# Hugging Face Models Scraper — Search, Downloads, Likes, Tags (`seemuapps/huggingface-models-scraper`) Actor Search Hugging Face for models by task, tag, or keyword and export downloads, likes, library, license, and tags to a clean dataset. - **URL**: https://apify.com/seemuapps/huggingface-models-scraper.md - **Developed by:** [Andrew](https://apify.com/seemuapps) (community) - **Categories:** Developer tools, AI, Agents - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN bookmarks - **User rating**: No ratings yet ## Pricing from $3.00 / 1,000 models 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 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 Models Scraper — Search, Downloads, Likes, Tags Search the Hugging Face Hub for machine learning models by task, library, or keyword and export downloads, likes, license, and tags to a clean dataset. No login required. ### What you get - Model ID, author, model name, and direct Hugging Face URL for every result - Pipeline task tag (text-generation, text-to-image, summarization, etc.) and library name (transformers, diffusers, sentence-transformers, peft, timm…) - All Hub tags — useful for filtering by license, language, base model, dataset, or framework - 30-day download count, likes count, and trending score - Gated / private flags so you can spot restricted models - Created and last-modified timestamps for freshness tracking - Direct export to JSON, CSV, Excel, or Google Sheets ### Use cases - ML / model market research — measure popularity and adoption of open-source models - Competitor tracking — monitor downloads and likes for models in your space (LLMs, image generation, embeddings, etc.) - Build a model catalog — power an internal tool, blog post, or product comparison with up-to-date Hub metadata - Lead generation — discover authors and organizations publishing in a specific task or library - License audit — surface every tag (`license:apache-2.0`, `license:mit`, `license:llama3`, …) across a curated list ### How to use 1. Enter a **Search query** (e.g. `llama`, `stable-diffusion`, `whisper`) — or leave empty to browse the whole Hub 2. Optionally pick a **Pipeline task** to narrow to a single task type 3. Optionally enter a **Library filter** (`transformers`, `diffusers`, `sentence-transformers`, `peft`, `timm`, …) 4. Pick a **Sort by** field (Downloads, Likes, Recently updated, Recently created, Trending) and a **Sort direction** 5. Set **Max items** — the actor auto-paginates the Hugging Face Hub API to collect them 6. Run the actor — one model per row in the **Dataset** tab ### Output format One model per dataset row — perfect for direct CSV, Excel, or Google Sheets export: ```json { "id": "meta-llama/Llama-3.1-8B", "author": "meta-llama", "modelName": "Llama-3.1-8B", "huggingfaceUrl": "https://huggingface.co/meta-llama/Llama-3.1-8B", "pipelineTag": "text-generation", "libraryName": "transformers", "tags": ["transformers", "safetensors", "llama", "text-generation", "en", "license:llama3.1"], "downloads": 2845123, "likes": 5421, "gated": "manual", "private": false, "createdAt": "2024-07-22T18:30:00.000Z", "lastModified": "2025-09-14T10:12:33.000Z", "trendingScore": 87.4, "scrapedAt": "2026-05-23T12:34:56.000Z" } ```` ### Input options | Field | Type | Description | |-------|------|-------------| | Search query | string | Free-text search across model IDs and descriptions — optional | | Pipeline task | enum | Restrict to a Hugging Face pipeline tag (text-generation, text-to-image, …) — optional | | Library filter | string | Restrict to a single library (`transformers`, `diffusers`, `sentence-transformers`, …) — optional | | Sort by | enum | Downloads, Likes, Recently updated, Recently created, Trending — default Downloads | | Sort direction | enum | Descending (most first) or Ascending (least first) — default Descending | | Max items | integer | Total models to collect across paginated calls — default 100, max 5000 | # Actor input Schema ## `query` (type: `string`): Free-text search across model IDs and descriptions. Leave empty to list every model in the Hub (filtered by task/library if set). ## `task` (type: `string`): Restrict results to a Hugging Face pipeline task tag. ## `library` (type: `string`): Restrict to models that declare a specific library — e.g. transformers, diffusers, sentence-transformers, peft, timm. Leave empty for any library. ## `sort` (type: `string`): Field to sort the results by. ## `direction` (type: `string`): Descending (-1) returns the most downloads/likes/etc. first; ascending (1) returns the least. ## `maxItems` (type: `integer`): Total number of models to fetch across paginated calls. Hugging Face returns 100 per page; this actor follows the Link header automatically. ## Actor input object example ```json { "query": "llama", "task": "", "sort": "downloads", "direction": "-1", "maxItems": 100 } ``` # Actor output Schema ## `results` (type: `string`): One row per model: id, author, modelName, huggingfaceUrl, pipelineTag, libraryName, tags, downloads (30-day), likes, gated, private, createdAt, lastModified, trendingScore, scrapedAt. # 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 = { "query": "llama" }; // Run the Actor and wait for it to finish const run = await client.actor("seemuapps/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 = { "query": "llama" } # Run the Actor and wait for it to finish run = client.actor("seemuapps/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 '{ "query": "llama" }' | apify call seemuapps/huggingface-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=seemuapps/huggingface-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Models Scraper — Search, Downloads, Likes, Tags", "description": "Search Hugging Face for models by task, tag, or keyword and export downloads, likes, library, license, and tags to a clean dataset.", "version": "1.0", "x-build-id": "huLthO7ZObrHgg2Tl" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/seemuapps~huggingface-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-seemuapps-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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/seemuapps~huggingface-models-scraper/runs": { "post": { "operationId": "runs-sync-seemuapps-huggingface-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/seemuapps~huggingface-models-scraper/run-sync": { "post": { "operationId": "run-sync-seemuapps-huggingface-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "query": { "title": "Search query", "type": "string", "description": "Free-text search across model IDs and descriptions. Leave empty to list every model in the Hub (filtered by task/library if set)." }, "task": { "title": "Pipeline task", "enum": [ "", "text-generation", "text-classification", "text-to-image", "image-classification", "object-detection", "automatic-speech-recognition", "translation", "summarization", "question-answering", "feature-extraction", "fill-mask", "zero-shot-classification", "sentence-similarity" ], "type": "string", "description": "Restrict results to a Hugging Face pipeline task tag.", "default": "" }, "library": { "title": "Library filter", "type": "string", "description": "Restrict to models that declare a specific library — e.g. transformers, diffusers, sentence-transformers, peft, timm. Leave empty for any library." }, "sort": { "title": "Sort by", "enum": [ "downloads", "likes", "lastModified", "createdAt", "trendingScore" ], "type": "string", "description": "Field to sort the results by.", "default": "downloads" }, "direction": { "title": "Sort direction", "enum": [ "-1", "1" ], "type": "string", "description": "Descending (-1) returns the most downloads/likes/etc. first; ascending (1) returns the least.", "default": "-1" }, "maxItems": { "title": "Max items", "minimum": 1, "maximum": 5000, "type": "integer", "description": "Total number of models to fetch across paginated calls. Hugging Face returns 100 per page; this actor follows the Link header automatically.", "default": 100 } } }, "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 } } } } } } } } } } ```