# Hugging Face Model Search & Stats Scraper (Free) (`fit_melon/huggingface-model-search-stats`) Actor Search the Hugging Face Hub and export AI model stats as JSON: downloads, likes, task, library, license, base models, datasets and arXiv papers. Track AI model popularity and trends for free. - **URL**: https://apify.com/fit\_melon/huggingface-model-search-stats.md - **Developed by:** [D N](https://apify.com/fit_melon) (community) - **Categories:** AI, Developer tools - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing Pay per usage This Actor is paid per platform usage. The Actor is free to use, and you only pay for the Apify platform usage, which gets cheaper the higher subscription plan you have. Learn more: https://docs.apify.com/platform/actors/running/actors-in-store#pay-per-usage ## 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.md): ```bash npm install apify-client ``` In Python projects, use official [Python client library](https://docs.apify.com/api/client/python.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/platform/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 Model Search & Stats Scraper (Free) Search the **Hugging Face Hub** and export clean, structured stats for every model: **downloads, likes, trending score, task (pipeline tag), library, license, base models, training datasets, arXiv papers, file count and last update date**. Use it to **track AI model popularity**, monitor what a competitor or organization is shipping, benchmark model adoption over time, or build a dataset of the LLM/AI ecosystem. Search by keyword or fetch exact model IDs. This actor is **100% free** — you only pay your own Apify platform usage. ### Input | Field | Type | Default | Description | |---|---|---|---| | `queries` | array of strings | — | Keywords to search the Hub. One item per model found. | | `modelIds` | array of strings | — | Exact repo IDs, e.g. `google-bert/bert-base-uncased`. | | `maxResultsPerQuery` | integer | `10` | Models returned per search query (1–100). | | `sort` | string | `downloads` | `downloads`, `likes`, `lastModified`, `createdAt` or `trendingScore`. | | `direction` | string | `desc` | Sort direction. | | `pipelineTag` | string | `""` | Optional task filter, e.g. `text-generation`. | | `library` | string | `""` | Optional library filter, e.g. `transformers`. | | `author` | string | `""` | Optional author filter, e.g. `mistralai`. | At least one of `queries` or `modelIds` is required. ### Output One dataset item per model, always with an explicit `status` field (`found`, `not_found` or `error`) — the actor never crashes on an unknown model. ```json { "status": "found", "query": "bert", "source": "search", "rank": 1, "modelId": "google-bert/bert-base-uncased", "author": "google-bert", "modelName": "bert-base-uncased", "downloads": 78962390, "likes": 2704, "pipelineTag": "fill-mask", "libraryName": "transformers", "license": "apache-2.0", "languages": ["en"], "datasets": ["bookcorpus", "wikipedia"], "baseModels": [], "arxivIds": ["1810.04805"], "tags": ["transformers", "pytorch", "fill-mask"], "gated": false, "private": false, "fileCount": 16, "createdAt": "2022-03-02T23:29:04.000Z", "lastModified": "2024-02-19T11:06:12.000Z", "url": "https://huggingface.co/google-bert/bert-base-uncased", "error": null } ```` ### Use cases - **Track AI model trends** — snapshot downloads and likes on a schedule to build a time series. - **Competitive & ecosystem research** — see every model an organization has published, with licences. - **Model selection** — compare candidates for a task by adoption, licence and last update before committing. - **Licence & compliance audits** — flag gated or non-commercial models before they reach production. ### Limitations & fair use - Uses the public Hugging Face Hub API. Only **public** models are returned; private or gated-restricted repos require auth and are out of scope. - `downloads` reflects the Hub's rolling 30-day count, not all-time, unless `downloadsAllTime` is provided by the API. - Search relevance and ordering are decided by the Hub itself. - A polite delay is applied between requests. Network calls retry up to 3 times with backoff; failures are reported as an `error` item rather than crashing the run. - Model content and licences belong to their respective authors — always check a model's licence before use. ### More actors by this developer Browse the full collection of free data and enrichment actors: **[apify.com/fit\_melon](https://apify.com/fit_melon)** # Actor input Schema ## `queries` (type: `array`): Keywords to search the Hugging Face Hub. Each query returns up to 'Max results per query' models, one dataset item each. Example: bert, llama, whisper. ## `modelIds` (type: `array`): Exact model repo IDs to fetch directly, e.g. google-bert/bert-base-uncased. One dataset item per ID. ## `maxResultsPerQuery` (type: `integer`): Number of models returned per search query (1-100). ## `sort` (type: `string`): Field used to sort search results. ## `direction` (type: `string`): Descending returns the most downloaded / most liked first. ## `pipelineTag` (type: `string`): Optional task filter, e.g. text-generation, fill-mask, automatic-speech-recognition. Leave empty for all tasks. ## `library` (type: `string`): Optional library filter, e.g. transformers, diffusers, sentence-transformers. ## `author` (type: `string`): Optional author filter, e.g. meta-llama, openai, mistralai. ## Actor input object example ```json { "queries": [ "bert" ], "modelIds": [ "google-bert/bert-base-uncased" ], "maxResultsPerQuery": 10, "sort": "downloads", "direction": "desc", "pipelineTag": "", "library": "", "author": "" } ``` # 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 = { "queries": [ "bert" ], "modelIds": [ "google-bert/bert-base-uncased" ] }; // Run the Actor and wait for it to finish const run = await client.actor("fit_melon/huggingface-model-search-stats").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 = { "queries": ["bert"], "modelIds": ["google-bert/bert-base-uncased"], } # Run the Actor and wait for it to finish run = client.actor("fit_melon/huggingface-model-search-stats").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 '{ "queries": [ "bert" ], "modelIds": [ "google-bert/bert-base-uncased" ] }' | apify call fit_melon/huggingface-model-search-stats --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fit_melon/huggingface-model-search-stats", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Model Search & Stats Scraper (Free)", "description": "Search the Hugging Face Hub and export AI model stats as JSON: downloads, likes, task, library, license, base models, datasets and arXiv papers. Track AI model popularity and trends for free.", "version": "0.0", "x-build-id": "fzDvtMAOxyXruXO2w" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fit_melon~huggingface-model-search-stats/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fit_melon-huggingface-model-search-stats", "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/fit_melon~huggingface-model-search-stats/runs": { "post": { "operationId": "runs-sync-fit_melon-huggingface-model-search-stats", "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/fit_melon~huggingface-model-search-stats/run-sync": { "post": { "operationId": "run-sync-fit_melon-huggingface-model-search-stats", "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": { "queries": { "title": "Search queries", "type": "array", "description": "Keywords to search the Hugging Face Hub. Each query returns up to 'Max results per query' models, one dataset item each. Example: bert, llama, whisper.", "items": { "type": "string" } }, "modelIds": { "title": "Exact model IDs", "type": "array", "description": "Exact model repo IDs to fetch directly, e.g. google-bert/bert-base-uncased. One dataset item per ID.", "items": { "type": "string" } }, "maxResultsPerQuery": { "title": "Max results per query", "minimum": 1, "maximum": 100, "type": "integer", "description": "Number of models returned per search query (1-100).", "default": 10 }, "sort": { "title": "Sort by", "enum": [ "downloads", "likes", "lastModified", "createdAt", "trendingScore" ], "type": "string", "description": "Field used to sort search results.", "default": "downloads" }, "direction": { "title": "Sort direction", "enum": [ "desc", "asc" ], "type": "string", "description": "Descending returns the most downloaded / most liked first.", "default": "desc" }, "pipelineTag": { "title": "Task filter (pipeline tag)", "type": "string", "description": "Optional task filter, e.g. text-generation, fill-mask, automatic-speech-recognition. Leave empty for all tasks.", "default": "" }, "library": { "title": "Library filter", "type": "string", "description": "Optional library filter, e.g. transformers, diffusers, sentence-transformers.", "default": "" }, "author": { "title": "Author / organization filter", "type": "string", "description": "Optional author filter, e.g. meta-llama, openai, mistralai.", "default": "" } } }, "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 } } } } } } } } } } ```