# Hugging Face Models, Datasets & Spaces Scraper (`scrapers_lat/huggingface-models-scraper`) Actor
Scrape Hugging Face Hub models, datasets and spaces with downloads, all-time downloads, likes, trending score, task, library, license, author, base model and dates. Track trending AI models and prolific authors. Export to JSON, CSV or Excel.
- **URL**: https://apify.com/scrapers\_lat/huggingface-models-scraper.md
- **Developed by:** [Scrapers Lat](https://apify.com/scrapers_lat) (community)
- **Categories:** Developer tools, Business, AI
- **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks
- **User rating**: No ratings yet
## Pricing
from $6.80 / 1,000 results
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/platform/actors/running/actors-in-store#pay-per-event
## What's an Apify Actor?
Actors are a software tools running on the Apify platform, for all kinds of web data extraction and automation use cases.
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.
In JavaScript/TypeScript projects, use official [JavaScript/TypeScript client](https://docs.apify.com/api/client/js/docs.md):
```bash
npm install apify-client
```
In Python projects, use official [Python client library](https://docs.apify.com/api/client/python/docs.md):
```bash
pip install apify-client
```
In shell scripts, use [Apify CLI](https://docs.apify.com/cli/docs.md):
````bash
# MacOS / Linux
curl -fsSL https://apify.com/install-cli.sh | bash
# Windows
irm https://apify.com/install-cli.ps1 | iex
```bash
In AI frameworks, you might use the [Apify MCP server](https://docs.apify.com/integrations/mcp.md).
If your project is in a different language, use 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 Models, Datasets & Spaces Scraper
> Track trending AI models, datasets and spaces from the Hugging Face Hub with downloads, likes, trending score, task, library, license and author. Filter by keyword or task and export clean structured data for research, sourcing and lead generation.
Downloads & likes plus trending score
Task, library & license per model
JSON / CSV / Excel output formats
### What you get
Each record is one model, dataset or space, ready for a dashboard, a market map or a spreadsheet:
- **id** and **url**: the full identifier and a direct link
- **modelName** and **author**: the item name and the author or organization behind it
- **pipelineTag**: the task, for example `text-generation`, `text-to-image` or `automatic-speech-recognition`
- **library**: the framework or SDK, for example `transformers`, `diffusers` or `gradio`
- **downloads**: downloads over the last 30 days
- **downloadsAllTime**: cumulative downloads since publication
- **likes**: community likes
- **trendingScore**: how much momentum the item has right now
- **license**: the declared license, for example `apache-2.0`, `mit` or `llama3.1`
- **baseModel**: the parent model a fine-tune is derived from, when available
- **gated**: whether access is gated
- **createdAt** and **lastModified**: publication and last update dates
- **tags**: the full tag list (languages, tasks, datasets, arXiv links and more)
- **observedAt**: freshness
### Who is it for
| Use case | Who benefits |
|---|---|
| AI market research | Analysts tracking which models and tasks are gaining traction |
| Model discovery | Engineers finding the best model for a task, ranked by downloads and likes |
| Lead generation | Teams identifying prolific authors and organizations in the AI space |
| Trend monitoring | Builders watching trending score to catch fast-rising models early |
### How to use it
1. Choose **what to scrape**: models, datasets or spaces.
2. Optionally add a **search keyword** (for example `llama`, `whisper`, `stable-diffusion`) and, for models, a **task / pipeline tag**.
3. Pick a **sort** (downloads, likes, trending or recently modified), set **Max Items** and run. Export as JSON, CSV or Excel, or pull it through the Apify API.
### Frequently Asked Questions
**What is the difference between downloads and downloadsAllTime?**
`downloads` counts downloads over the last 30 days, so it reflects current usage. `downloadsAllTime` is the cumulative total since the item was published.
**Can I get only one task, like text-to-image?**
Yes. For models, set the task / pipeline tag (for example `text-to-image` or `text-generation`) to keep only models for that task.
**What does trending score mean?**
It is a momentum signal that surfaces items gaining attention right now. Sort by trending to see what is rising fastest.
**How fresh is the data?**
Downloads, likes and trending score are read live at run time, so each record reflects the item at the moment of the run (see observedAt).
### Related scrapers
- [GitHub Repositories Scraper](https://apify.com/scrapers_lat/github-repositories-scraper)
- [arXiv Research Papers & Abstracts Scraper](https://apify.com/scrapers_lat/arxiv-papers-scraper)
- [NPM Packages Scraper](https://apify.com/scrapers_lat/npm-packages-scraper)
### More scrapers at scrapers.lat
This actor is built and maintained by [scrapers.lat](https://scrapers.lat), where we publish scrapers for public platforms: finance, news, real estate, jobs, e-commerce and government data. Browse the full catalog or ask us for a custom scraper at [scrapers.lat](https://scrapers.lat).
---
> This actor is an independent tool and has no affiliation with Hugging Face. It only accesses publicly available catalog data. Use the results in accordance with the source's terms.
# Actor input Schema
## `maxModels` (type: `integer`):
Maximum number of models (or datasets/spaces) to collect. Optional.
## `type` (type: `string`):
Choose whether to collect AI models, datasets or spaces from the Hugging Face Hub.
## `searchQuery` (type: `string`):
Optional. Keep only items whose name matches this text (for example 'llama', 'whisper', 'stable-diffusion'). Leave empty to get the global top items by your chosen sort.
## `pipelineTag` (type: `string`):
Optional. Restrict models to one task, for example 'text-generation', 'text-to-image', 'automatic-speech-recognition', 'text-classification', 'image-classification'.
## `sortBy` (type: `string`):
Order results by 30-day downloads, likes, trending score or most recently modified.
## Actor input object example
```json
{
"maxModels": 30,
"type": "models",
"searchQuery": "llama",
"sortBy": "downloads"
}
````
# Actor output Schema
## `results` (type: `string`):
No description
# 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 '' with your token
const client = new ApifyClient({
token: '',
});
// Prepare Actor input
const input = {
"maxModels": 30,
"searchQuery": "llama"
};
// Run the Actor and wait for it to finish
const run = await client.actor("scrapers_lat/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 '' with your token.
client = ApifyClient("")
# Prepare the Actor input
run_input = {
"maxModels": 30,
"searchQuery": "llama",
}
# Run the Actor and wait for it to finish
run = client.actor("scrapers_lat/huggingface-models-scraper").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 '{
"maxModels": 30,
"searchQuery": "llama"
}' |
apify call scrapers_lat/huggingface-models-scraper --silent --output-dataset
```
## MCP server setup
```json
{
"mcpServers": {
"apify": {
"command": "npx",
"args": [
"mcp-remote",
"https://mcp.apify.com/?tools=scrapers_lat/huggingface-models-scraper",
"--header",
"Authorization: Bearer "
]
}
}
}
```
## OpenAPI specification
```json
{
"openapi": "3.0.1",
"info": {
"title": "Hugging Face Models, Datasets & Spaces Scraper",
"description": "Scrape Hugging Face Hub models, datasets and spaces with downloads, all-time downloads, likes, trending score, task, library, license, author, base model and dates. Track trending AI models and prolific authors. Export to JSON, CSV or Excel.",
"version": "0.1",
"x-build-id": "ucR3tdyDGxxFgRESr"
},
"servers": [
{
"url": "https://api.apify.com/v2"
}
],
"paths": {
"/acts/scrapers_lat~huggingface-models-scraper/run-sync-get-dataset-items": {
"post": {
"operationId": "run-sync-get-dataset-items-scrapers_lat-huggingface-models-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
},
"/acts/scrapers_lat~huggingface-models-scraper/runs": {
"post": {
"operationId": "runs-sync-scrapers_lat-huggingface-models-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor and returns information about the initiated run in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK",
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/runsResponseSchema"
}
}
}
}
}
}
},
"/acts/scrapers_lat~huggingface-models-scraper/run-sync": {
"post": {
"operationId": "run-sync-scrapers_lat-huggingface-models-scraper",
"x-openai-isConsequential": false,
"summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.",
"tags": [
"Run Actor"
],
"requestBody": {
"required": true,
"content": {
"application/json": {
"schema": {
"$ref": "#/components/schemas/inputSchema"
}
}
}
},
"parameters": [
{
"name": "token",
"in": "query",
"required": true,
"schema": {
"type": "string"
},
"description": "Enter your Apify token here"
}
],
"responses": {
"200": {
"description": "OK"
}
}
}
}
},
"components": {
"schemas": {
"inputSchema": {
"type": "object",
"properties": {
"maxModels": {
"title": "Max Models",
"minimum": 1,
"maximum": 1000000,
"type": "integer",
"description": "Maximum number of models (or datasets/spaces) to collect. Optional."
},
"type": {
"title": "What to scrape",
"enum": [
"models",
"datasets",
"spaces"
],
"type": "string",
"description": "Choose whether to collect AI models, datasets or spaces from the Hugging Face Hub.",
"default": "models"
},
"searchQuery": {
"title": "Search keyword",
"type": "string",
"description": "Optional. Keep only items whose name matches this text (for example 'llama', 'whisper', 'stable-diffusion'). Leave empty to get the global top items by your chosen sort."
},
"pipelineTag": {
"title": "Task / pipeline tag (models only)",
"type": "string",
"description": "Optional. Restrict models to one task, for example 'text-generation', 'text-to-image', 'automatic-speech-recognition', 'text-classification', 'image-classification'."
},
"sortBy": {
"title": "Sort by",
"enum": [
"downloads",
"likes",
"trending",
"modified"
],
"type": "string",
"description": "Order results by 30-day downloads, likes, trending score or most recently modified.",
"default": "downloads"
}
}
},
"runsResponseSchema": {
"type": "object",
"properties": {
"data": {
"type": "object",
"properties": {
"id": {
"type": "string"
},
"actId": {
"type": "string"
},
"userId": {
"type": "string"
},
"startedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"finishedAt": {
"type": "string",
"format": "date-time",
"example": "2025-01-08T00:00:00.000Z"
},
"status": {
"type": "string",
"example": "READY"
},
"meta": {
"type": "object",
"properties": {
"origin": {
"type": "string",
"example": "API"
},
"userAgent": {
"type": "string"
}
}
},
"stats": {
"type": "object",
"properties": {
"inputBodyLen": {
"type": "integer",
"example": 2000
},
"rebootCount": {
"type": "integer",
"example": 0
},
"restartCount": {
"type": "integer",
"example": 0
},
"resurrectCount": {
"type": "integer",
"example": 0
},
"computeUnits": {
"type": "integer",
"example": 0
}
}
},
"options": {
"type": "object",
"properties": {
"build": {
"type": "string",
"example": "latest"
},
"timeoutSecs": {
"type": "integer",
"example": 300
},
"memoryMbytes": {
"type": "integer",
"example": 1024
},
"diskMbytes": {
"type": "integer",
"example": 2048
}
}
},
"buildId": {
"type": "string"
},
"defaultKeyValueStoreId": {
"type": "string"
},
"defaultDatasetId": {
"type": "string"
},
"defaultRequestQueueId": {
"type": "string"
},
"buildNumber": {
"type": "string",
"example": "1.0.0"
},
"containerUrl": {
"type": "string"
},
"usage": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "integer",
"example": 1
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
},
"usageTotalUsd": {
"type": "number",
"example": 0.00005
},
"usageUsd": {
"type": "object",
"properties": {
"ACTOR_COMPUTE_UNITS": {
"type": "integer",
"example": 0
},
"DATASET_READS": {
"type": "integer",
"example": 0
},
"DATASET_WRITES": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_READS": {
"type": "integer",
"example": 0
},
"KEY_VALUE_STORE_WRITES": {
"type": "number",
"example": 0.00005
},
"KEY_VALUE_STORE_LISTS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_READS": {
"type": "integer",
"example": 0
},
"REQUEST_QUEUE_WRITES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_INTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"DATA_TRANSFER_EXTERNAL_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_RESIDENTIAL_TRANSFER_GBYTES": {
"type": "integer",
"example": 0
},
"PROXY_SERPS": {
"type": "integer",
"example": 0
}
}
}
}
}
}
}
}
}
}
```