# Huggingface Models (`david_flagg/huggingface-models`) Actor Scrape model metadata from HuggingFace Hub — the largest open-source ML model registry. Get downloads, likes, trending scores, licenses, tags, and\ architecture info for 1M+ models. Filter by task type, ML library, or author. Uses the official HF API — no auth required. - **URL**: https://apify.com/david\_flagg/huggingface-models.md - **Developed by:** [David Flagg](https://apify.com/david_flagg) (community) - **Categories:** AI, Automation, Developer tools - **Stats:** 7 total users, 1 monthly users, 100.0% runs succeeded, 0 bookmarks - **User rating**: No ratings yet ## Pricing from $1.00 / 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. 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 ## HuggingFace Models Scraper Scrape model metadata from [HuggingFace Hub](https://huggingface.co/models) — the largest open-source ML model registry with 1M+ models. ### What it does Fetches model data from the HuggingFace API including: - **Identifiers**: model ID, author/org, model name - **Popularity**: download count, likes, trending score - **Metadata**: task type (text-generation, image-classification, etc.), ML library, model architecture, license - **Timestamps**: creation date, last modified - **Tags**: filtered model tags (framework, task, format markers) - **Access**: public/private, gated status ### Use cases - **Track trending AI models** — monitor what's gaining traction on HuggingFace - **Compare model popularity** — downloads, likes, and trending scores across models - **Filter by task** — find all text-generation, text-to-image, speech recognition models, etc. - **Monitor organizations** — track new releases from Meta, Google, Mistral, Qwen, etc. - **License auditing** — filter models by license for commercial use - **Market research** — understand which ML frameworks and architectures are most popular ### Input options | Parameter | Description | Default | |-----------|-------------|---------| | Search Query | Search model names and descriptions | (empty) | | Author / Organization | Filter by HuggingFace username or org | (empty) | | Task Type | Filter by ML task (text-generation, image-classification, etc.) | Any | | ML Library | Filter by framework (transformers, diffusers, gguf, etc.) | (empty) | | Sort By | Trending, Most Downloads, Most Likes, Recently Updated, or Newest | Trending | | Max Results | Maximum number of models to return (0 = unlimited) | 100 | ### Example output ```json { "model_id": "meta-llama/Llama-4-Scout-17B-16E-Instruct", "author": "meta-llama", "model_name": "meta-llama/Llama-4-Scout-17B-16E-Instruct", "pipeline_tag": "text-generation", "library_name": "transformers", "model_type": "llama4", "tags": ["safetensors", "llama4", "text-generation", "conversational", "en", "de", "fr", "..."], "downloads": 2847291, "likes": 1205, "trending_score": 482, "license": "llama4", "created_at": "2026-02-05T18:01:22.000Z", "last_modified": "2026-03-10T15:22:47.000Z", "private": false, "gated": false, "url": "https://huggingface.co/meta-llama/Llama-4-Scout-17B-16E-Instruct", "fetched_at": "2026-03-13T12:00:00.000000+00:00" } ```` ### Notes - Uses the official HuggingFace Hub API (no scraping or authentication required) - Paginates automatically for large result sets - All sort orders are descending (most popular / newest first) - Tags are filtered to remove internal metadata (region, dataset references, arxiv links) # Actor input Schema ## `search` (type: `string`): Search model names and descriptions (e.g. 'llama', 'diffusion', 'whisper') ## `author` (type: `string`): Filter by HuggingFace username or organization (e.g. 'meta-llama', 'mistralai', 'google') ## `pipelineTag` (type: `string`): Filter by ML task ## `libraryName` (type: `string`): Filter by framework (e.g. 'transformers', 'diffusers', 'gguf') ## `sortBy` (type: `string`): How to sort results (all sorted descending — most popular/recent first) ## `maxResults` (type: `integer`): Maximum number of models to return. Set to 0 for unlimited (use with caution — HuggingFace has 1M+ models). ## Actor input object example ```json { "search": "", "author": "", "pipelineTag": "", "libraryName": "", "sortBy": "trending", "maxResults": 100 } ``` # Actor output Schema ## `dataset` (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 = {}; // Run the Actor and wait for it to finish const run = await client.actor("david_flagg/huggingface-models").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 = {} # Run the Actor and wait for it to finish run = client.actor("david_flagg/huggingface-models").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 '{}' | apify call david_flagg/huggingface-models --silent --output-dataset ``` ## MCP server setup ```json { "mcpServers": { "apify": { "command": "npx", "args": [ "mcp-remote", "https://mcp.apify.com/?tools=david_flagg/huggingface-models", "--header", "Authorization: Bearer " ] } } } ``` ## OpenAPI specification ```json { "openapi": "3.0.1", "info": { "title": "Huggingface Models", "description": "Scrape model metadata from HuggingFace Hub — the largest open-source ML model \nregistry. Get downloads, likes, trending scores, licenses, tags, and \narchitecture info for 1M+ models. Filter by task type, ML library, or author. \nUses the official HF API — no auth required.", "version": "0.1", "x-build-id": "geb4mnpKbPjUrnDV9" }, "servers": [ { "url": "https://api.apify.com/v2" } ], "paths": { "/acts/david_flagg~huggingface-models/run-sync-get-dataset-items": { "post": { "operationId": "run-sync-get-dataset-items-david_flagg-huggingface-models", "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": { "$ref": "#/components/schemas/inputSchema" } } } }, "parameters": [ { "name": "token", "in": "query", "required": true, "schema": { "type": "string" }, "description": "Enter your Apify token here" } ], "responses": { "200": { "description": "OK" } } } }, "/acts/david_flagg~huggingface-models/runs": { "post": { "operationId": "runs-sync-david_flagg-huggingface-models", "x-openai-isConsequential": false, "summary": "Executes an Actor and returns information about the initiated run in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": { "$ref": "#/components/schemas/runsResponseSchema" } } } } } } }, "/acts/david_flagg~huggingface-models/run-sync": { "post": { "operationId": "run-sync-david_flagg-huggingface-models", "x-openai-isConsequential": false, "summary": "Executes an Actor, waits for completion, and returns the OUTPUT from Key-value store in response.", "tags": [ "Run Actor" ], "requestBody": { "required": true, "content": { "application/json": { "schema": { "$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": "Search model names and descriptions (e.g. 'llama', 'diffusion', 'whisper')", "default": "" }, "author": { "title": "Author / Organization", "type": "string", "description": "Filter by HuggingFace username or organization (e.g. 'meta-llama', 'mistralai', 'google')", "default": "" }, "pipelineTag": { "title": "Task Type", "enum": [ "", "text-generation", "text-classification", "token-classification", "question-answering", "summarization", "translation", "fill-mask", "text2text-generation", "image-classification", "object-detection", "image-segmentation", "image-to-text", "text-to-image", "text-to-video", "image-to-video", "automatic-speech-recognition", "text-to-speech", "feature-extraction", "sentence-similarity", "zero-shot-classification", "reinforcement-learning" ], "type": "string", "description": "Filter by ML task", "default": "" }, "libraryName": { "title": "ML Library", "type": "string", "description": "Filter by framework (e.g. 'transformers', 'diffusers', 'gguf')", "default": "" }, "sortBy": { "title": "Sort By", "enum": [ "trending", "downloads", "likes", "lastModified", "createdAt" ], "type": "string", "description": "How to sort results (all sorted descending — most popular/recent first)", "default": "trending" }, "maxResults": { "title": "Max Results", "minimum": 0, "maximum": 10000, "type": "integer", "description": "Maximum number of models to return. Set to 0 for unlimited (use with caution — HuggingFace has 1M+ models).", "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 } } } } } } } } } } ```