsonl-dataset.jpg)
</Frame>

***

## 1. Prepare Your Dataset in JSONL Format

OpenAI fine-tuning requires a line-delimited JSON (JSONL) file. Each line should be a valid JSON object with two keys—`prompt` and `completion`—terminated by a stop token (e.g., `END`).

<Callout icon="lightbulb" color="#1CB2FE">
  Ensure each JSON object is newline-delimited (no commas between lines) and ends with your chosen stop token.
</Callout>

Example `qna.jsonl`:

```jsonl theme={null}
{"prompt": "Question: What is AI? ->", "completion": "AI stands for artificial intelligence.\nEND"}
{"prompt": "Question: Define machine learning ->", "completion": "Machine learning is a subset of AI focused on data-driven models.\nEND"}
```

Fields:

* **prompt**: The user’s input or instruction.
* **completion**: The desired response, ending with `END`.

You can handcraft this file or generate it via scripts or even using a base model like ChatGPT, then export it to `.jsonl`.

***

## 2. Upload & Validate Your JSONL Dataset

Before training, preprocess and upload your dataset using the OpenAI CLI:

```bash theme={null}
pip install --upgrade openai
openai tools fine_tunes.prepare_data -f qna.jsonl
```

This command:

* Checks for JSONL formatting issues.
* Removes duplicate entries.
* Produces a cleaned file named `qna_prepared.jsonl`.

***

## 3. Launch & Monitor the Fine-Tuning Job

Submit the prepared dataset to fine-tune a base model (e.g., `davinci`):

```bash theme={null}
openai api fine_tunes.create \
  -t qna_prepared.jsonl \
  -m davinci
```

You’ll receive a `fine_tune_job_id`. Track progress with:

```bash theme={null}
openai api fine_tunes.follow -i <FINE_TUNE_JOB_ID>
```

Job durations vary based on data size and model choice—it can take minutes or hours.

***

## 4. Query Your New Fine-Tuned Model

After completion, you’ll get a model ID like `davinci:ft-your-org-2024-06-01-00-00-00`. Test it via the Completions API:

```bash theme={null}
openai api completions.create \
  -m <FINE_TUNED_MODEL_ID> \
  -p "Question: What is AI? ->" \
  --stop "END"
```

Use this same pattern for tasks such as summarization, classification, or conversational agents.

***

## 5. Base Model Selection & Cost Comparison

Choosing the right base model balances performance and budget. Review the table below:

| Base Model | Price (1K tokens) | Context Window | Ideal Use Case          |
| ---------- | ----------------- | -------------- | ----------------------- |
| Ada        | \$0.0004          | 2,048 tokens   | Simple classification   |
| Babbage    | \$0.0005          | 2,048 tokens   | Moderate Q\&A           |
| Curie      | \$0.0020          | 2,048 tokens   | Summarization & chat    |
| Davinci    | \$0.0200          | 4,096 tokens   | Complex reasoning tasks |

<Callout icon="triangle-alert" color="#FF6B6B">
  Fine-tuning costs include both training and usage. Always check the [OpenAI pricing page](https://openai.com/pricing) and monitor your token consumption to avoid unexpected charges.
</Callout>

***

## Links and References

* OpenAI Fine-Tuning Guide: [https://platform.openai.com/docs/guides/fine-tuning](https://platform.openai.com/docs/guides/fine-tuning)
* OpenAI Pricing: [https://openai.com/pricing](https://openai.com/pricing)
* JSONL Specification: [https://jsonlines.org/](https://jsonlines.org/)
* OpenAI CLI Reference: [https://github.com/openai/openai-cli](https://github.com/openai/openai-cli)

With this end-to-end workflow—dataset preparation, validation, training, and model invocation—you’re ready to fine-tune any future OpenAI model for your custom use cases.

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