# Hugging Face Models Scraper - Downloads, Likes, Trending, Tags (`fetchcraft/huggingface-models-scraper`) Actor Search and scrape Hugging Face Models with downloads, likes, trending score, tags, license, library, pipeline. No API key. Filter by author (meta-llama, google, mistralai), pipeline (text-generation, image-to-text), or search. $0.001 per model. Free preview. - **URL**: https://apify.com/fetchcraft/huggingface-models-scraper.md - **Developed by:** [Emily Ward](https://apify.com/fetchcraft) (community) - **Categories:** AI, Developer tools, Other - **Stats:** 2 total users, 1 monthly users, 100.0% runs succeeded, NaN 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 Models Scraper ![Hugging Face Models Scraper - Downloads, Likes, Trending](https://ai-sales-personalizer.vercel.app/og/huggingface-models-scraper.png) Search and scrape Hugging Face's model catalog with downloads, likes, trending score, tags, license, library, pipeline task, and full metadata. No API key required, uses the public Hugging Face Hub API. ### Why use this - **AI tooling vendors**: build a model coverage matrix for your inference API. Filter by pipeline tag + library to see exactly what your customers want supported. - **Investors and analysts**: track trending open-source models and the orgs publishing them. The `trendingScore` is the live signal HF uses to surface the homepage. - **Open-source maintainers**: monitor your competitive landscape. Filter by pipeline_tag + library to see who you're ranking against. - **Recruiters / VCs**: surface organisations publishing high-impact models. Filter by author and sort by downloads. - **Researchers**: pull a full model index for citation analysis, licensing audits, or pipeline-tag distribution studies. ### Quickstart Paste this into the input and click Start: ```json { "search": "llama", "sort": "downloads", "direction": "desc", "min_downloads": 10000, "max_results": 25, "preview_mode": true } ```` Returns the top 25 Llama-related models by recent download count, free preview enabled. ### What it returns Each row (one per model): ```json { "model_id": "meta-llama/Llama-3.1-8B-Instruct", "author": "meta-llama", "name": "Llama-3.1-8B-Instruct", "url": "https://huggingface.co/meta-llama/Llama-3.1-8B-Instruct", "downloads": 11146200, "downloads_all_time": 89000000, "likes": 6021, "trending_score": 766, "last_modified": "2026-06-01T12:30:00.000Z", "created_at": "2024-07-22T15:00:00.000Z", "pipeline_tag": "text-generation", "library_name": "transformers", "tags": ["transformers", "safetensors", "llama", "text-generation", "conversational", "en", "license:llama3.1"], "license": "license:llama3.1", "is_gated": true, "is_private": false, "scraped_at": "2026-06-09T07:00:00.000Z" } ``` ### Filters - **search**: Free-text search across model names and tags - **author**: Restrict to one org (`meta-llama`, `google`, `mistralai`, `openai`, `stabilityai`, etc.) - **pipeline\_tag**: Filter by HF pipeline (text-generation, image-to-text, automatic-speech-recognition, etc.) - **library**: Filter by library (`transformers`, `diffusers`, `sentence-transformers`, `peft`, `gguf`) - **sort + direction**: Sort by downloads, likes, trendingScore, lastModified, createdAt - **min\_downloads / min\_likes**: Threshold filters to skip noise - **max\_results**: Hard cap (default 50, max 1,000) - **preview\_mode**: Free preview (5 results, no charge) ### Pricing - **$0.001 per model returned.** Pay only for results you keep after filtering. - Free preview mode returns the first 5 models without charging. ### Pairs well with - **[github-trending-scraper](https://apify.com/fetchcraft/github-trending-scraper)**: Cross-reference trending HF models with trending GitHub repos publishing them. Free preview. - **[github-repo-bulk-analyzer](https://apify.com/fetchcraft/github-repo-bulk-analyzer)**: For each model's GitHub repo, pull stars, contributors, and activity. $0.005 per repo. - **[devto-top-articles-scraper](https://apify.com/fetchcraft/devto-top-articles-scraper)**: Pair trending models with top dev.to articles in the AI tag. $0.005 per article. - **[ai-sales-personalizer](https://apify.com/fetchcraft/ai-sales-personalizer)**: Outbound to orgs publishing high-impact models with personalised openers. $0.10 to $0.20 per lead. - **[contact-details-extractor](https://apify.com/fetchcraft/contact-details-extractor)**: Pull contact details from the org's website. $0.01 per site. ### Integrations This actor works out of the box with every Apify-supported integration: - **API**: call via Apify API or any official SDK (Python, JavaScript, PHP, .NET). Returns a clean dataset URL. - **Schedule**: set a daily, weekly, or custom cron cadence in Apify Console. Combine with `notification` for fresh feeds. - **Webhooks**: wire `ACTOR.RUN.SUCCEEDED` to Slack, Discord, Zapier, Make, n8n, Pipedream, or any HTTPS endpoint. - **MCP**: this actor is discoverable through Apify's hosted MCP server at `mcp.apify.com` for Claude, Cursor, Cline, Windsurf, and other MCP clients. - **n8n / Make / Zapier**: native HTTP-Request integration. Trigger the actor on schedule, pipe results to Google Sheets, Airtable, your CRM, or any database. ### Try it free Every Apify user gets **$5/month in free platform credits** (around 5,000 events at this actor's per-event price). Run preview mode first to confirm output shape before scaling. New to Apify? [Sign up here](https://apify.com/sign-up?fpr=fetchcraft) to get free credits on signup. ### What's New - 2026-06-09: Initial release. Public Hugging Face Hub API integration, no key required, full metadata pulled. ### Last Updated 2026-06-09 # Actor input Schema ## `search` (type: `string`): Text search across model names, tags, and descriptions. Examples: 'llama', 'whisper', 'flux', 'gemma'. Leave empty to browse the full catalog ranked by your sort field. ## `author` (type: `string`): Limit results to a specific Hugging Face user or organisation. Examples: 'meta-llama', 'google', 'openai', 'stabilityai', 'mistralai', 'Qwen'. ## `pipeline_tag` (type: `string`): Hugging Face pipeline category. Common values: text-generation, image-to-text, automatic-speech-recognition, text-to-image, image-classification, sentence-similarity, feature-extraction, fill-mask, summarization, translation. ## `library` (type: `string`): ML library the model is published with. Examples: transformers, diffusers, sentence-transformers, peft, gguf, sd\_lora. ## `sort` (type: `string`): Field to sort results by. 'downloads' = recent download count. 'likes' = community upvotes. 'trendingScore' = the live trending signal. 'lastModified' = recency. ## `direction` (type: `string`): Sort direction. Descending shows highest values first (highest downloads, most likes, etc). ## `min_downloads` (type: `integer`): Filter out models below this download threshold. Set to 1000 to skip tiny one-off experiments. Set to 0 (default) to keep all. ## `min_likes` (type: `integer`): Filter out models below this like count. ## `max_results` (type: `integer`): Hard cap on total models returned across all pages. ## `preview_mode` (type: `boolean`): Returns the top 5 models without charging. Use to verify output shape before bulk runs. ## Actor input object example ```json { "search": "llama", "pipeline_tag": "", "sort": "downloads", "direction": "desc", "min_downloads": 0, "min_likes": 0, "max_results": 50, "preview_mode": false } ``` # 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 = { "search": "llama" }; // Run the Actor and wait for it to finish const run = await client.actor("fetchcraft/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 = { "search": "llama" } # Run the Actor and wait for it to finish run = client.actor("fetchcraft/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 '{ "search": "llama" }' | apify call fetchcraft/huggingface-models-scraper --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=fetchcraft/huggingface-models-scraper", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Hugging Face Models Scraper - Downloads, Likes, Trending, Tags", "description": "Search and scrape Hugging Face Models with downloads, likes, trending score, tags, license, library, pipeline. No API key. Filter by author (meta-llama, google, mistralai), pipeline (text-generation, image-to-text), or search. $0.001 per model. Free preview.", "version": "0.1", "x-build-id": "hV9dn2v7mfhOiLvGV" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/fetchcraft~huggingface-models-scraper/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-fetchcraft-huggingface-models-scraper", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for its completion, and returns Actor's dataset items in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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/fetchcraft~huggingface-models-scraper/runs": { "post": { "operationId": "runs-sync-fetchcraft-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/fetchcraft~huggingface-models-scraper/run-sync": { "post": { "operationId": "run-sync-fetchcraft-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": { "search": { "title": "Search query", "type": "string", "description": "Text search across model names, tags, and descriptions. Examples: 'llama', 'whisper', 'flux', 'gemma'. Leave empty to browse the full catalog ranked by your sort field." }, "author": { "title": "Author / organisation filter", "type": "string", "description": "Limit results to a specific Hugging Face user or organisation. Examples: 'meta-llama', 'google', 'openai', 'stabilityai', 'mistralai', 'Qwen'." }, "pipeline_tag": { "title": "Pipeline tag filter", "enum": [ "", "text-generation", "text-classification", "text-to-image", "image-to-text", "image-classification", "automatic-speech-recognition", "text-to-speech", "translation", "summarization", "question-answering", "fill-mask", "token-classification", "sentence-similarity", "feature-extraction", "zero-shot-classification", "object-detection", "video-classification", "depth-estimation" ], "type": "string", "description": "Hugging Face pipeline category. Common values: text-generation, image-to-text, automatic-speech-recognition, text-to-image, image-classification, sentence-similarity, feature-extraction, fill-mask, summarization, translation.", "default": "" }, "library": { "title": "Library filter", "type": "string", "description": "ML library the model is published with. Examples: transformers, diffusers, sentence-transformers, peft, gguf, sd_lora." }, "sort": { "title": "Sort by", "enum": [ "downloads", "likes", "trendingScore", "lastModified", "createdAt" ], "type": "string", "description": "Field to sort results by. 'downloads' = recent download count. 'likes' = community upvotes. 'trendingScore' = the live trending signal. 'lastModified' = recency.", "default": "downloads" }, "direction": { "title": "Sort direction", "enum": [ "desc", "asc" ], "type": "string", "description": "Sort direction. Descending shows highest values first (highest downloads, most likes, etc).", "default": "desc" }, "min_downloads": { "title": "Minimum downloads", "minimum": 0, "maximum": 100000000, "type": "integer", "description": "Filter out models below this download threshold. Set to 1000 to skip tiny one-off experiments. Set to 0 (default) to keep all.", "default": 0 }, "min_likes": { "title": "Minimum likes", "minimum": 0, "maximum": 100000, "type": "integer", "description": "Filter out models below this like count.", "default": 0 }, "max_results": { "title": "Maximum results", "minimum": 1, "maximum": 1000, "type": "integer", "description": "Hard cap on total models returned across all pages.", "default": 50 }, "preview_mode": { "title": "Preview mode (free, 5 results)", "type": "boolean", "description": "Returns the top 5 models without charging. Use to verify output shape before bulk runs.", "default": false } } }, "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 } } } } } } } } } } ```