Hugging Face Trending AI Models and Datasets Radar
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$0.90 / 1,000 results
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Hugging Face Trending AI Models and Datasets Radar
Monitor trending open-source AI models, LLMs, download statistics, likes, pipeline tasks, and new releases across Hugging Face.
Pricing
$0.90 / 1,000 results
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0.0
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Developer
Darim Quraishi
Maintained by CommunityActor stats
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2
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1
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18 hours ago
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Hugging Face Models and Datasets Scraper
Extract model metadata, download counts, community likes, pipeline tasks, and dataset listings from Hugging Face.
Features
- Monitors trending and popular models across computer vision, NLP, audio, and multimodal tasks.
- Filters by pipeline task, model author, library framework, and commercial license.
- Extracts monthly download figures, total likes, and model card URLs.
- Tracks new model releases and recent update timestamps.
- Supports dataset discovery alongside model listings.
- Queries official Hugging Face API endpoints proxy-free.
Input parameters
| Parameter | Type | Default | Description |
|---|---|---|---|
searchQuery | String | "" | Filter by keyword (e.g. llama, deepseek, flux, whisper). |
pipelineTag | String | "all" | Task type (text-generation, text-to-image, etc.). |
sortBy | String | "downloads" | downloads, likes, trendingScore, or lastModified. |
maxModels | Integer | 50 | Maximum number of models to collect. |
includeTags | Boolean | true | Include tags (precision, frameworks, formats). |
Output format
json { "modelId": "sentence-transformers/all-MiniLM-L6-v2", "author": "sentence-transformers", "name": "all-MiniLM-L6-v2", "pipelineTag": "sentence-similarity", "downloads": 198627085, "likes": 5996, "trendingScore": 84, "license": "apache-2.0", "isPrivate": false, "tags": ["transformers", "pytorch", "sentence-similarity", "feature-extraction"], "url": "https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2", "lastModified": "2026-10-18T14:22:00.000Z", "scrapedAt": "2026-10-22T13:50:00.000Z" }Use cases
- AI ecosystem monitoring: Track which model architectures and tasks gain the fastest adoption.
- Model selection: Filter open-source models by license, task, and download count.
- Competitive research: Monitor release cycles from AI research labs and organizations.
- Tooling automation: Feed new model releases into evaluation benchmarks.
Pricing and efficiency
- Typical runtime: 2 to 4 seconds per execution.
- Zero proxy costs: Runs over public REST and RSS endpoints without residential proxy pools.
- Suitable for scheduled runs and continuous monitoring under standard platform limits.
