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'.
- Rows
- 250
- Columns
- 19
- Source cadence
- Weekly
- Last refreshed
- Sep 25, 2026
- Theme
- technology
| Column | 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) |
First 10 sample rows — a preview, not the complete dataset.
| 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 |
Profiled Sep 25, 2026 from snapshot
Measured- Completeness
- 100%
- Rows
- 250
- Columns
- 19
- Columns with gaps
- 0
| Column | Missing | Distinct | Range | 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 → 16,230median 1,607 | 6 outside 1st–99th percentile |
| downloadsbigint | 0% | 262 | 0 → 250,598,416median 2,821,643 | 3 outside 1st–99th percentile |
| created_atvarchar | 0% | 244 | — |
|
| age_daysdouble | 0% | 207 | 4.5 → 1,667median 434.35 | 3 outside 1st–99th percentile |
| likes_per_daydouble | 0% | 201 | 0.01 → 493.14median 3.88 | 5 outside 1st–99th percentile |
| downloads_per_daydouble | 0% | 260 | 0 → 387,867median 6,441 | 3 outside 1st–99th percentile |
| trend_scoredouble | 0% | 200 | 0.4 → 72.17median 1.49 | 5 outside 1st–99th percentile |
| trend_rankbigint | 0% | 265 | 1 → 250median 125.5 | 6 outside 1st–99th percentile |
- Current
20260925T044841Z-d52789e11ac1 · sha256 d52789e11ac1…
250 rows · first snapshot
Point any LLM at the metadata endpoint — the documentation above is machine-readable too (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)API endpoint: https://datazimuts.com/v1/datasets/hf_trending_signals/hf_trending_models_weekly
Tip: fetch /llms.txt for the full machine-readable catalog.
Where this data comes from and what was made from it. Other people's work shows as counts; only shared projects are named.
Cite this snapshot
Pinned to snapshot 20260925T044841Z-d52789e11ac1 and its content hash, so readers get exactly the data you used.
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/en/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/en/datasets/hf_trending_signals/hf_trending_models_weekly?snapshot=20260925T044841Z-d52789e11ac1}},
note = {Snapshot 20260925T044841Z-d52789e11ac1, sha256 d52789e11ac1dab7acc9b8ad34d788310a566a3a44756bc5ea27f41c39341e57; accessed 2026-09-25}
}Embed a table or a chart
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<iframe src="https://datazimuts.com/embed/chart?dataset=hf_trending_signals%2Fhf_trending_models_weekly&lang=en&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>
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