Feature: Improve LoRA Training Graph with Validation-Aware Signals and Best-Checkpoint Selection

[1larity]

Note: This is a personal design / future-work note, not an immediate priority.
Current maintenance and stability issues take precedence.
This document is intended as a reference for improving training UX once the core pipeline is solid.

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Problem

The current LoRA training UI plots only raw training loss (step vs loss).

This is useful for checking optimizer health, but it is insufficient for quality decisions, because:

• Training loss can continue decreasing while model quality plateaus or overfits
• Users cannot easily identify the best checkpoint
• There is no visual signal for when further training has diminishing returns

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Requested Improvements

Graph Metrics Expansion

• Keep raw training loss
• Add smoothed training loss (EMA or moving average)
• Add validation loss (points or curve) at configurable intervals
• Optionally add learning rate curve
  • Secondary y-axis, or
  • Separate subplot
• Add best-checkpoint marker
  • Vertical line and/or highlighted point at best validation step or epoch

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Run Summary Annotations

Display key stats near or overlaid on the graph:

• Latest smoothed training loss
• Best validation loss + step / epoch
• Elapsed time and optional ETA
• Optional run metadata:
  • LoRA rank
  • Learning rate
  • Batch size
  • Dataset name

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Best Checkpoint Handling

• Track best validation metric during training
• Save or copy the best checkpoint to a stable path
  • Example: output/checkpoints/best/
• Continue saving:
  • Periodic epoch checkpoints (epoch_N)
  • Final checkpoint
• Update export flow to allow explicit selection:
  • best
  • latest
  • final
  • manual epoch path

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Auto-Stop Support (Optional)

• Add early stopping based on validation metric:
  • patience
  • min_delta
  • eval_interval
• Keep disabled by default
• Make fully configurable in UI

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Implementation Notes

• Current code already provides:
  • Step / loss
  • Time tracking
  • Periodic checkpointing
• Validation metrics and best-checkpoint tracking are not yet implemented
• For richer overlays, markers, and annotations:
  • Consider migrating from gr.LinePlot to gr.Plot
  • Use Plotly or Matplotlib for multi-layer graphs

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Acceptance Criteria

• Graph displays:
  • Raw training loss
  • Smoothed training loss
  • Validation loss
• Best checkpoint is:
  • Clearly indicated on the graph
  • Saved to a dedicated best path
• User can export or select the best checkpoint directly from the UI
• Early stopping:
  • Works when enabled
  • Logs the reason for stopping
• Existing training flow remains fully backward compatible when new options are disabled

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[jayvenn21]

I'll tackle this and open a PR for this now that the beta version has been released. Feel free to give any feedback along the way.