snapshot_date stringdate 2026-08-02 00:00:00 2026-08-02 00:00:00 | model_id stringlengths 11 59 | author stringlengths 4 21 | pipeline_tag stringlengths 9 30 ⌀ | downloads int64 8.25M 251M | likes int64 18 13.5k | trending_score float64 |
|---|---|---|---|---|---|---|
2026-08-02 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | sentence-similarity | 251,140,343 | 5,159 | null |
2026-08-02 | google-bert/bert-base-uncased | google-bert | fill-mask | 104,862,287 | 2,724 | null |
2026-08-02 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | text-ranking | 86,782,291 | 293 | null |
2026-08-02 | BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | 69,732,954 | 520 | null |
2026-08-02 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | sentence-similarity | 58,584,859 | 1,338 | null |
2026-08-02 | google/electra-base-discriminator | google | null | 53,796,214 | 147 | null |
2026-08-02 | BAAI/bge-m3 | BAAI | sentence-similarity | 34,621,449 | 3,297 | null |
2026-08-02 | lpiccinelli/unidepth-v2-vitl14 | lpiccinelli | null | 30,203,709 | 47 | null |
2026-08-02 | Qwen/Qwen3-0.6B | Qwen | text-generation | 28,290,155 | 1,473 | null |
2026-08-02 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | sentence-similarity | 25,903,929 | 1,337 | null |
2026-08-02 | amazon/chronos-2 | amazon | time-series-forecasting | 25,698,547 | 385 | null |
2026-08-02 | google-t5/t5-small | google-t5 | translation | 25,177,578 | 587 | null |
2026-08-02 | openai/clip-vit-base-patch32 | openai | zero-shot-image-classification | 22,913,187 | 992 | null |
2026-08-02 | FacebookAI/xlm-roberta-base | FacebookAI | fill-mask | 21,870,389 | 876 | null |
2026-08-02 | BAAI/bge-reranker-v2-m3 | BAAI | text-classification | 18,890,749 | 1,118 | null |
2026-08-02 | timm/mobilenetv3_small_100.lamb_in1k | timm | image-classification | 18,051,971 | 99 | null |
2026-08-02 | facebook/opt-125m | facebook | text-generation | 17,259,914 | 286 | null |
2026-08-02 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | text-generation | 15,503,997 | 18 | null |
2026-08-02 | intfloat/multilingual-e5-small | intfloat | sentence-similarity | 15,419,162 | 378 | null |
2026-08-02 | Qwen/Qwen3-8B | Qwen | text-generation | 15,076,048 | 1,266 | null |
2026-08-02 | nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | 14,892,358 | 883 | null |
2026-08-02 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | text-generation | 13,883,160 | 786 | null |
2026-08-02 | openai-community/gpt2 | openai-community | text-generation | 13,392,389 | 3,385 | null |
2026-08-02 | autogluon/chronos-bolt-small | autogluon | time-series-forecasting | 13,204,045 | 58 | null |
2026-08-02 | BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | 12,935,445 | 707 | null |
2026-08-02 | FacebookAI/roberta-large | FacebookAI | fill-mask | 12,457,602 | 317 | null |
2026-08-02 | google/gemma-4-26B-A4B-it | google | image-text-to-text | 12,066,870 | 1,344 | null |
2026-08-02 | autogluon/chronos-2 | autogluon | time-series-forecasting | 11,965,661 | 47 | null |
2026-08-02 | cross-encoder/ms-marco-MiniLM-L4-v2 | cross-encoder | text-ranking | 11,886,462 | 23 | null |
2026-08-02 | Qwen/Qwen2.5-7B-Instruct | Qwen | text-generation | 11,883,439 | 1,452 | null |
2026-08-02 | google/gemma-4-31B-it | google | image-text-to-text | 11,792,520 | 3,425 | null |
2026-08-02 | Qwen/Qwen3.5-9B | Qwen | image-text-to-text | 11,767,971 | 1,780 | null |
2026-08-02 | FacebookAI/roberta-base | FacebookAI | fill-mask | 11,456,752 | 632 | null |
2026-08-02 | hexgrad/Kokoro-82M | hexgrad | text-to-speech | 11,282,032 | 6,623 | null |
2026-08-02 | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | sentence-transformers | sentence-similarity | 10,984,757 | 484 | null |
2026-08-02 | Bingsu/adetailer | Bingsu | null | 10,272,979 | 755 | null |
2026-08-02 | nvidia/Qwen3.6-35B-A3B-NVFP4 | nvidia | text-generation | 10,255,063 | 526 | null |
2026-08-02 | meta-llama/Llama-3.2-1B-Instruct | meta-llama | text-generation | 10,002,758 | 1,549 | null |
2026-08-02 | distilbert/distilbert-base-uncased | distilbert | fill-mask | 9,962,729 | 924 | null |
2026-08-02 | Qwen/Qwen3-Embedding-0.6B | Qwen | feature-extraction | 9,801,487 | 1,135 | null |
2026-08-02 | deepseek-ai/DeepSeek-R1 | deepseek-ai | text-generation | 9,399,065 | 13,532 | null |
2026-08-02 | Qwen/Qwen3-32B | Qwen | text-generation | 9,171,663 | 727 | null |
2026-08-02 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | image-text-to-text | 9,062,856 | 1,660 | null |
2026-08-02 | coqui/XTTS-v2 | coqui | text-to-speech | 8,977,492 | 3,700 | null |
2026-08-02 | BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | 8,889,806 | 461 | null |
2026-08-02 | openai/clip-vit-large-patch14 | openai | zero-shot-image-classification | 8,850,031 | 2,063 | null |
2026-08-02 | pyannote/speaker-diarization-3.1 | pyannote | automatic-speech-recognition | 8,622,264 | 2,932 | null |
2026-08-02 | openai/whisper-large-v3-turbo | openai | automatic-speech-recognition | 8,503,879 | 3,209 | null |
2026-08-02 | Qwen/Qwen3.6-35B-A3B-FP8 | Qwen | image-text-to-text | 8,313,119 | 334 | null |
2026-08-02 | openai/gpt-oss-20b | openai | text-generation | 8,250,813 | 4,868 | 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: 2026-08-02
- 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." 2026-08-02. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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