Huggingface Tools

Configuration File: huggingface_tools.json Tool Type: Local Tools Count: 3

This page contains all tools defined in the huggingface_tools.json configuration file.

Available Tools

HuggingFace_get_model (Type: BaseRESTTool)

Get detailed metadata for a specific model on HuggingFace Hub by its author and model name. Retur…

HuggingFace_get_model tool specification

Tool Information:

  • Name: HuggingFace_get_model

  • Type: BaseRESTTool

  • Description: Get detailed metadata for a specific model on HuggingFace Hub by its author and model name. Returns comprehensive information including model architecture, tags, download/like counts, creation date, license, widget data for interactive testing, and card metadata. Model IDs on HuggingFace follow the ‘author/model-name’ format; provide the author and model_name as separate parameters. Use HuggingFace_search_models to find model IDs (the ‘id’ field returns ‘author/model-name’).

Parameters:

  • author (string) (required) Model author or organization (e.g., ‘facebook’, ‘openai-community’, ‘google-bert’, ‘meta-llama’)

  • model_name (string) (required) Model name within the author’s namespace (e.g., ‘esm2_t33_650M_UR50D’, ‘gpt2’, ‘bert-base-uncased’)

Example Usage:

query = {
    "name": "HuggingFace_get_model",
    "arguments": {
        "author": "example_value",
        "model_name": "example_value"
    }
}
result = tu.run(query)

HuggingFace_search_datasets (Type: BaseRESTTool)

Search HuggingFace Hub for machine learning datasets by keyword. HuggingFace hosts 100,000+ open …

HuggingFace_search_datasets tool specification

Tool Information:

  • Name: HuggingFace_search_datasets

  • Type: BaseRESTTool

  • Description: Search HuggingFace Hub for machine learning datasets by keyword. HuggingFace hosts 100,000+ open datasets spanning text, image, audio, tabular, and biological sequence data. Returns dataset IDs, authors, download counts, likes, tags (including format, modality, size category), and descriptions. Use this to find training/evaluation datasets for ML models, benchmark datasets, or domain-specific datasets (e.g., protein sequences, medical images, genomics).

Parameters:

  • search (string) (required) Search query for dataset name or topic (e.g., ‘protein sequence’, ‘medical imaging’, ‘sentiment analysis’, ‘genomics’)

  • limit ([‘integer’, ‘null’]) (optional) Maximum number of results to return (default 20, max 1000)

Example Usage:

query = {
    "name": "HuggingFace_search_datasets",
    "arguments": {
        "search": "example_value"
    }
}
result = tu.run(query)

HuggingFace_search_models (Type: BaseRESTTool)

Search the HuggingFace Hub for machine learning models by keyword, task type, or ML framework. Hu…

HuggingFace_search_models tool specification

Tool Information:

  • Name: HuggingFace_search_models

  • Type: BaseRESTTool

  • Description: Search the HuggingFace Hub for machine learning models by keyword, task type, or ML framework. HuggingFace Hub hosts 500,000+ open-source models spanning NLP, computer vision, audio, protein language models, and more. Returns model IDs, download counts, likes, tags, pipeline task, and library information. Filter by pipeline_tag (e.g., ‘fill-mask’, ‘text-classification’, ‘token-classification’, ‘image-classification’, ‘feature-extraction’) and by library (e.g., ‘transformers’, ‘diffusers’, ‘timm’). Use HuggingFace_get_model for full details on a specific model.

Parameters:

  • search (string) (required) Search query for model name, task, or topic (e.g., ‘protein language model’, ‘bert’, ‘image segmentation’, ‘drug discovery’)

  • limit ([‘integer’, ‘null’]) (optional) Maximum number of results to return (default 20, max 1000)

  • pipeline_tag ([‘string’, ‘null’]) (optional) Filter by ML task type. Common values: ‘text-classification’, ‘token-classification’, ‘fill-mask’, ‘text-generation’, ‘image-classification’, ‘object-detection’, ‘feature-extraction’, ‘question-answering’, ‘summarization’, ‘translation’, ‘text-to-image’, ‘automatic-speech-recognition’

  • library ([‘string’, ‘null’]) (optional) Filter by ML framework/library. Common values: ‘transformers’, ‘diffusers’, ‘timm’, ‘sentence-transformers’, ‘spacy’, ‘flair’, ‘allennlp’, ‘adapter-transformers’

Example Usage:

query = {
    "name": "HuggingFace_search_models",
    "arguments": {
        "search": "example_value"
    }
}
result = tu.run(query)