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). |
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. |
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. |
- 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 for details.
Additional files
| File | Description |
|---|---|
checkpoint_status.json | Metadata about checkpoint state. |
training.log | Full training log file. |