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

# Overview

> Fine-tune a base model on your own dataset to adapt it for your use case.

The Fine-tuning page lets you train a base model on your own dataset and deploy the resulting checkpoints as inference endpoints. Three training modes are available: [SFT](./fine-tuning/sft), [Image Editing](./fine-tuning/image-editing), and Agent RL (coming soon).

## The jobs list

The Fine-tuning page shows a table of all your training jobs.

| Column           | Description                                                           |
| ---------------- | --------------------------------------------------------------------- |
| Fine-tuning jobs | Job name and ID. Click the copy icon to copy the job ID.              |
| Status           | Current state: **Queued**, **Running**, **Completed**, or **Failed**. |
| Base model       | The model used as the starting point for training.                    |
| Dataset          | The training dataset filename and ID.                                 |
| Create time      | When the job was submitted.                                           |
| Actions          | Additional controls when available (e.g., cancel a running job).      |

Use the **Search** box to filter by job name, ID, dataset, or creator. Use the **Training types** and **Status** dropdowns to narrow the list further.

## Job details

Click any job in the list to open its detail page.

### Configuration

Shows the full configuration used for the job:

* Status, Training mode, Base model, Training dataset, Evaluation dataset
* Batch size, Learning rate, Epochs, Queue position
* Created / Started / Completed timestamps

### Training metrics

Real-time charts are updated as training progresses.

| Chart         | Description                                                                                     |
| ------------- | ----------------------------------------------------------------------------------------------- |
| Loss          | Training loss over steps. Includes min/max values, data point count, and EMA smoothing control. |
| Gradient Norm | Gradient norm over steps.                                                                       |
| Learning Rate | Learning rate schedule over steps.                                                              |

The latest values are shown as a summary line above each chart (e.g., `loss 0.2466 • grad 1.591 • lr 2.00e-6 @ step 124`). Source data comes from `training.log`.

### Model checkpoints

After training completes, EigenAI saves one checkpoint per epoch in HuggingFace format.

| Field               | Description                                                                           |
| ------------------- | ------------------------------------------------------------------------------------- |
| Epoch N             | Checkpoint label (e.g., Epoch 1 through Epoch 5).                                     |
| HuggingFace         | Format of the saved weights.                                                          |
| Files / Size / Step | Number of files, total size, and the training step at which the checkpoint was saved. |

Each checkpoint has two buttons:

* **Details** — View the full list of files in the checkpoint.
* **Deploy** — Create an inference deployment directly from this checkpoint to test the training results. See [Deployments](/platform/deployments) for details.

### Additional files

| File                     | Description                      |
| ------------------------ | -------------------------------- |
| `checkpoint_status.json` | Metadata about checkpoint state. |
| `training.log`           | Full training log file.          |

Click **Download** next to either file to save it locally.

### Logs

The **Logs** section displays the last 200 lines of real-time training output. Click **Refresh** to fetch the latest lines.