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Training Metrics

Track job status, training and validation loss for fine-tuning jobs.

This article explains how to monitor and access training data for fine-tuning jobs in Hyperstack AI Studio. It covers how to check job status, track training and validation loss, and interpret visualizations to evaluate model performance throughout the training process.

Prefer the API?

You can also retrieve training metrics programmatically. See the Get training info API reference for the request format, parameters, and example response.

View Training Metrics​

You can monitor training metrics in Hyperstack AI Studio during and after the fine-tuning process. While training is in progress, metrics are displayed on the model’s detail page. Once training completes, the full set of data becomes available under the Training Metrics tab in the Model Evaluations section.

The Training Metrics page includes the following:

  • Final Metrics – Reports your model’s final training and validation loss values.
  • Hyperparameters Used – Lists the configuration settings used during training, such as learning rate and batch size.
  • Performance Comparison (Start/End) – Summarizes the change in loss values from before and after fine-tuning.
  • Performance Comparison (Loss Chart) – A bar chart showing how much loss decreased through training.
  • Model Performance Over Steps – A line graph tracking the training loss reduction over time.
Interpreting training metrics

See the Interpreting Training Metrics section for guidance on understanding your model’s fine-tuning results.

Monitor Training Metrics During Fine-Tuning​

To view training metrics while a fine-tuning job is in progress, follow these steps:

  1. Navigate to the My Models page and select the fine-tuned model you want to monitor.

  2. During training, real-time progress and metrics are shown directly on the model’s detail page.

Access Metrics After Training Completes​

To review training metrics after a fine-tuning job has completed, follow these steps:

  1. Once training finishes, navigate to the My Models page and select the fine-tuned model you want to monitor.

  2. Under the Model Evaluations section on the model’s detail page, click Training Metrics to access the full set of training results.

Interpreting Training Metrics​

After training completes, the Training Metrics page displays a comprehensive summary of how your model performed during fine-tuning. The metrics and visualizations are organized into several key sections, each helping you assess different aspects of model behavior.

Final Metrics​

  • Training Loss: Indicates how well the model fit your training data. Lower values reflect better performance. In many cases, values below 1.0 suggest strong learning.
  • Validation Loss: Measures how well the model generalizes to unseen data. Ideally, this should be close to the training loss. A large gap between the two may suggest overfitting.

Hyperparameters Used​

These settings define the training configuration and can help explain why the model performed a certain way:

  • Learning Rate: The step size for model weight updates. Typical values are around 0.0001 for stable training.
  • Batch Size: Number of examples processed in one step.
  • Epochs: The number of full passes over the training data. More epochs can improve learning, but excessive values may overfit.
  • Percentage of Dataset for Eval: Fraction of data held out for validation—commonly 5%.
  • LoRA Rank (r): Controls the rank of inserted low-rank adapters. 32–64 is standard for balancing performance and resource usage.
  • LoRA Alpha: A scaling factor for LoRA updates. Larger values increase the effect of the fine-tuned weights.
  • LoRA Dropout: Helps prevent overfitting by adding noise. A value of 0.05 is commonly used.
  • Gradient Accumulation Steps: Number of steps before backpropagation.
  • Micro Batch Size: Size of sub-batches within an accumulated step.

Performance Comparison (Start/End of Fine-Tuning)​

This section summarizes the change in loss before and after training:

  • Training Loss Reduction: Indicates how much better the model performs on its training data post-fine-tuning.
  • Validation Loss Reduction: Reflects improved generalization. A strong decrease is desirable.

Performance Comparison (Loss Chart)​

Bar chart showing pre- and post-training loss values:

  • Before Fine-Tuning (Gray): Baseline loss levels.
  • After Fine-Tuning (Purple): Final loss values after model updates.

Interpretation:

  • A visible drop in both bars indicates successful fine-tuning.
  • Minimal change may indicate ineffective training or data mismatch.

Model Performance Over Steps (Loss Curve)​

Line chart visualizing how training loss changed over time:

  • A downward-sloping curve signals successful learning progression.
  • Spikes or instability can suggest noisy data or poor learning rates.
  • A flat or plateauing curve might indicate early convergence or underfitting.
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