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Search ML Models

hf.hub.models
Read-onlyIdempotent

Search over 1 million machine learning models on HuggingFace Hub. Filter by model name, task (like text-generation), or library (like transformers). Retrieve model ID, downloads, likes, and pipeline tag. Results sorted by downloads.

Instructions

Search 1M+ ML models on HuggingFace Hub by name, task (text-generation, image-classification, translation), or library (transformers, diffusers). Returns model ID, downloads, likes, pipeline tag. Sorted by downloads.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
searchYesSearch query — model name or keyword (e.g. "llama", "stable-diffusion", "whisper")
taskNoFilter by ML task: text-generation, image-classification, translation, text-to-image, automatic-speech-recognition, etc.
libraryNoFilter by framework: transformers, diffusers, sentence-transformers, gguf, etc.
limitNoNumber of results (1-20, default 10)

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultNoTool response payload. Shape varies per tool — consult the tool description and inputSchema. May be an object, array, string, or number depending on the upstream provider response.
errorNoPresent only when the call failed. Includes error code, message, request_id, and any provider-specific extras.
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations already indicate read-only, idempotent, non-destructive behavior. The description adds context about sorting by downloads and the scale (1M+ models), but does not detail pagination or limit behavior beyond schema. No contradiction with annotations; value added beyond schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences immediately stating purpose and scope. No filler. Front-loaded with key information.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given an output schema exists (though not provided in this context), the description covers search criteria, return fields, and sorting. It is sufficient for a search tool with 4 well-documented parameters and annotations ensuring safety.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Input schema has 100% description coverage, so the description does not add meaning beyond what is already in the schema. It mentions 'name, task, or library' which maps to parameters but does not provide new details. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Description clearly states the tool searches ML models on HuggingFace Hub, specifying the resource (1M+ models), search criteria (name, task, library), return fields, and sorting. It distinguishes from sibling tools like hf.hub.datasets and hf.hub.model_details.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description tells when to use the tool (searching for models by name/task/library) but does not explicitly mention when not to use it or direct to alternative tools like hf.hub.model_details for detailed info. It is clear but lacks exclusions.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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