Find Hugging Face models matching a keyword and/or task.

Use this to discover candidate models. For "what is popular/new right now", prefer get_trending_models instead.

Args: query: Free-text keyword, e.g. "sentiment", "whisper", "code". task: Hugging Face pipeline tag. Must be an exact Hub tag such as: text-classification, token-classification, question-answering, summarization, translation, text-generation, fill-mask, sentence-similarity, feature-extraction, automatic-speech-recognition, text-to-speech, audio-classification, image-classification, object-detection, image-segmentation, text-to-image, image-to-text, tabular-classification, tabular-regression, time-series-forecasting, reinforcement-learning. Leave empty to search all tasks. limit: Maximum number of results (default 10, capped at 50).

Get structured facts about one model: task, license, parameter count, downloads, and any benchmark scores published in its model card.

Use this when the user names a specific model, or to check details before recommending one. For free-text questions about training data, limitations, or intended use, use ask_about_model instead.

Args: model_id: Hugging Face model ID, e.g. "google-bert/bert-base-uncased". Short forms like "bert-base-uncased" are resolved automatically.

Compare 2-6 models side by side on size, license, downloads, and benchmark scores.

Use this whenever the user is choosing between named alternatives — it is cheaper and easier to read than calling get_model_card repeatedly.

Args: model_ids: List of 2 to 6 Hugging Face model IDs.

List models trending on Hugging Face right now, by live trending score.

Use this for "what's popular", "what's new", or "what are people using lately". For a specific keyword or an exhaustive search, use search_models.

Args: task: Optional exact Hub pipeline tag to filter by, e.g. "text-to-image" or "text-generation". Leave empty for all tasks. limit: Maximum number of results (default 10, capped at 50).

Answer a free-text question using the written model card README.

Use this for things only prose documents: training data, intended use, limitations, known biases, evaluation setup, or usage instructions. Returns the most relevant excerpts with their section headings as citations — base the answer only on these excerpts.

For structured facts (license, size, downloads, benchmark numbers), use get_model_card instead; it is cheaper and more reliable for those.

Args: model_id: Hugging Face model ID, e.g. "openai/whisper-large-v3". question: The question to answer, e.g. "what data was it trained on?"