| --- |
| license: cc-by-4.0 |
| pretty_name: Datamata AI Model Popularity Index |
| language: |
| - en |
| tags: |
| - ai |
| - machine-learning |
| - hugging-face |
| - model-popularity |
| - llm |
| - model-trends |
| size_categories: |
| - n<1K |
| source_datasets: |
| - original |
| configs: |
| - config_name: default |
| data_files: ai-model-popularity.csv |
| --- |
| |
|  |
|
|
| # Datamata AI Model Popularity Index |
|
|
| Weekly popularity of the most-downloaded and trending Hugging Face models: trailing downloads, likes, the model's task and its trending rank. One row per model from the most recent weekly snapshot. |
|
|
| - **Latest snapshot:** 2026-07-05 |
| - **Models in this release:** 50 |
| - **Updated:** weekly |
| - **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — free to use and adapt, including commercially, with attribution. |
| - **Source & methodology:** <https://www.datamatastudios.com/datasets> |
|
|
| ## Quickstart |
|
|
| ```python |
| import pandas as pd |
| |
| # Stream straight from the Hub — no download step needed |
| df = pd.read_csv("hf://datasets/datamatastudios/ai-model-popularity/ai-model-popularity.csv") |
| |
| # Most-downloaded models right now |
| print(df.sort_values("downloads", ascending=False).head(10)) |
| ``` |
|
|
| Or load it with the 🤗 `datasets` library: |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("datamatastudios/ai-model-popularity") |
| ``` |
|
|
| ## What you can answer with it |
|
|
| - Which Hugging Face models lead by downloads and likes right now. |
| - Which models are trending this week (`trending_score`) versus steady high-download workhorses. |
| - How popularity splits by task (`pipeline_tag`) — text-generation, text-to-image, embeddings and more. |
| - How a model's popularity moves over time, by appending each weekly snapshot. |
|
|
| ## Columns |
|
|
| | Column | Type | Description | |
| |---|---|---| |
| | `snapshot_date` | string | UTC date the snapshot was taken (YYYY-MM-DD). | |
| | `model_id` | string | Hugging Face model identifier (e.g. meta-llama/Llama-3-8B). | |
| | `author` | string | Owning org or user (the part of model_id before the slash). Blank for un-namespaced models. | |
| | `pipeline_tag` | string | Primary task the model is tagged with (e.g. text-generation, text-to-image). Blank if untagged. | |
| | `downloads` | number | Hugging Face downloads in the trailing 30 days on the snapshot date. | |
| | `likes` | number | Hugging Face likes on the snapshot date. | |
| | `trending_score` | number | Hugging Face trending score on the snapshot date. Blank for models that ranked by downloads only. | |
|
|
| ## How it is built |
|
|
| Each week we query the public Hugging Face Hub API for the top models by |
| trailing-30-day downloads and the current trending models, recording each model's |
| downloads, likes, task tag and trending score on the snapshot date. Full method and |
| known limitations: <https://www.datamatastudios.com/methodology>. |
|
|
| ## Citation |
|
|
| > Datamata Studios. "Datamata AI Model Popularity Index." 2026-07-05. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0. |
|
|
