snapshot_date stringdate 2026-07-05 00:00:00 2026-07-05 00:00:00 | model_id stringlengths 11 59 | author stringlengths 4 21 | pipeline_tag stringlengths 9 30 ⌀ | downloads int64 7.87M 245M | likes int64 9 13.4k | trending_score null |
|---|---|---|---|---|---|---|
2026-07-05 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | sentence-similarity | 244,738,593 | 5,029 | null |
2026-07-05 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | text-ranking | 80,140,032 | 272 | null |
2026-07-05 | google-bert/bert-base-uncased | google-bert | fill-mask | 62,729,232 | 2,696 | null |
2026-07-05 | BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | 61,655,086 | 503 | null |
2026-07-05 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | sentence-similarity | 47,757,847 | 1,298 | null |
2026-07-05 | google/electra-base-discriminator | google | null | 39,982,991 | 132 | null |
2026-07-05 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | sentence-similarity | 33,082,579 | 1,318 | null |
2026-07-05 | BAAI/bge-m3 | BAAI | sentence-similarity | 32,213,391 | 3,188 | null |
2026-07-05 | Qwen/Qwen3-0.6B | Qwen | text-generation | 27,994,605 | 1,384 | null |
2026-07-05 | timm/mobilenetv3_small_100.lamb_in1k | timm | image-classification | 26,028,831 | 83 | null |
2026-07-05 | openai/clip-vit-base-patch32 | openai | zero-shot-image-classification | 22,238,110 | 969 | null |
2026-07-05 | FacebookAI/xlm-roberta-base | FacebookAI | fill-mask | 20,362,288 | 859 | null |
2026-07-05 | google-t5/t5-small | google-t5 | translation | 19,104,056 | 563 | null |
2026-07-05 | nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | 16,566,816 | 859 | null |
2026-07-05 | BAAI/bge-reranker-v2-m3 | BAAI | text-classification | 16,233,937 | 1,067 | null |
2026-07-05 | Qwen/Qwen3-8B | Qwen | text-generation | 16,001,721 | 1,180 | null |
2026-07-05 | amazon/chronos-2 | amazon | time-series-forecasting | 15,197,990 | 345 | null |
2026-07-05 | BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | 14,137,797 | 691 | null |
2026-07-05 | hexgrad/Kokoro-82M | hexgrad | text-to-speech | 13,949,161 | 6,438 | null |
2026-07-05 | facebook/opt-125m | facebook | text-generation | 13,795,345 | 271 | null |
2026-07-05 | autogluon/chronos-bolt-small | autogluon | time-series-forecasting | 13,591,787 | 45 | null |
2026-07-05 | google/gemma-4-26B-A4B-it | google | image-text-to-text | 13,568,647 | 1,228 | null |
2026-07-05 | openai-community/gpt2 | openai-community | text-generation | 13,218,856 | 3,328 | null |
2026-07-05 | laion/clap-htsat-fused | laion | audio-classification | 12,844,513 | 108 | null |
2026-07-05 | Qwen/Qwen2.5-7B-Instruct | Qwen | text-generation | 12,834,959 | 1,398 | null |
2026-07-05 | FacebookAI/roberta-large | FacebookAI | fill-mask | 12,773,172 | 303 | null |
2026-07-05 | openai/clip-vit-large-patch14 | openai | zero-shot-image-classification | 12,471,027 | 2,048 | null |
2026-07-05 | intfloat/multilingual-e5-large | intfloat | feature-extraction | 12,188,485 | 1,214 | null |
2026-07-05 | Bingsu/adetailer | Bingsu | null | 11,925,074 | 736 | null |
2026-07-05 | google/gemma-4-31B-it | google | image-text-to-text | 11,248,247 | 3,125 | null |
2026-07-05 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | text-generation | 11,194,051 | 759 | null |
2026-07-05 | FacebookAI/roberta-base | FacebookAI | fill-mask | 10,558,402 | 618 | null |
2026-07-05 | Qwen/Qwen3-4B | Qwen | text-generation | 10,483,582 | 646 | null |
2026-07-05 | intfloat/multilingual-e5-small | intfloat | sentence-similarity | 10,390,289 | 354 | null |
2026-07-05 | Qwen/Qwen3-Embedding-0.6B | Qwen | feature-extraction | 10,351,571 | 1,094 | null |
2026-07-05 | answerdotai/ModernBERT-base | answerdotai | fill-mask | 10,127,181 | 1,064 | null |
2026-07-05 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | image-text-to-text | 9,818,459 | 1,605 | null |
2026-07-05 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | text-generation | 9,701,891 | 9 | null |
2026-07-05 | coqui/XTTS-v2 | coqui | text-to-speech | 9,303,058 | 3,635 | null |
2026-07-05 | meta-llama/Llama-3.1-8B-Instruct | meta-llama | text-generation | 9,285,862 | 6,223 | null |
2026-07-05 | Falconsai/nsfw_image_detection | Falconsai | image-classification | 9,080,050 | 1,125 | null |
2026-07-05 | meta-llama/Llama-3.2-1B-Instruct | meta-llama | text-generation | 9,020,161 | 1,505 | null |
2026-07-05 | colbert-ir/colbertv2.0 | colbert-ir | null | 8,912,824 | 363 | null |
2026-07-05 | Qwen/Qwen3.5-9B | Qwen | image-text-to-text | 8,837,513 | 1,660 | null |
2026-07-05 | distilbert/distilbert-base-uncased | distilbert | fill-mask | 8,552,826 | 907 | null |
2026-07-05 | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | sentence-transformers | sentence-similarity | 8,484,118 | 468 | null |
2026-07-05 | pyannote/speaker-diarization-3.1 | pyannote | automatic-speech-recognition | 8,250,685 | 2,589 | null |
2026-07-05 | BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | 8,247,637 | 444 | null |
2026-07-05 | deepseek-ai/DeepSeek-R1 | deepseek-ai | text-generation | 8,214,698 | 13,434 | null |
2026-07-05 | autogluon/chronos-2 | autogluon | time-series-forecasting | 7,873,249 | 33 | null |
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.
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