AI & Machine Learning · page 13

Enter model and task to get LoRA rank and alpha guidance with capacity tradeoffs.

Enter your GPU VRAM to get feasibility and settings for QLoRA fine-tuning.

Paste dataset description to get quality checks for balance, noise, and leakage.

Enter your labeling task to get a guidelines document with examples and edge rules.

Enter your classes to get a label schema with definitions and boundary rules.

Enter your team setup to get an annotation quality protocol with review loops.

Enter your labeling volume to get an adjudication workflow for conflicting labels.

Enter dataset size and structure to get train, validation, and test split ratios.

Paste label counts to get imbalance ratios, warnings, and visualization-ready tables.

Enter your imbalance ratio to get remedies from resampling to loss weighting.

Enter your strata to get a stratified sampling plan that preserves distributions.

Paste corpus statistics to get a dedup plan with thresholds and expected removals.

Enter your task to get augmentation techniques with applicability and risk notes.

Enter your schema to get a synthetic data plan with diversity and realism checks.

Enter feature risks to get a synthetic edge case spec for hard examples.

Enter your dataset fields to get a scrub workflow for personal data before training.

Enter data sources to get license and consent checks before use in training.

Enter dataset facts to get a data card covering provenance, limits, and intended use.

Enter your run config to get a pre-flight checklist covering seeds, saves, and monitoring.

Enter model size to get starting learning rates, batch sizes, and warmup values.

Enter schedule preference to get warmup and decay plans with visualizable curves.

Paste your training curves to get an overfitting diagnosis with remedies.

Paste your training curves to get an underfitting diagnosis with capacity fixes.

Enter VRAM pressure to get memory-saving options including gradient checkpointing tradeoffs.