Datasets:

datamatastudios
/

ai-model-popularity

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 — free to use and adapt, including commercially, with attribution.
Source & methodology: https://www.datamatastudios.com/datasets

Quickstart

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:

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.