snapshot_date stringdate 2026-06-28 00:00:00 2026-06-28 00:00:00 | model_id stringlengths 11 59 | author stringlengths 4 21 | pipeline_tag stringlengths 9 30 ⌀ | downloads int64 8.18M 246M | likes int64 7 6.4k | trending_score float64 |
|---|---|---|---|---|---|---|
2026-06-28 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | sentence-similarity | 245,742,847 | 5,015 | null |
2026-06-28 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | text-ranking | 80,423,371 | 269 | null |
2026-06-28 | BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | 61,803,330 | 497 | null |
2026-06-28 | google-bert/bert-base-uncased | google-bert | fill-mask | 60,271,662 | 2,690 | null |
2026-06-28 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | sentence-similarity | 50,349,812 | 1,290 | null |
2026-06-28 | google/electra-base-discriminator | google | null | 41,762,860 | 128 | null |
2026-06-28 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | sentence-similarity | 33,515,916 | 1,313 | null |
2026-06-28 | BAAI/bge-m3 | BAAI | sentence-similarity | 31,360,936 | 3,158 | null |
2026-06-28 | Qwen/Qwen3-0.6B | Qwen | text-generation | 27,739,500 | 1,362 | null |
2026-06-28 | openai/clip-vit-base-patch32 | openai | zero-shot-image-classification | 23,159,737 | 964 | null |
2026-06-28 | timm/mobilenetv3_small_100.lamb_in1k | timm | image-classification | 21,222,099 | 80 | null |
2026-06-28 | FacebookAI/xlm-roberta-base | FacebookAI | fill-mask | 20,459,644 | 855 | null |
2026-06-28 | nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | 18,124,658 | 857 | null |
2026-06-28 | BAAI/bge-reranker-v2-m3 | BAAI | text-classification | 16,278,800 | 1,056 | null |
2026-06-28 | Qwen/Qwen3-4B | Qwen | text-generation | 15,932,949 | 641 | null |
2026-06-28 | laion/clap-htsat-fused | laion | audio-classification | 15,828,115 | 107 | null |
2026-06-28 | hexgrad/Kokoro-82M | hexgrad | text-to-speech | 15,754,089 | 6,395 | null |
2026-06-28 | amazon/chronos-2 | amazon | time-series-forecasting | 15,256,609 | 338 | null |
2026-06-28 | BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | 14,764,689 | 689 | null |
2026-06-28 | autogluon/chronos-bolt-small | autogluon | time-series-forecasting | 13,904,827 | 44 | null |
2026-06-28 | google-t5/t5-small | google-t5 | translation | 13,698,687 | 556 | null |
2026-06-28 | Qwen/Qwen3-8B | Qwen | text-generation | 13,501,708 | 1,166 | null |
2026-06-28 | google/gemma-4-26B-A4B-it | google | image-text-to-text | 13,172,985 | 1,197 | null |
2026-06-28 | openai-community/gpt2 | openai-community | text-generation | 12,980,059 | 3,316 | null |
2026-06-28 | facebook/opt-125m | facebook | text-generation | 12,733,324 | 267 | null |
2026-06-28 | Qwen/Qwen2.5-7B-Instruct | Qwen | text-generation | 12,715,875 | 1,387 | null |
2026-06-28 | Bingsu/adetailer | Bingsu | null | 12,439,088 | 730 | null |
2026-06-28 | FacebookAI/roberta-large | FacebookAI | fill-mask | 12,075,442 | 301 | null |
2026-06-28 | openai/clip-vit-large-patch14 | openai | zero-shot-image-classification | 11,807,851 | 2,040 | null |
2026-06-28 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | text-generation | 11,692,294 | 749 | null |
2026-06-28 | FacebookAI/roberta-base | FacebookAI | fill-mask | 11,648,834 | 617 | null |
2026-06-28 | google/gemma-4-31B-it | google | image-text-to-text | 11,138,450 | 3,071 | null |
2026-06-28 | colbert-ir/colbertv2.0 | colbert-ir | null | 11,101,714 | 362 | null |
2026-06-28 | Qwen/Qwen3-Embedding-0.6B | Qwen | feature-extraction | 10,301,022 | 1,085 | null |
2026-06-28 | meta-llama/Llama-3.1-8B-Instruct | meta-llama | text-generation | 10,147,881 | 6,161 | null |
2026-06-28 | intfloat/multilingual-e5-small | intfloat | sentence-similarity | 9,936,733 | 348 | null |
2026-06-28 | answerdotai/ModernBERT-base | answerdotai | fill-mask | 9,894,988 | 1,060 | null |
2026-06-28 | Falconsai/nsfw_image_detection | Falconsai | image-classification | 9,893,862 | 1,110 | null |
2026-06-28 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | image-text-to-text | 9,588,885 | 1,595 | null |
2026-06-28 | coqui/XTTS-v2 | coqui | text-to-speech | 9,445,398 | 3,623 | null |
2026-06-28 | Qwen/Qwen3.5-9B | Qwen | image-text-to-text | 9,427,429 | 1,628 | null |
2026-06-28 | Qwen/Qwen3.5-4B | Qwen | image-text-to-text | 9,224,801 | 693 | null |
2026-06-28 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | text-generation | 8,876,437 | 7 | null |
2026-06-28 | distilbert/distilbert-base-uncased | distilbert | fill-mask | 8,864,724 | 903 | null |
2026-06-28 | intfloat/multilingual-e5-large | intfloat | feature-extraction | 8,580,779 | 1,208 | null |
2026-06-28 | BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | 8,435,495 | 441 | null |
2026-06-28 | meta-llama/Llama-3.2-1B-Instruct | meta-llama | text-generation | 8,395,796 | 1,502 | null |
2026-06-28 | argmaxinc/whisperkit-coreml | argmaxinc | automatic-speech-recognition | 8,390,579 | 193 | null |
2026-06-28 | pyannote/speaker-diarization-3.1 | pyannote | automatic-speech-recognition | 8,329,860 | 2,510 | null |
2026-06-28 | Qwen/Qwen2.5-3B-Instruct | Qwen | text-generation | 8,182,332 | 513 | 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: 2026-06-28
- 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." 2026-06-28. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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