Release 0.14

Modular foundation model training
- The two components of the pretrain stage — data preprocessing & representation fitting, and FM training — can now be run independently, or together, as before:
  - Two respective functions: fit_behavioral_representation and train_foundation_model were added to compute features and train foundation model separately.
  - Changed monad.pretrain command name to monad.run with optional --fit and --fm arguments to compute only features or foundation model.
Flexible prediction outputs
- Introduced different types of predicted output defined by mandatoryoutput_type parameter. This requires changes to previously written scripts.
- Added functions readout_sketch and read_target_entity_ids to map recommendation outputs to feature values.
Enhanced model training with early stopping
- Introduced early_stopping parameter to prevent overfitting.
Expanded model interpretability
- Introduced interpret_entity function to compute event-level attributions for a single main entity.
Automated model testing
- Introduced test method to compute metrics based on predictions and ground truth.
Flexible BigQuery connection
- Added project_id parameter to define project different from the one in the service account.

Cascading run execution – enables dependent jobs/runs to trigger in sequence.
Run and job status tracking – added detailed status tracking for better monitoring.
New table designs – updated tables with improved layout and readability.
Validation improvements – enhanced input and data validation across the platform.
Multi-GPU training – enabled distributed model training across multiple GPUs.
Listing state restoration – automatically restores UI state when returning to listings.

Fixed an issue where all main entities were loaded on one GPU when distributed training across multiple GPUs was used.
Fixed an issue where default multiclass metric was returning an error.
Fixed an issue where interpretability attributions for time-series features were empty.