Your AI companion for Hugging Face.

Compare models by their evidence rather than their headline score.

A benchmark result can be useful, but it rarely answers whether a model fits a particular language, latency target, license, hardware constraint, or safety requirement.

Strawberry can read the model cards and relevant open tabs you choose, then lay out the documented evidence, gaps, and decision questions in one comparison.

Review a dataset before its limitations become someone else’s problem.

Dataset names and row counts do not tell a team where the data came from, what it represents, or whether its license and intended use match the proposed project.

Strawberry can prepare a readable dataset due-diligence note from the card, repository, and supporting material currently open.

Leave an experiment record that another researcher can trust.

ML work becomes hard to reproduce when an experiment handoff contains a model name and a conclusion but not the version, settings, evaluation conditions, or caveats that led there.

Strawberry can turn the selected Hugging Face context into a structured note that preserves what was tested and what remains unverified.

Watch a model shortlist at a cadence that fits research, not panic.

A weekly shortlist check is useful for teams evaluating a fast-moving area, because it surfaces changed cards or newly documented results without creating a stream of noisy alerts.

Keep the evaluation rubric in a Hugging Face skill and schedule a weekly routine that produces a private watchlist note. It should identify what changed and what deserves re-review, never alter a repository or publish a conclusion.

Frequently asked questions