# Fine Tuning (OpenAI)
This guide shows how to fine-tune a model within **RagaAI Catalyst** using its built-in **Fine Tuning Enabler**. The Fine Tuning Enabler orchestrates the necessary dataset formatting, job creation, and interaction with third-party platforms (such as OpenAI) under the hood, so you can focus on defining your job configurations and data mappings. Refer to the annotated screenshots below for a visual walkthrough.
### Fine Tuning Enabler
The **Fine Tuning Enabler** is RagaAI Catalyst’s behind-the-scenes orchestrator for:
* **Dataset Conversion**: Automatically transforms your dataset into the structure required by the selected platform.
* **Job Creation**: Safely uploads files and initiates fine-tuning jobs with your chosen hyperparameters.
* **Progress Tracking**: Polls job status from the provider and updates Catalyst in near real-time.
* **Result Registration**: Stores the final fine-tuned model reference so you can easily query or test it in the Playground.
You do not need to perform any extra steps to use the Fine Tuning Enabler; simply provide the correct configurations in the UI, and it handles the rest.
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### 1. Accessing the Fine-Tuning Tab
1. In the Catalyst sidebar, click **Finetuning**.
2. If you have not created any jobs yet, you will see **“No Job found.”**
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### 2. Creating a New Job
1. Click **+ New Job** (see bottom-right corner in the “No Job found” page).
2. A dialog box titled **“Create New Job”** appears.
3. Provide a **Job Name** (such as `New_job`) and click **Create**.
> **Note**\
> After you click **Create**, you will be taken to the **Job Detail** page where you can configure your fine-tuning parameters.
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### 3. Selecting a Dataset
Once you’re on the **Job Detail** page:
1. Use the **Select Dataset** dropdown at the top-left of the main area to choose from your project’s available datasets (e.g., `new_test_data`, `pii_test_v`).
2. If your project has many datasets, use the search bar within the dropdown to quickly locate the one you need.
> **Important**\
> The chosen dataset’s schema and rows will be used to create prompts, contexts, and responses during fine-tuning. Make sure your dataset is structured appropriately (e.g., columns that can map to prompt, instructions, or answer text).
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### 4. Configuring Hyperparameters
On the **right-hand side** of the Job Detail page:
1. Under **Platform**, choose your provider (e.g., **OpenAI**).
2. Set your training config values:
* **Epoch**: Number of training epochs.
* **Learning Rate**: Learning rate for gradient updates.
* **Batch Size**: Size of mini-batches during training.
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### 5. Template and Preview
The **middle panel** displays a **Template** section, illustrating how your dataset columns will map to:
* **System** (overall instructions)
* **User** (prompt)
* **Assistant** (desired response)
You can also see a short **Result** summary at the bottom:
* **JobID** (placeholder if not yet created)
* **Model** (to be determined once you run the job)
* **Status** (e.g., `Draft`, `Running`, or `Succeeded`)
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### 6. Executing the Fine-Tuning Job
1. Review your chosen dataset, hyperparameters, and the template.
2. Click **Execute Job** (in the bottom-right corner of the middle panel).
3. If needed, **Save Changes** (top-right corner) to preserve your configuration before or after execution.
Once executed, the **Fine Tuning Enabler** will:
* Convert your dataset into the appropriate JSONL or other required format.
* Upload it securely to the selected platform (e.g., OpenAI).
* Create the corresponding Fine-Tuning job on that platform.
* Poll for job status, updating it in RagaAI Catalyst.
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### 7. Monitoring Job Status
1. Navigate back to **Finetuning** from Job Status Page
2. Each job card displays whether it is **Running**, **Succeeded**, or **Failed**.
3. Click on a job card to view detailed logs, error messages (if any), and final results (fine-tuned model ID).
> **Tip**\
> Once your job succeeds, you can reference the newly fine-tuned model in **Playground** or other parts of RagaAI Catalyst.
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