snapshot_date stringdate 2026-08-17 00:00:00 2026-08-17 00:00:00 | model_id stringlengths 11 59 | author stringlengths 4 21 | pipeline_tag stringlengths 9 30 ⌀ | downloads int64 7.93M 257M | likes int64 20 6.7k | trending_score float64 |
|---|---|---|---|---|---|---|
2026-08-17 | sentence-transformers/all-MiniLM-L6-v2 | sentence-transformers | sentence-similarity | 257,365,486 | 5,211 | null |
2026-08-17 | google-bert/bert-base-uncased | google-bert | fill-mask | 114,428,580 | 2,734 | null |
2026-08-17 | cross-encoder/ms-marco-MiniLM-L6-v2 | cross-encoder | text-ranking | 89,344,493 | 300 | null |
2026-08-17 | BAAI/bge-small-en-v1.5 | BAAI | feature-extraction | 73,700,917 | 534 | null |
2026-08-17 | sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2 | sentence-transformers | sentence-similarity | 56,263,986 | 1,345 | null |
2026-08-17 | google/electra-base-discriminator | google | null | 56,101,174 | 153 | null |
2026-08-17 | amazon/chronos-2 | amazon | time-series-forecasting | 38,402,399 | 398 | null |
2026-08-17 | lpiccinelli/unidepth-v2-vitl14 | lpiccinelli | null | 36,305,199 | 50 | null |
2026-08-17 | BAAI/bge-m3 | BAAI | sentence-similarity | 35,540,813 | 3,404 | null |
2026-08-17 | Qwen/Qwen3-0.6B | Qwen | text-generation | 28,848,552 | 1,515 | null |
2026-08-17 | sentence-transformers/all-mpnet-base-v2 | sentence-transformers | sentence-similarity | 25,073,877 | 1,341 | null |
2026-08-17 | google-t5/t5-small | google-t5 | translation | 22,855,462 | 592 | null |
2026-08-17 | openai/clip-vit-base-patch32 | openai | zero-shot-image-classification | 20,842,822 | 1,001 | null |
2026-08-17 | BAAI/bge-reranker-v2-m3 | BAAI | text-classification | 18,790,042 | 1,137 | null |
2026-08-17 | timm/mobilenetv3_small_100.lamb_in1k | timm | image-classification | 18,517,276 | 101 | null |
2026-08-17 | FacebookAI/xlm-roberta-base | FacebookAI | fill-mask | 17,815,917 | 882 | null |
2026-08-17 | facebook/opt-125m | facebook | text-generation | 16,919,764 | 292 | null |
2026-08-17 | nomic-ai/nomic-embed-text-v1.5 | nomic-ai | sentence-similarity | 16,340,303 | 892 | null |
2026-08-17 | Qwen/Qwen3-8B | Qwen | text-generation | 15,839,401 | 1,299 | null |
2026-08-17 | trl-internal-testing/tiny-Qwen2ForCausalLM-2.5 | trl-internal-testing | text-generation | 15,570,414 | 20 | null |
2026-08-17 | Qwen/Qwen3.5-9B | Qwen | image-text-to-text | 14,080,772 | 1,824 | null |
2026-08-17 | Comfy-Org/MiniMax-H3 | Comfy-Org | null | 14,015,769 | 1,393 | null |
2026-08-17 | openai-community/gpt2 | openai-community | text-generation | 13,806,023 | 3,407 | null |
2026-08-17 | FacebookAI/roberta-base | FacebookAI | fill-mask | 13,168,427 | 638 | null |
2026-08-17 | BAAI/bge-large-en-v1.5 | BAAI | feature-extraction | 12,805,164 | 714 | null |
2026-08-17 | intfloat/multilingual-e5-small | intfloat | sentence-similarity | 12,497,099 | 386 | null |
2026-08-17 | hexgrad/Kokoro-82M | hexgrad | text-to-speech | 12,392,256 | 6,698 | null |
2026-08-17 | nvidia/Qwen3.6-35B-A3B-NVFP4 | nvidia | text-generation | 12,284,707 | 555 | null |
2026-08-17 | Qwen/Qwen2.5-7B-Instruct | Qwen | text-generation | 12,245,511 | 1,543 | null |
2026-08-17 | Qwen/Qwen3.6-35B-A3B-FP8 | Qwen | image-text-to-text | 11,896,063 | 354 | null |
2026-08-17 | FacebookAI/roberta-large | FacebookAI | fill-mask | 11,665,599 | 319 | null |
2026-08-17 | BAAI/bge-base-en-v1.5 | BAAI | feature-extraction | 11,626,140 | 464 | null |
2026-08-17 | sentence-transformers/paraphrase-multilingual-mpnet-base-v2 | sentence-transformers | sentence-similarity | 10,830,214 | 489 | null |
2026-08-17 | Qwen/Qwen2.5-1.5B-Instruct | Qwen | text-generation | 10,681,138 | 799 | null |
2026-08-17 | Bingsu/adetailer | Bingsu | null | 10,484,126 | 762 | null |
2026-08-17 | Comfy-Org/stable-diffusion-v1-5-archive | Comfy-Org | null | 10,247,399 | 114 | null |
2026-08-17 | unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF | unsloth | text-generation | 10,174,497 | 901 | null |
2026-08-17 | argmaxinc/whisperkit-coreml | argmaxinc | automatic-speech-recognition | 9,922,956 | 200 | null |
2026-08-17 | google/gemma-4-26B-A4B-it | google | image-text-to-text | 9,845,922 | 1,396 | null |
2026-08-17 | google/gemma-4-31B-it | google | image-text-to-text | 9,498,281 | 3,578 | null |
2026-08-17 | pyannote/speaker-diarization-3.1 | pyannote | automatic-speech-recognition | 9,494,893 | 3,086 | null |
2026-08-17 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | image-text-to-text | 9,469,967 | 1,678 | null |
2026-08-17 | Qwen/Qwen3.6-27B-FP8 | Qwen | image-text-to-text | 9,422,975 | 346 | null |
2026-08-17 | autogluon/chronos-bolt-small | autogluon | time-series-forecasting | 8,921,340 | 61 | null |
2026-08-17 | cross-encoder/ms-marco-MiniLM-L4-v2 | cross-encoder | text-ranking | 8,856,070 | 28 | null |
2026-08-17 | meta-llama/Llama-3.2-1B-Instruct | meta-llama | text-generation | 8,764,927 | 1,574 | null |
2026-08-17 | coqui/XTTS-v2 | coqui | text-to-speech | 8,617,817 | 3,732 | null |
2026-08-17 | autogluon/chronos-2 | autogluon | time-series-forecasting | 8,281,124 | 48 | null |
2026-08-17 | Qwen/Qwen3-Embedding-0.6B | Qwen | feature-extraction | 8,114,722 | 1,153 | null |
2026-08-17 | facebook/contriever | facebook | null | 8,004,885 | 96 | null |
2026-08-17 | openai/whisper-large-v3-turbo | openai | automatic-speech-recognition | 7,927,708 | 3,245 | 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: 2026-08-17
- Models in this release: 51
- 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." 2026-08-17. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
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