# Hugging Face AI Models, Datasets & Trends Scraper (`kravennagen/huggingface-models-scraper`) Actor

Extract trending AI models, LLMs, datasets, download counts, like velocity, tags, and pipeline architectures via direct Hugging Face REST API.

- **URL**: https://apify.com/kravennagen/huggingface-models-scraper.md
- **Developed by:** [Morgane Flamant](https://apify.com/kravennagen) (community)
- **Categories:** AI, Developer tools, Lead generation
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet

## Pricing

from $0.40 / 1,000 extracted hugging face items

This Actor is paid per event. You are not charged for the Apify platform usage, but only a fixed price for specific events.
Since this Actor supports Apify Store discounts, the price gets lower the higher subscription plan you have.

Learn more: https://docs.apify.com/actors/running/actors-in-store.md#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 AI Models, Datasets & Trends Scraper

A high-performance **Apify Actor** built in Python to extract trending AI models, open-source LLMs, datasets, download velocity, upvote counts, task pipeline tags, and architecture metadata directly from Hugging Face's REST APIs.

***

### 🌟 Key Features

- **⚡ Direct REST API**: Hits official Hugging Face API endpoints (`huggingface.co/api/models` and `huggingface.co/api/datasets`) directly. No headless browser overhead (~128MB RAM footprint).
- **🤖 Models & Datasets Support**: Seamlessly switch between extracting machine learning models or open-source datasets.
- **🏷️ Task & Pipeline Filtering**: Filter AI models by task pipeline (e.g., `text-generation`, `text-to-image`, `automatic-speech-recognition`, `zero-shot-classification`).
- **📈 Popularity & Recency Metrics**: Sort records by `downloads`, `likes`, `lastModified`, or `createdAt`.
- **📊 Store-Ready Output**: Automatically pushes Pydantic-validated dataset records with built-in tabular overview support.

***

### 📥 Input Parameters

The Actor accepts the following input settings in JSON format:

| Parameter | Type | Default | Description |
| :--- | :--- | :--- | :--- |
| `resourceType` | `string` | `"models"` | Resource type to scrape: `"models"` or `"datasets"`. |
| `pipelineTag` | `string` | `"text-generation"` | Filter models by task tag (e.g. `text-generation`, `text-to-image`). Leave blank for all. |
| `searchQuery` | `string` | `""` | Optional keyword or architecture filter (e.g. `"qwen"`, `"llama"`, `"whisper"`). |
| `sort` | `string` | `"downloads"` | Metric to sort by: `downloads`, `likes`, `lastModified`, or `createdAt`. |
| `maxItems` | `integer` | `100` | Maximum number of records to retrieve. |

#### Example Input JSON

```json
{
  "resourceType": "models",
  "pipelineTag": "text-generation",
  "searchQuery": "llama",
  "sort": "downloads",
  "maxItems": 50
}
```

***

### 📤 Output Format

Each item pushed to the output dataset follows a structured schema:

```json
{
  "id": "meta-llama/Llama-3.1-8B-Instruct",
  "item_type": "model",
  "author": "meta-llama",
  "repo_name": "Llama-3.1-8B-Instruct",
  "hf_url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct",
  "pipeline_tag": "text-generation",
  "downloads_count": 8421090,
  "likes_count": 4820,
  "library_name": "transformers",
  "tags": ["transformers", "llama", "text-generation", "en", "license:llama3.1"],
  "created_at": "2024-07-23T12:00:00.000Z",
  "last_modified": "2024-08-20T14:30:00.000Z",
  "is_private": false,
  "is_gated": true
}
```

***

### 🚀 Running Locally

#### Step 1: Install Dependencies

```bash
pip install -r requirements.txt
```

#### Step 2: Run the Actor

```bash
python -m src.main
```

***

### 🐳 Docker Support

To build and run containerized:

```bash
docker build -t huggingface-scraper .
docker run -it huggingface-scraper
```

# Actor input Schema

## `resourceType` (type: `string`):

Scrape Models or Datasets.

## `pipelineTag` (type: `string`):

Filter models by task (e.g. 'text-generation', 'text-to-image', 'automatic-speech-recognition'). Leave blank for all.

## `searchQuery` (type: `string`):

Optional keyword or architecture filter (e.g. 'qwen', 'llama', 'whisper').

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

Order records by popularity or recency.

## `maxItems` (type: `integer`):

Maximum number of models or datasets to extract.

## Actor input object example

```json
{
  "resourceType": "models",
  "pipelineTag": "text-generation",
  "sort": "downloads",
  "maxItems": 100
}
```

# Actor output Schema

## `results` (type: `string`):

Structured Hugging Face dataset

# 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 = {
    "maxItems": 100
};

// Run the Actor and wait for it to finish
const run = await client.actor("kravennagen/huggingface-models-scraper").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 = { "maxItems": 100 }

# Run the Actor and wait for it to finish
run = client.actor("kravennagen/huggingface-models-scraper").call(run_input=run_input)

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

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

```

## CLI example

```bash
echo '{
  "maxItems": 100
}' |
apify call kravennagen/huggingface-models-scraper --silent --output-dataset

```

## MCP server setup

```json
{
    "mcpServers": {
        "apify": {
            "type": "http",
            "url": "https://mcp.apify.com/?tools=fetch-actor-details,kravennagen/huggingface-models-scraper"
        }
    }
}

```

The hosted server signs you in with OAuth on first connect, so no API token belongs in this config. Clients without OAuth support can send an `Authorization: Bearer <APIFY_API_TOKEN>` header instead, using a token from API & Integrations in Apify Console (https://console.apify.com/settings/integrations).

## OpenAPI specification

Download the OpenAPI definition: https://api.apify.com/v2/actors/UjvCBYkfeajrbzDLU/builds/Brx0X79IPAniCOoQb/openapi.json