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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.

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, Image Editing, and Agent RL (coming soon).

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The jobs list

The Fine-tuning page shows a table of all your training jobs.
ColumnDescription
Fine-tuning jobsJob name and ID. Click the copy icon to copy the job ID.
StatusCurrent state: Queued, Running, Completed, or Failed.
Base modelThe model used as the starting point for training.
DatasetThe training dataset filename and ID.
Create timeWhen the job was submitted.
ActionsAdditional 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.

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Job details

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

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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

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Training metrics

Real-time charts are updated as training progresses.
ChartDescription
LossTraining loss over steps. Includes min/max values, data point count, and EMA smoothing control.
Gradient NormGradient norm over steps.
Learning RateLearning 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.

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Model checkpoints

After training completes, EigenAI saves one checkpoint per epoch in HuggingFace format.
FieldDescription
Epoch NCheckpoint label (e.g., Epoch 1 through Epoch 5).
HuggingFaceFormat of the saved weights.
Files / Size / StepNumber 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 for details.

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Additional files

FileDescription
checkpoint_status.jsonMetadata about checkpoint state.
training.logFull training log file.
Click Download next to either file to save it locally.

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Logs

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