# Hugging Face Model Explorer (`lovely_radiologist/hf-model-explorer`) Actor

Structured export of HF models with task, library, license, download count, and parsed model-card metadata. Built for AI teams doing model selection at scale.

- **URL**: https://apify.com/lovely\_radiologist/hf-model-explorer.md
- **Developed by:** [Vivek Gaur](https://apify.com/lovely_radiologist) (community)
- **Categories:** AI, Other
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $3.00 / 1,000 model extracteds

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.

Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-event

## What's an Apify Actor?

Actors are web data automations that power AI and operations. They run on the Apify platform to scrape websites, process data, connect APIs, and automate workflows.
In Batch mode, an Actor accepts a well-defined JSON input, performs an action which can take anything from a few seconds to a few hours,
and optionally produces a well-defined JSON output, datasets with results, or files in key-value store.
In Standby mode, an Actor provides a web server which can be used as a website, API, or an MCP server.
Actors are written with capital "A".

## How to integrate an Actor?

If asked about integration, you help developers integrate Actors into their projects.
You adapt to their stack and deliver integrations that are safe, well-documented, and production-ready.
The best way to integrate Actors is as follows.

- **AI agents and MCP clients** — the [Apify MCP server](https://docs.apify.com/integrations/mcp.md) at `https://mcp.apify.com` (remote, streamable HTTP, OAuth on first use).
- **Agentic workflows and local Actor development** — [Agent Skills](https://apify.com/.well-known/agent-skills/index.json) with the [Apify CLI](https://docs.apify.com/cli/docs.md): `npm install -g apify-cli`, then `apify login`.
- **JavaScript/TypeScript projects** — the official [JS/TS client](https://docs.apify.com/api/client/js/docs.md): `npm install apify-client`.
- **Python projects** — the official [Python client](https://docs.apify.com/api/client/python/docs.md): `pip install apify-client`.
- **Any other language** — the [REST API](https://docs.apify.com/api/v2.md).

For usage examples, see the [API](#api) section below.

For more details, see Apify documentation as [Markdown index](https://docs.apify.com/llms.txt) and [Markdown full-text](https://docs.apify.com/llms-full.txt).

# README

## Hugging Face Model Explorer

Structured export of Hugging Face models matching search/filter criteria, with full model-card metadata parsed from YAML frontmatter — not raw markdown.

### Why This Over the HF Website?

The HF model hub UI is great for browsing. This Actor is for teams that need to:

- Compare 50+ models programmatically (license check, task type, library)
- Feed model metadata into a selection pipeline or RAG index
- Monitor model downloads/likes over time via scheduled runs

The key differentiator: **parsed model card fields** (license, base model, quantization info) — most scraping attempts just return raw README markdown.

### Input

| Field | Type | Default |
|-------|------|---------|
| `query` | string | — |
| `task` | string | — |
| `library` | string | — |
| `sort` | string | `downloads` |
| `maxResults` | integer | 50 |

### Output

```json
{
  "modelId": "meta-llama/Llama-3.1-8B-Instruct",
  "author": "meta-llama",
  "downloads": 8200000,
  "likes": 12400,
  "task": "text-generation",
  "library": "transformers",
  "license": "llama3.1",
  "lastModified": "2024-07-23T00:00:00Z",
  "tags": ["llama", "facebook", "pytorch"],
  "cardSummary": "The Meta Llama 3.1 collection of multilingual..."
}
```

### Pricing (PPE)

| Event | Price |
|-------|-------|
| `actor-start` | $0.01 |
| `model-item` | $0.015 per model |

### Related Actors

- [HF Dataset Scraper](https://apify.com/TODO) — same pattern for datasets
- [HF Trends & Leaderboard](https://apify.com/TODO) — track what's trending
- [GitHub Org Scraper](https://apify.com/TODO) — explore the orgs behind these models

# Actor input Schema

## `query` (type: `string`):

Search term to filter models by name or keyword.

## `task` (type: `string`):

Pipeline tag filter, e.g. text-generation, image-classification

## `library` (type: `string`):

e.g. transformers, diffusers, sentence-transformers

## `sort` (type: `string`):

Sorting order for the model results.

## `maxResults` (type: `integer`):

Maximum number of models to return.

## Actor input object example

```json
{
  "query": "llama",
  "sort": "downloads",
  "maxResults": 50
}
```

# API

You can run this Actor programmatically using our API. Below are code examples in JavaScript, Python, and CLI, as well as the OpenAPI specification and MCP server setup.

## JavaScript example

```javascript
import { ApifyClient } from 'apify-client';

// Initialize the ApifyClient with your Apify API token
// Replace the '<YOUR_API_TOKEN>' with your token
const client = new ApifyClient({
    token: '<YOUR_API_TOKEN>',
});

// Prepare Actor input
const input = {
    "query": "llama",
    "task": "",
    "library": ""
};

// Run the Actor and wait for it to finish
const run = await client.actor("lovely_radiologist/hf-model-explorer").call(input);

// Fetch and print Actor results from the run's dataset (if any)
console.log('Results from dataset');
console.log(`💾 Check your data here: https://console.apify.com/storage/datasets/${run.defaultDatasetId}`);
const { items } = await client.dataset(run.defaultDatasetId).listItems();
items.forEach((item) => {
    console.dir(item);
});

// 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/js/docs

```

## Python example

```python
from apify_client import ApifyClient

# Initialize the ApifyClient with your Apify API token
# Replace '<YOUR_API_TOKEN>' with your token.
client = ApifyClient("<YOUR_API_TOKEN>")

# Prepare the Actor input
run_input = {
    "query": "llama",
    "task": "",
    "library": "",
}

# Run the Actor and wait for it to finish
run = client.actor("lovely_radiologist/hf-model-explorer").call(run_input=run_input)

# Fetch and print Actor results from the run's dataset (if there are any)
print("💾 Check your data here: https://console.apify.com/storage/datasets/" + run["defaultDatasetId"])
for item in client.dataset(run["defaultDatasetId"]).iterate_items():
    print(item)

# 📚 Want to learn more 📖? Go to → https://docs.apify.com/api/client/python/docs/quick-start

```

## CLI example

```bash
echo '{
  "query": "llama",
  "task": "",
  "library": ""
}' |
apify call lovely_radiologist/hf-model-explorer --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "command": "npx",
            "args": [
                "mcp-remote",
                "https://mcp.apify.com/?tools=lovely_radiologist/hf-model-explorer",
                "--header",
                "Authorization: Bearer <YOUR_API_TOKEN>"
            ]
        }
    }
}

```

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/e7eahoq42bC4gigiN/builds/XbLJyG9RWaivz6LcD/openapi.json