Update 2026-09-02 (50 models)

README.md

@@ -1,82 +1,82 @@
----
-license: cc-by-4.0
-pretty_name: Datamata AI Model Popularity Index
-language:
-  - en
-tags:
-  - ai
-  - machine-learning
-  - hugging-face
-  - model-popularity
-  - llm
-  - model-trends
-size_categories:
-  - n<1K
-source_datasets:
-  - original
-configs:
-  - config_name: default
-    data_files: ai-model-popularity.csv
----
-
-![Datamata AI Model Popularity Index](dataset-cover-image.png)
-
-# 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:** 2026-08-25
-- **Models in this release:** 50
-- **Updated:** weekly
-- **Licence:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) — free to use and adapt, including commercially, with attribution.
-- **Source & methodology:** <https://www.datamatastudios.com/datasets>
-
-## Quickstart
-
-```python
-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:
-
-```python
-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." 2026-08-25. https://www.datamatastudios.com/datasets. Licensed under CC BY 4.0.
+---
+license: cc-by-4.0
+pretty_name: Datamata AI Model Popularity Index
+language:
+  - en
+tags:
+  - ai
+  - machine-learning
+  - hugging-face
+  - model-popularity
+  - llm
+  - model-trends
+size_categories:
+  - n<1K
+source_datasets:
+  - original
+configs:
+  - config_name: default
+    data_files: ai-model-popularity.csv
+---
+
+![Datamata AI Model Popularity Index](dataset-cover-image.png)
+
+# 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](https://creativecommons.org/licenses/by/4.0/) — free to use and adapt, including commercially, with attribution.
+- **Source & methodology:** <https://www.datamatastudios.com/datasets>
+
+## Quickstart
+
+```python
+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:
+
+```python
+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.

ai-model-popularity.csv

@@ -1,51 +1,51 @@
 snapshot_date,model_id,author,pipeline_tag,downloads,likes,trending_score
-2026-08-25,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,sentence-similarity,255841858,5252,
-2026-08-25,google-bert/bert-base-uncased,google-bert,fill-mask,95424377,2739,
-2026-08-25,cross-encoder/ms-marco-MiniLM-L6-v2,cross-encoder,text-ranking,87612957,301,
-2026-08-25,BAAI/bge-small-en-v1.5,BAAI,feature-extraction,71619849,538,
-2026-08-25,google/electra-base-discriminator,google,,54698880,154,
-2026-08-25,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,sentence-transformers,sentence-similarity,54529466,1354,
-2026-08-25,BAAI/bge-m3,BAAI,sentence-similarity,36411088,3427,
-2026-08-25,amazon/chronos-2,amazon,time-series-forecasting,35054213,407,
-2026-08-25,lpiccinelli/unidepth-v2-vitl14,lpiccinelli,,26310151,50,
-2026-08-25,sentence-transformers/all-mpnet-base-v2,sentence-transformers,sentence-similarity,24860325,1343,
-2026-08-25,Qwen/Qwen3-0.6B,Qwen,text-generation,24307256,1536,
-2026-08-25,google-t5/t5-small,google-t5,translation,22916929,593,
-2026-08-25,openai/clip-vit-base-patch32,openai,zero-shot-image-classification,20449844,1002,
-2026-08-25,timm/mobilenetv3_small_100.lamb_in1k,timm,image-classification,18769442,101,
-2026-08-25,FacebookAI/xlm-roberta-base,FacebookAI,fill-mask,18722400,885,
-2026-08-25,BAAI/bge-reranker-v2-m3,BAAI,text-classification,18610016,1142,
-2026-08-25,Comfy-Org/MiniMax-H3,Comfy-Org,,17824142,1524,
-2026-08-25,nomic-ai/nomic-embed-text-v1.5,nomic-ai,sentence-similarity,16576020,893,
-2026-08-25,trl-internal-testing/tiny-Qwen2ForCausalLM-2.5,trl-internal-testing,text-generation,15660954,20,
-2026-08-25,facebook/opt-125m,facebook,text-generation,14783147,293,
-2026-08-25,Qwen/Qwen3-8B,Qwen,text-generation,14667733,1319,
-2026-08-25,openai-community/gpt2,openai-community,text-generation,14166161,3415,
-2026-08-25,Qwen/Qwen3.5-9B,Qwen,image-text-to-text,13620332,1854,
-2026-08-25,BAAI/bge-large-en-v1.5,BAAI,feature-extraction,13501200,715,
-2026-08-25,Qwen/Qwen3.6-35B-A3B-FP8,Qwen,image-text-to-text,13237178,357,
-2026-08-25,unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF,unsloth,text-generation,12748404,919,
-2026-08-25,nvidia/Qwen3.6-35B-A3B-NVFP4,nvidia,text-generation,12435020,567,
-2026-08-25,intfloat/multilingual-e5-small,intfloat,sentence-similarity,12400325,393,
-2026-08-25,hexgrad/Kokoro-82M,hexgrad,text-to-speech,12305209,6747,
-2026-08-25,BAAI/bge-base-en-v1.5,BAAI,feature-extraction,11973561,463,
-2026-08-25,Qwen/Qwen2.5-7B-Instruct,Qwen,text-generation,11527060,1554,
-2026-08-25,argmaxinc/whisperkit-coreml,argmaxinc,automatic-speech-recognition,11167694,201,
-2026-08-25,FacebookAI/roberta-base,FacebookAI,fill-mask,11016518,638,
-2026-08-25,Comfy-Org/stable-diffusion-v1-5-archive,Comfy-Org,,10755740,118,
-2026-08-25,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,sentence-transformers,sentence-similarity,10313963,489,
-2026-08-25,FacebookAI/roberta-large,FacebookAI,fill-mask,10118275,319,
-2026-08-25,Bingsu/adetailer,Bingsu,,10114219,762,
-2026-08-25,pyannote/speaker-diarization-3.1,pyannote,automatic-speech-recognition,9900213,3162,
-2026-08-25,Qwen/Qwen3.6-27B-FP8,Qwen,image-text-to-text,9037684,347,
-2026-08-25,google/gemma-4-26B-A4B-it,google,image-text-to-text,8942465,1431,
-2026-08-25,jonatasgrosman/wav2vec2-large-xlsr-53-japanese,jonatasgrosman,automatic-speech-recognition,8902610,62,
-2026-08-25,Qwen/Qwen2.5-VL-7B-Instruct,Qwen,image-text-to-text,8892121,1684,
-2026-08-25,Qwen/Qwen2.5-1.5B-Instruct,Qwen,text-generation,8859994,806,
-2026-08-25,google/gemma-4-31B-it,google,image-text-to-text,8792968,3627,
-2026-08-25,coqui/XTTS-v2,coqui,text-to-speech,8291354,3743,
-2026-08-25,facebook/contriever,facebook,,8043189,96,
-2026-08-25,laion/clap-htsat-fused,laion,audio-classification,7882414,126,
-2026-08-25,cross-encoder/ms-marco-MiniLM-L4-v2,cross-encoder,text-ranking,7800833,28,
-2026-08-25,Qwen/Qwen3.5-4B,Qwen,image-text-to-text,7695410,843,
-2026-08-25,openai/whisper-large-v3-turbo,openai,automatic-speech-recognition,7667996,3261,
+2026-09-02,sentence-transformers/all-MiniLM-L6-v2,sentence-transformers,sentence-similarity,255143740,5353,
+2026-09-02,cross-encoder/ms-marco-MiniLM-L6-v2,cross-encoder,text-ranking,86800084,309,
+2026-09-02,google-bert/bert-base-uncased,google-bert,fill-mask,69651344,2832,
+2026-09-02,BAAI/bge-small-en-v1.5,BAAI,feature-extraction,67786780,542,
+2026-09-02,google/electra-base-discriminator,google,,56965269,157,
+2026-09-02,sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2,sentence-transformers,sentence-similarity,46622739,1360,
+2026-09-02,BAAI/bge-m3,BAAI,sentence-similarity,37487482,3453,
+2026-09-02,amazon/chronos-2,amazon,time-series-forecasting,28325839,417,
+2026-09-02,sentence-transformers/all-mpnet-base-v2,sentence-transformers,sentence-similarity,24576514,1347,
+2026-09-02,google-t5/t5-small,google-t5,translation,23282469,596,
+2026-09-02,Qwen/Qwen3-0.6B,Qwen,text-generation,22741013,1564,
+2026-09-02,Comfy-Org/MiniMax-H3,Comfy-Org,,22661854,1644,
+2026-09-02,FacebookAI/xlm-roberta-base,FacebookAI,fill-mask,21313287,890,
+2026-09-02,openai/clip-vit-base-patch32,openai,zero-shot-image-classification,20562228,1076,
+2026-09-02,BAAI/bge-reranker-v2-m3,BAAI,text-classification,18150311,1153,
+2026-09-02,timm/mobilenetv3_small_100.lamb_in1k,timm,image-classification,18115838,102,
+2026-09-02,nomic-ai/nomic-embed-text-v1.5,nomic-ai,sentence-similarity,16520519,897,
+2026-09-02,trl-internal-testing/tiny-Qwen2ForCausalLM-2.5,trl-internal-testing,text-generation,16346646,20,
+2026-09-02,openai-community/gpt2,openai-community,text-generation,14502665,3508,
+2026-09-02,BAAI/bge-large-en-v1.5,BAAI,feature-extraction,13639795,718,
+2026-09-02,Qwen/Qwen3-8B,Qwen,text-generation,13632574,1334,
+2026-09-02,Qwen/Qwen3.6-35B-A3B-FP8,Qwen,image-text-to-text,13314395,365,
+2026-09-02,jonatasgrosman/wav2vec2-large-xlsr-53-japanese,jonatasgrosman,automatic-speech-recognition,13047619,62,
+2026-09-02,Comfy-Org/stable-diffusion-v1-5-archive,Comfy-Org,,12873043,120,
+2026-09-02,unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF,unsloth,text-generation,12706054,944,
+2026-09-02,Qwen/Qwen3.5-9B,Qwen,image-text-to-text,12621674,1886,
+2026-09-02,intfloat/multilingual-e5-small,intfloat,sentence-similarity,12226559,394,
+2026-09-02,timm/efficientnet_b3.ra2_in1k,timm,image-classification,11984066,5,
+2026-09-02,BAAI/bge-base-en-v1.5,BAAI,feature-extraction,11838084,467,
+2026-09-02,argmaxinc/whisperkit-coreml,argmaxinc,automatic-speech-recognition,11704093,204,
+2026-09-02,hexgrad/Kokoro-82M,hexgrad,text-to-speech,11656973,6792,
+2026-09-02,facebook/opt-125m,facebook,text-generation,11139753,294,
+2026-09-02,Qwen/Qwen2.5-7B-Instruct,Qwen,text-generation,10645526,1571,
+2026-09-02,nvidia/Qwen3.6-35B-A3B-NVFP4,nvidia,text-generation,10606938,581,
+2026-09-02,FacebookAI/roberta-base,FacebookAI,fill-mask,10010113,638,
+2026-09-02,sentence-transformers/paraphrase-multilingual-mpnet-base-v2,sentence-transformers,sentence-similarity,9878113,492,
+2026-09-02,Bingsu/adetailer,Bingsu,,9869144,761,
+2026-09-02,pyannote/speaker-diarization-3.1,pyannote,automatic-speech-recognition,9630770,3292,
+2026-09-02,Qwen/Qwen3-VL-8B-Instruct,Qwen,image-text-to-text,9584763,1074,
+2026-09-02,lpiccinelli/unidepth-v2-vitl14,lpiccinelli,,9550085,50,
+2026-09-02,unsloth/Qwen3.8-27B-GGUF,unsloth,,9354057,3337,
+2026-09-02,laion/clap-htsat-fused,laion,audio-classification,9060689,126,
+2026-09-02,google/gemma-4-31B-it,google,image-text-to-text,8282072,3689,
+2026-09-02,google/gemma-4-26B-A4B-it,google,image-text-to-text,8189743,1464,
+2026-09-02,Qwen/Qwen3.6-27B-FP8,Qwen,image-text-to-text,8157861,350,
+2026-09-02,Qwen/Qwen2.5-VL-7B-Instruct,Qwen,image-text-to-text,8028205,1692,
+2026-09-02,facebook/contriever,facebook,,7989793,96,
+2026-09-02,coqui/XTTS-v2,coqui,text-to-speech,7786333,3756,
+2026-09-02,Qwen/Qwen2.5-1.5B-Instruct,Qwen,text-generation,7684224,813,
+2026-09-02,Qwen/Qwen2.5-3B-Instruct,Qwen,text-generation,7635044,557,

ai-model-popularity.json

@@ -1,518 +1,518 @@
-{
-  "name": "Datamata AI Model Popularity Index",
-  "version": "1.0",
-  "description": "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.",
-  "source": "https://www.datamatastudios.com/datasets",
-  "license": {
-    "name": "CC BY 4.0",
-    "url": "https://creativecommons.org/licenses/by/4.0/"
-  },
-  "generated_at": "2026-08-25T01:44:48.707291+00:00",
-  "latest_snapshot_date": "2026-08-25",
-  "tasks": [
-    "audio-classification",
-    "automatic-speech-recognition",
-    "feature-extraction",
-    "fill-mask",
-    "image-classification",
-    "image-text-to-text",
-    "sentence-similarity",
-    "text-classification",
-    "text-generation",
-    "text-ranking",
-    "text-to-speech",
-    "time-series-forecasting",
-    "translation",
-    "zero-shot-image-classification"
-  ],
-  "columns": [
-    {
-      "name": "snapshot_date",
-      "type": "string",
-      "description": "UTC date the snapshot was taken (YYYY-MM-DD)."
-    },
-    {
-      "name": "model_id",
-      "type": "string",
-      "description": "Hugging Face model identifier (e.g. meta-llama/Llama-3-8B)."
-    },
-    {
-      "name": "author",
-      "type": "string",
-      "description": "Owning org or user (the part of model_id before the slash). Blank for un-namespaced models."
-    },
-    {
-      "name": "pipeline_tag",
-      "type": "string",
-      "description": "Primary task the model is tagged with (e.g. text-generation, text-to-image). Blank if untagged."
-    },
-    {
-      "name": "downloads",
-      "type": "number",
-      "description": "Hugging Face downloads in the trailing 30 days on the snapshot date."
-    },
-    {
-      "name": "likes",
-      "type": "number",
-      "description": "Hugging Face likes on the snapshot date."
-    },
-    {
-      "name": "trending_score",
-      "type": "number",
-      "description": "Hugging Face trending score on the snapshot date. Blank for models that ranked by downloads only."
-    }
-  ],
-  "row_count": 50,
-  "rows": [
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "sentence-transformers/all-MiniLM-L6-v2",
-      "author": "sentence-transformers",
-      "pipeline_tag": "sentence-similarity",
-      "downloads": 255841858,
-      "likes": 5252,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "google-bert/bert-base-uncased",
-      "author": "google-bert",
-      "pipeline_tag": "fill-mask",
-      "downloads": 95424377,
-      "likes": 2739,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "cross-encoder/ms-marco-MiniLM-L6-v2",
-      "author": "cross-encoder",
-      "pipeline_tag": "text-ranking",
-      "downloads": 87612957,
-      "likes": 301,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "BAAI/bge-small-en-v1.5",
-      "author": "BAAI",
-      "pipeline_tag": "feature-extraction",
-      "downloads": 71619849,
-      "likes": 538,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "google/electra-base-discriminator",
-      "author": "google",
-      "pipeline_tag": "",
-      "downloads": 54698880,
-      "likes": 154,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "sentence-transformers/paraphrase-multilingual-MiniLM-L12-v2",
-      "author": "sentence-transformers",
-      "pipeline_tag": "sentence-similarity",
-      "downloads": 54529466,
-      "likes": 1354,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "BAAI/bge-m3",
-      "author": "BAAI",
-      "pipeline_tag": "sentence-similarity",
-      "downloads": 36411088,
-      "likes": 3427,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "amazon/chronos-2",
-      "author": "amazon",
-      "pipeline_tag": "time-series-forecasting",
-      "downloads": 35054213,
-      "likes": 407,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "lpiccinelli/unidepth-v2-vitl14",
-      "author": "lpiccinelli",
-      "pipeline_tag": "",
-      "downloads": 26310151,
-      "likes": 50,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "sentence-transformers/all-mpnet-base-v2",
-      "author": "sentence-transformers",
-      "pipeline_tag": "sentence-similarity",
-      "downloads": 24860325,
-      "likes": 1343,
-      "trending_score": null
-    },
-    {
-      "snapshot_date": "2026-08-25",
-      "model_id": "Qwen/Qwen3-0.6B",
-      "author": "Qwen",
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