snapshot_date stringdate 2026-09-02 00:00:00 2026-09-02 00:00:00 | model_id stringlengths 11 59 | author stringlengths 4 21 | pipeline_tag stringlengths 9 30 ⌀ | downloads int64 7.64M 255M | likes int64 5 6.79k | trending_score float64 |
|---|---|---|---|---|---|---|
2026-09-02 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | sentence-similarity | 255,143,740 | 5,353 | null |
2026-09-02 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | text-ranking | 86,800,084 | 309 | null |
2026-09-02 | google-bert/bert-base-uncased | google-bert | fill-mask | 69,651,344 | 2,832 | null |
2026-09-02 | BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | 67,786,780 | 542 | null |
2026-09-02 | google/electra-base-discriminator | google | null | 56,965,269 | 157 | null |
2026-09-02 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | sentence-similarity | 46,622,739 | 1,360 | null |
2026-09-02 | BAAI/bge-m3 | BAAI | sentence-similarity | 37,487,482 | 3,453 | null |
2026-09-02 | amazon/chronos-2 | amazon | time-series-forecasting | 28,325,839 | 417 | null |
2026-09-02 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | sentence-similarity | 24,576,514 | 1,347 | null |
2026-09-02 | google-t5/t5-small | google-t5 | translation | 23,282,469 | 596 | null |
2026-09-02 | Qwen/Qwen3-0.6B | Qwen | text-generation | 22,741,013 | 1,564 | null |
2026-09-02 | Comfy-Org/MiniMax-H3 | Comfy-Org | null | 22,661,854 | 1,644 | null |
2026-09-02 | FacebookAI/xlm-roberta-base | FacebookAI | fill-mask | 21,313,287 | 890 | null |
2026-09-02 | openai/clip-vit-base-patch32 | openai | zero-shot-image-classification | 20,562,228 | 1,076 | null |
2026-09-02 | BAAI/bge-reranker-v2-m3 | BAAI | text-classification | 18,150,311 | 1,153 | null |
2026-09-02 | timm/mobilenetv3_small_100.lamb_in1k | timm | image-classification | 18,115,838 | 102 | null |
2026-09-02 | nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | 16,520,519 | 897 | null |
2026-09-02 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | text-generation | 16,346,646 | 20 | null |
2026-09-02 | openai-community/gpt2 | openai-community | text-generation | 14,502,665 | 3,508 | null |
2026-09-02 | BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | 13,639,795 | 718 | null |
2026-09-02 | Qwen/Qwen3-8B | Qwen | text-generation | 13,632,574 | 1,334 | null |
2026-09-02 | Qwen/Qwen3.6-35B-A3B-FP8 | Qwen | image-text-to-text | 13,314,395 | 365 | null |
2026-09-02 | jonatasgrosman/wav2vec2-large-xlsr-53-japanese | jonatasgrosman | automatic-speech-recognition | 13,047,619 | 62 | null |
2026-09-02 | Comfy-Org/stable-diffusion-v1-5-archive | Comfy-Org | null | 12,873,043 | 120 | null |
2026-09-02 | unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | unsloth | text-generation | 12,706,054 | 944 | null |
2026-09-02 | Qwen/Qwen3.5-9B | Qwen | image-text-to-text | 12,621,674 | 1,886 | null |
2026-09-02 | intfloat/multilingual-e5-small | intfloat | sentence-similarity | 12,226,559 | 394 | null |
2026-09-02 | timm/efficientnet_b3.ra2_in1k | timm | image-classification | 11,984,066 | 5 | null |
2026-09-02 | BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | 11,838,084 | 467 | null |
2026-09-02 | argmaxinc/whisperkit-coreml | argmaxinc | automatic-speech-recognition | 11,704,093 | 204 | null |
2026-09-02 | hexgrad/Kokoro-82M | hexgrad | text-to-speech | 11,656,973 | 6,792 | null |
2026-09-02 | facebook/opt-125m | facebook | text-generation | 11,139,753 | 294 | null |
2026-09-02 | Qwen/Qwen2.5-7B-Instruct | Qwen | text-generation | 10,645,526 | 1,571 | null |
2026-09-02 | nvidia/Qwen3.6-35B-A3B-NVFP4 | nvidia | text-generation | 10,606,938 | 581 | null |
2026-09-02 | FacebookAI/roberta-base | FacebookAI | fill-mask | 10,010,113 | 638 | null |
2026-09-02 | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | sentence-transformers | sentence-similarity | 9,878,113 | 492 | null |
2026-09-02 | Bingsu/adetailer | Bingsu | null | 9,869,144 | 761 | null |
2026-09-02 | pyannote/speaker-diarization-3.1 | pyannote | automatic-speech-recognition | 9,630,770 | 3,292 | null |
2026-09-02 | Qwen/Qwen3-VL-8B-Instruct | Qwen | image-text-to-text | 9,584,763 | 1,074 | null |
2026-09-02 | lpiccinelli/unidepth-v2-vitl14 | lpiccinelli | null | 9,550,085 | 50 | null |
2026-09-02 | unsloth/Qwen3.8-27B-GGUF | unsloth | null | 9,354,057 | 3,337 | null |
2026-09-02 | laion/clap-htsat-fused | laion | audio-classification | 9,060,689 | 126 | null |
2026-09-02 | google/gemma-4-31B-it | google | image-text-to-text | 8,282,072 | 3,689 | null |
2026-09-02 | google/gemma-4-26B-A4B-it | google | image-text-to-text | 8,189,743 | 1,464 | null |
2026-09-02 | Qwen/Qwen3.6-27B-FP8 | Qwen | image-text-to-text | 8,157,861 | 350 | null |
2026-09-02 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | image-text-to-text | 8,028,205 | 1,692 | null |
2026-09-02 | facebook/contriever | facebook | null | 7,989,793 | 96 | null |
2026-09-02 | coqui/XTTS-v2 | coqui | text-to-speech | 7,786,333 | 3,756 | null |
2026-09-02 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | text-generation | 7,684,224 | 813 | null |
2026-09-02 | Qwen/Qwen2.5-3B-Instruct | Qwen | text-generation | 7,635,044 | 557 | 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: 2026-09-02
- 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." 2026-09-02. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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