Trending models on Hugging Face (weekly)
Weekly popularity-velocity ranking of trending models on the Hugging Face Hub. Two sweeps of the official keyless Hub API (https://huggingface.co/api/models), one sorted by likes and one by downloads (top 2 pages each), are merged and deduped on model id. Each model carries its license tag normalized to a canonical id (informational, not legal advice), its pipeline tag normalized to a coarse modality (text, vision, audio, video, multimodal, other, unknown), and author/name parts. Popularity velocity is likes and downloads per day since model creation (age floored at 1 day); the trend_score is a documented 0-100 composite = 50% min-max-normalized likes/day + 50% min-max-normalized downloads/day, and trend_rank orders by trend_score descending (ties: likes desc, then model id). Columns: ISO week, fetch timestamp, model id / URL / author / name, pipeline tag, library name, modality, normalized license id, comma-joined tags, likes, downloads, creation timestamp, age in days, likes/day, downloads/day, trend score (0-100), trend rank. Primary key: (week, model_id). Cadence: weekly. Nullability: pipeline_tag, library_name and license_spdx may be empty when the author supplied none; likes, downloads, trend_score and trend_rank are never null. Caveats: likes/downloads are cumulative totals, so per-day rates are lifetime averages, not trailing-week gains; the rank therefore favors models with sustained momentum as well as fast risers; license_spdx comes from author-applied tags and may be missing or wrong — verify before use; popularity is not quality. Sample use: order by trend_rank for the week's hottest models, or filter modality = 'vision'.
Les titres et les descriptions proviennent des sources de données, en anglais.
- Lignes
- 250
- Colonnes
- 19
- Cadence de la source
- Hebdomadaire
- Dernière actualisation
- 25 sept. 2026
- Thème
- technology
| Colonne | Type | Description |
|---|---|---|
| week | string | ISO week of the fetch (e.g. 2026-W39). (unit: ISO week) |
| fetched_at | string | — |
| model_id | string | — |
| model_url | string | Canonical public model page URL (https://huggingface.co/<author>/<name>); never null. (unit: url) |
| author | string | — |
| model_name | string | — |
| pipeline_tag | string | — |
| library_name | string | — |
| modality | string | Coarse modality normalized from the Hub pipeline tag: text, vision, audio, video, multimodal, other, or unknown (no tag set). Deterministic rule set. (unit: category) |
| license_spdx | string | License id extracted from the model's license:<id> tag, with common values normalized to canonical SPDX spelling. Informational only — verify before use. Empty when the author set no license tag. (unit: license) |
| tags | string | — |
| likes | integer | — |
| downloads | integer | — |
| created_at | string | — |
| age_days | float | Days from model creation to fetch, floored at 1.0. (unit: days) |
| likes_per_day | float | Likes divided by age_days (age floored at 1 day). Likes are cumulative, so this is a lifetime average, not a trailing-week gain. (unit: likes/day) |
| downloads_per_day | float | Downloads divided by age_days (age floored at 1 day). Downloads are cumulative, so this is a lifetime average. (unit: downloads/day) |
| trend_score | float | Popularity-velocity composite: 50% min-max-normalized likes_per_day + 50% min-max-normalized downloads_per_day, scaled 0-100 within the snapshot. (unit: 0-100) |
| trend_rank | integer | Rank by trend_score descending (1 = hottest); ties broken by total likes, then model id. (unit: rank) |
10 premières lignes d’exemple — un aperçu, pas le jeu de données complet.
| week | fetched_at | model_id | model_url | author | model_name | pipeline_tag | library_name | modality | license_spdx | tags | likes | downloads | created_at | age_days | likes_per_day | downloads_per_day | trend_score | trend_rank |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | prism-ml/Ternary-Bonsai-2-27B-gguf | https://huggingface.co/prism-ml/Ternary-Bonsai-2-27B-gguf | prism-ml | Ternary-Bonsai-2-27B-gguf | text-generation | llama.cpp | text | Apache-2.0 | 2-bit,base_model:Qwen/Qwen3.8-27B,base_model:quantized:Qwen/Qwen3.8-27B,bonsai,conversational,cuda,endpoints_compatible,gguf,hybrid-attention,license:apache-2.0,llama-cpp,llama.cpp,metal,on-device,prismml,region:us,ternary,text-generation | 2 043 | 2 991 233 | 2026-09-16T23:40:56+0000 | 8,2 | 248,73 | 364 180,64 | 72,17 | 1 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | Comfy-Org/MiniMax-H3 | https://huggingface.co/Comfy-Org/MiniMax-H3 | Comfy-Org | MiniMax-H3 | — | diffusion-single-file | unknown | other | base_model:MiniMaxAI/MiniMax-H3,base_model:finetune:MiniMaxAI/MiniMax-H3,comfyui,diffusion-single-file,license:other,region:us | 1 995 | 21 820 807 | 2026-07-30T22:36:12+0000 | 56,3 | 35,46 | 387 866,5 | 53,6 | 2 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | abenzerps/Qwen-Image-2.1-Uncensored-GGUF | https://huggingface.co/abenzerps/Qwen-Image-2.1-Uncensored-GGUF | abenzerps | Qwen-Image-2.1-Uncensored-GGUF | text-to-image | gguf | vision | other | base_model:Qwen/Qwen-Image-2.1,base_model:quantized:Qwen/Qwen-Image-2.1,comfyui,comfyui-gguf,gguf,image-generation,license:other,qwen,region:us,text-to-image | 1 656 | 575 697 | 2026-09-20T15:51:13+0000 | 4,5 | 364,77 | 126 811,4 | 53,33 | 3 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | convaiinnovations/laya | https://huggingface.co/convaiinnovations/laya | convaiinnovations | laya | text-classification | transformers | text | Apache-2.0 | calibrated-decisions,classification,commercial-use,endpoints_compatible,guardrails,laya,license:apache-2.0,moderation,region:us,reinforcement-learning,rlcd,routing,safetensors,scoring,system-one,text-classification,transformers | 3 446 | 0 | 2026-09-18T05:05:55+0000 | 7 | 493,14 | 0 | 50 | 4 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | Qwen/Qwen3.8-27B | https://huggingface.co/Qwen/Qwen3.8-27B | Qwen | Qwen3.8-27B | image-text-to-text | transformers | multimodal | Apache-2.0 | conversational,deploy:azure,deploy:sagemaker,endpoints_compatible,eval-results,image-text-to-text,license:apache-2.0,qwen3_5,region:us,safetensors,transformers | 16 230 | 6 765 008 | 2026-08-05T08:22:59+0000 | 50,9 | 319,17 | 133 035,73 | 49,51 | 5 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | Comfy-Org/Qwen-Image-2.1 | https://huggingface.co/Comfy-Org/Qwen-Image-2.1 | Comfy-Org | Qwen-Image-2.1 | — | diffusion-single-file | unknown | other | base_model:Qwen/Qwen-Image-2.1,base_model:finetune:Qwen/Qwen-Image-2.1,comfyui,diffusion-single-file,license:other,region:us | 693 | 2 858 923 | 2026-09-15T19:41:09+0000 | 9,4 | 73,88 | 304 785,56 | 46,78 | 6 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | unsloth/Qwen3.8-27B-GGUF | https://huggingface.co/unsloth/Qwen3.8-27B-GGUF | unsloth | Qwen3.8-27B-GGUF | — | — | unknown | Apache-2.0 | base_model:Qwen/Qwen3.8-27B,base_model:quantized:Qwen/Qwen3.8-27B,conversational,endpoints_compatible,gguf,imatrix,license:apache-2.0,qwen3_5,region:us,unsloth | 4 589 | 7 063 930 | 2026-08-13T08:28:40+0000 | 42,8 | 107,1 | 164 863,61 | 32,11 | 7 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | deepseek-ai/DeepSeek-V4.1-Flash | https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash | deepseek-ai | DeepSeek-V4.1-Flash | image-text-to-text | transformers | multimodal | MIT | 8-bit,deepseek_v41,endpoints_compatible,eval-results,fp8,image-text-to-text,license:mit,region:us,safetensors,text-generation,transformers | 3 725 | 606 028 | 2026-09-10T02:17:58+0000 | 15,1 | 246,61 | 40 122,23 | 30,18 | 8 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | zai-org/GLM-5.3-Flash | https://huggingface.co/zai-org/GLM-5.3-Flash | zai-org | GLM-5.3-Flash | image-text-to-text | transformers | multimodal | MIT | arxiv:2602.15763,conversational,en,endpoints_compatible,eval-results,fp8,glm5_next,image-text-to-text,license:mit,region:us,safetensors,transformers,zh | 2 558 | 4 005 810 | 2026-08-25T06:43:14+0000 | 30,9 | 82,73 | 129 552,62 | 25,09 | 9 |
| 2026-W39 | 2026-09-25T04:48:30.768467+00:00 | Edge0/Edge0-35B-A3B-preview | https://huggingface.co/Edge0/Edge0-35B-A3B-preview | Edge0 | Edge0-35B-A3B-preview | text-generation | mlx | text | Apache-2.0 | 4-bit,arxiv:2609.18063,base_model:Qwen/Qwen3.6-35B-A3B,base_model:adapter:Qwen/Qwen3.6-35B-A3B,conversational,edge-inference,license:apache-2.0,lora,mlx,moe,prerouter,qwen3_5_moe,region:us,safetensors,ssd-offload,text-generation | 3 558 | 78 952 | 2026-09-08T13:56:18+0000 | 16,6 | 214,08 | 4 750,54 | 22,32 | 10 |
Profilé le 25 sept. 2026 à partir de l’instantané
Mesuré- Complétude
- 100 %
- Lignes
- 250
- Colonnes
- 19
- Colonnes incomplètes
- 0
| Colonne | Manquant | Distinctes | Plage | Distribution |
|---|---|---|---|---|
| weekvarchar | 0 % | 1 | — |
|
| fetched_atvarchar | 0 % | 1 | — |
|
| model_idvarchar | 0 % | 265 | — |
|
| model_urlvarchar | 0 % | 240 | — |
|
| authorvarchar | 0 % | 88 | — |
|
| model_namevarchar | 0 % | 274 | — |
|
| pipeline_tagvarchar | 0 % | 30 | — |
|
| library_namevarchar | 0 % | 29 | — |
|
| modalityvarchar | 0 % | 7 | — |
|
| license_spdxvarchar | 0 % | 14 | — |
|
| tagsvarchar | 0 % | 295 | — |
|
| likesbigint | 0 % | 231 | 3 → 16 230médiane 1 607 | 6 hors du 1er–99e centile |
| downloadsbigint | 0 % | 262 | 0 → 250 598 416médiane 2 821 643 | 3 hors du 1er–99e centile |
| created_atvarchar | 0 % | 244 | — |
|
| age_daysdouble | 0 % | 207 | 4,5 → 1 667médiane 434,35 | 3 hors du 1er–99e centile |
| likes_per_daydouble | 0 % | 201 | 0,01 → 493,14médiane 3,88 | 5 hors du 1er–99e centile |
| downloads_per_daydouble | 0 % | 260 | 0 → 387 867médiane 6 441 | 3 hors du 1er–99e centile |
| trend_scoredouble | 0 % | 200 | 0,4 → 72,17médiane 1,49 | 5 hors du 1er–99e centile |
| trend_rankbigint | 0 % | 265 | 1 → 250médiane 125,5 | 6 hors du 1er–99e centile |
- Actuelle
20260925T044841Z-d52789e11ac1 · sha256 d52789e11ac1…
250 lignes · premier instantané
Dirigez n’importe quel LLM vers le point d’accès des métadonnées — la documentation ci-dessus est aussi lisible par machine (JSON-LD + Croissant).
curl "https://datazimuts.com/v1/datasets/hf_trending_signals/hf_trending_models_weekly" | jq '{title, rows, columns_count, license}'import requests
ds = requests.get("https://datazimuts.com/v1/datasets/hf_trending_signals/hf_trending_models_weekly").json()
print(ds["title"], ds["rows"], "rows")
# Sample rows for an LLM context window
for row in ds.get("sample_rows", [])[:5]:
print(row)Point d’accès API : https://datazimuts.com/v1/datasets/hf_trending_signals/hf_trending_models_weekly
Astuce : récupérez /llms.txt pour le catalogue complet lisible par machine.
D’où viennent ces données et ce qui en a été fait. Le travail des autres apparaît sous forme de décomptes ; seuls les projets partagés sont nommés.
Citer cet instantané
Épinglé à l’instantané 20260925T044841Z-d52789e11ac1 et à son empreinte, pour que vos lecteurs obtiennent exactement les données utilisées.
Hugging Face trending models (agent-curated). (2026). Trending models on Hugging Face (weekly) [Data set, snapshot 20260925T044841Z-d52789e11ac1, sha256 d52789e11ac1]. Datazimuts. Retrieved 2026-09-25, from https://datazimuts.com/fr/datasets/hf_trending_signals/hf_trending_models_weekly?snapshot=20260925T044841Z-d52789e11ac1
@misc{dz_hf_trending_signals_hf_trending_models_w_d52789e1,
title = {{Trending models on Hugging Face (weekly)}},
author = {{Hugging Face trending models (agent-curated)}},
year = {2026},
publisher = {Datazimuts},
howpublished = {\url{https://datazimuts.com/fr/datasets/hf_trending_signals/hf_trending_models_weekly?snapshot=20260925T044841Z-d52789e11ac1}},
note = {Snapshot 20260925T044841Z-d52789e11ac1, sha256 d52789e11ac1dab7acc9b8ad34d788310a566a3a44756bc5ea27f41c39341e57; accessed 2026-09-25}
}Intégrer un tableau ou un graphique
Collez ce code dans n’importe quelle page. L’intégration est épinglée au même instantané, suit le thème clair ou sombre du lecteur et affiche toujours la source, la licence et un lien de retour.
<iframe src="https://datazimuts.com/embed/chart?dataset=hf_trending_signals%2Fhf_trending_models_weekly&lang=fr&theme=auto&snapshot=20260925T044841Z-d52789e11ac1&x=week&y=likes&agg=avg" title="Trending models on Hugging Face (weekly)" width="100%" height="380" style="border:0" loading="lazy"></iframe>
Posez une question sur ce jeu de données. Les réponses viennent uniquement de sa fiche, de son profil mesuré et de son historique, et citent les faits utilisés.