Add per-Space export, Moonbot notes, design system and analysis cache (team-only)

.gitattributes

@@ -33,3 +33,7 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+spaces_by_creator_client_2026-04.csv filter=lfs diff=lfs merge=lfs -text
+spaces_by_creator_client_2026-05.csv filter=lfs diff=lfs merge=lfs -text
+spaces_by_creator_client_2026-06.csv filter=lfs diff=lfs merge=lfs -text
+spaces_by_creator_client_apr_sep_2026_part2_jul_sep.csv filter=lfs diff=lfs merge=lfs -text

Hugging Face Design System.zip

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+size 13144149

README.md

@@ -27,7 +27,13 @@ short_description: Open models in Spaces and agent usage on the Hub, Oct 2026
   - `05_build_report.py`: inlines the data into `analysis/report_template.html` → the report
 - `*.csv`: aggregated internal extracts from the backend (Moonbot). Sources and start dates are in `table.tsv`.
 
+## Also included (team-only Space)
+
+- `spaces_by_creator_client_*.csv`: Moonbot's per-Space export (every Space created Apr–Sep 2026, incl. private and deleted ones, with creating client, agent name, visits and likes). This drives the per-Space agent charts.
+- `moonbot.txt`: the Slack thread with Moonbot's original numbers and caveats. `state_of_oss_report.html`: Moonbot's first report.
+- `Hugging Face Design System.zip`: the design system the report is styled with.
+- `analysis/cache/`: intermediate outputs, so steps can be re-run without re-fetching: classified Spaces, Hub API pull for 2026 Spaces, config-file scan stages, chart data, logs.
+
 ## Not included
 
-- Moonbot's per-Space export (`spaces_by_creator_client_*.csv`). It lists private and deleted Space IDs with visit counts. Steps 1–4 need it in the repo root to recompute the per-Space agent charts. The aggregated results are already in `analysis/cache/chart_data.json`, so `python3 analysis/05_build_report.py` rebuilds the page as-is.
-- `analysis/cache/` downloads (hub-stats parquet files, Hub API pulls). The scripts fetch them again.
+- `analysis/cache/spaces.parquet`: a 665 MB copy of the public [cfahlgren1/hub-stats](https://huggingface.co/datasets/cfahlgren1/hub-stats) `spaces.parquet`. Step 1 downloads it automatically.

analysis/cache/api_spaces_2026.parquet

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analysis/cache/cfg_families.json

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+{"Claude Code": "fam_0", "AGENTS.md (cross-agent)": "fam_1", "Codex": "fam_2", "Cursor": "fam_3", "Gemini / Antigravity": "fam_4", "Other agents": "fam_5"}

analysis/cache/cfg_hits.parquet

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analysis/cache/cfg_log.txt

@@ -0,0 +1,95 @@
+model: row group 1/63, hits so far 16
+model: row group 2/63, hits so far 16
+model: row group 3/63, hits so far 16
+model: row group 4/63, hits so far 16
+model: row group 5/63, hits so far 16
+model: row group 6/63, hits so far 16
+model: row group 7/63, hits so far 16
+model: row group 8/63, hits so far 16
+model: row group 9/63, hits so far 16
+model: row group 10/63, hits so far 16
+model: row group 11/63, hits so far 16
+model: row group 12/63, hits so far 16
+model: row group 13/63, hits so far 16
+model: row group 14/63, hits so far 16
+model: row group 15/63, hits so far 16
+model: row group 16/63, hits so far 16
+model: row group 17/63, hits so far 16
+model: row group 18/63, hits so far 16
+model: row group 19/63, hits so far 17
+model: row group 20/63, hits so far 17
+model: row group 21/63, hits so far 17
+model: row group 22/63, hits so far 17
+model: row group 23/63, hits so far 17
+model: row group 24/63, hits so far 17
+model: row group 25/63, hits so far 17
+model: row group 26/63, hits so far 17
+model: row group 27/63, hits so far 17
+model: row group 28/63, hits so far 18
+model: row group 29/63, hits so far 19
+model: row group 30/63, hits so far 20
+model: row group 31/63, hits so far 20
+model: row group 32/63, hits so far 20
+model: row group 33/63, hits so far 20
+model: row group 34/63, hits so far 23
+model: row group 35/63, hits so far 26
+model: row group 36/63, hits so far 29
+model: row group 37/63, hits so far 31
+model: row group 38/63, hits so far 37
+model: row group 39/63, hits so far 43
+model: row group 40/63, hits so far 45
+model: row group 41/63, hits so far 49
+model: row group 42/63, hits so far 51
+model: row group 43/63, hits so far 60
+model: row group 44/63, hits so far 68
+model: row group 45/63, hits so far 76
+model: row group 46/63, hits so far 82
+model: row group 47/63, hits so far 90
+model: row group 48/63, hits so far 105
+model: row group 49/63, hits so far 123
+model: row group 50/63, hits so far 133
+model: row group 51/63, hits so far 153
+model: row group 52/63, hits so far 175
+model: row group 53/63, hits so far 209
+model: row group 54/63, hits so far 239
+model: row group 55/63, hits so far 265
+model: row group 56/63, hits so far 310
+model: row group 57/63, hits so far 334
+model: row group 58/63, hits so far 361
+model: row group 59/63, hits so far 391
+model: row group 60/63, hits so far 430
+model: row group 61/63, hits so far 474
+model: row group 62/63, hits so far 642
+model: row group 63/63, hits so far 649
+space: row group 1/30, hits so far 57
+space: row group 2/30, hits so far 59
+space: row group 3/30, hits so far 66
+space: row group 4/30, hits so far 76
+space: row group 5/30, hits so far 80
+space: row group 6/30, hits so far 88
+space: row group 7/30, hits so far 103
+space: row group 8/30, hits so far 134
+space: row group 9/30, hits so far 166
+space: row group 10/30, hits so far 224
+space: row group 11/30, hits so far 282
+space: row group 12/30, hits so far 368
+space: row group 13/30, hits so far 440
+space: row group 14/30, hits so far 463
+space: row group 15/30, hits so far 506
+space: row group 16/30, hits so far 551
+space: row group 17/30, hits so far 603
+space: row group 18/30, hits so far 701
+space: row group 19/30, hits so far 857
+space: row group 20/30, hits so far 1,130
+space: row group 21/30, hits so far 1,507
+space: row group 22/30, hits so far 1,910
+space: row group 23/30, hits so far 2,530
+space: row group 24/30, hits so far 3,254
+space: row group 25/30, hits so far 5,628
+space: row group 26/30, hits so far 6,473
+space: row group 27/30, hits so far 7,512
+space: row group 28/30, hits so far 8,548
+space: row group 29/30, hits so far 9,009
+space: row group 30/30, hits so far 9,359
+datasets: checked 31,494 of 31,500 sampled, hits 14
+wrote /Users/avijit/Documents/State of OSS Oct26/analysis/cache/cfg_hits.parquet 10022 and /Users/avijit/Documents/State of OSS Oct26/analysis/cache/cfg_month_totals.parquet

analysis/cache/cfg_models_hits.parquet

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+size 31675

analysis/cache/cfg_models_totals.parquet

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analysis/cache/cfg_month_totals.parquet

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analysis/cache/cfg_spaces_hits.parquet

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analysis/cache/cfg_spaces_totals.parquet

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analysis/cache/fetch_log.txt

@@ -0,0 +1,36 @@
+page 25: 25,000 spaces, oldest 2026-09-10T01:07:29.000Z
+page 50: 50,000 spaces, oldest 2026-08-13T14:22:13.000Z
+page 75: 75,000 spaces, oldest 2026-07-21T10:05:55.000Z
+page 100: 100,000 spaces, oldest 2026-07-04T02:37:51.000Z
+page 125: 125,000 spaces, oldest 2026-06-23T08:06:22.000Z
+page 150: 150,000 spaces, oldest 2026-06-13T08:22:58.000Z
+page 175: 175,000 spaces, oldest 2026-06-03T21:59:25.000Z
+page 200: 200,000 spaces, oldest 2026-05-24T04:44:15.000Z
+page 225: 225,000 spaces, oldest 2026-05-13T14:58:14.000Z
+page 250: 250,000 spaces, oldest 2026-05-04T15:27:18.000Z
+page 275: 275,000 spaces, oldest 2026-04-25T12:45:30.000Z
+page 300: 300,000 spaces, oldest 2026-04-16T07:03:29.000Z
+page 325: 325,000 spaces, oldest 2026-04-08T06:40:59.000Z
+page 350: 350,000 spaces, oldest 2026-03-30T01:06:04.000Z
+page 375: 375,000 spaces, oldest 2026-03-18T10:11:07.000Z
+page 400: 400,000 spaces, oldest 2026-03-06T10:59:36.000Z
+page 425: 425,000 spaces, oldest 2026-02-22T22:50:14.000Z
+page 450: 450,000 spaces, oldest 2026-02-11T22:14:43.000Z
+page 475: 475,000 spaces, oldest 2026-01-30T20:35:47.000Z
+page 500: 500,000 spaces, oldest 2026-01-19T03:30:41.000Z
+page 525: 525,000 spaces, oldest 2026-01-07T04:12:22.000Z
+wrote /Users/avijit/Documents/State of OSS Oct26/analysis/cache/api_spaces_2026.parquet 536291
+wrote /Users/avijit/Documents/State of OSS Oct26/analysis/cache/api_commit_sample.parquet 4484 ok: 4483
+sample       trailer
+config-file  NaN        1119
+             claude      341
+             cursor       11
+             copilot       8
+             codex         4
+             other         1
+random       NaN        2916
+             claude       73
+             cursor        8
+             copilot       2
+             codex         1
+Name: count, dtype: int64

analysis/cache/spaces_classified.parquet

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moonbot.txt

@@ -0,0 +1,143 @@
+I've pulled numbers for all four questions. I ran out of time before attaching the CSVs and building the HTML report, so here are the headline figures. The CSVs are written to /tmp/oss/out/ and I'll attach them on your next message, if the sandbox hasn't been reset by then.
+
+1. What kinds of Spaces people build with open models
+There are 1.91M public Spaces (plus 1.06M private). 395k of the public ones (21%) link at least one public Hub model, and those get 37% of all Space visits in the last 12 months.
+Of the Spaces linking models, 285k link a model with an OSI-style license (Apache, MIT and similar). 105k link an open-weight model with a custom license (Llama, Gemma, OpenRAIL, "other"). Some Spaces fall in both groups.
+Gradio is the main way people use open models in Spaces: 302k of the 395k. Docker Spaces get the most visits overall (409M of 726M), but only 57k of them link a model.
+By the auto-assigned category: Chatbots (49k), Text Generation (29k) and Image Generation (19k) have the most Spaces using open models. Image Generation, Video Generation, Image Editing and Text Generation get the most visits (roughly 32–41M each over 12 months).
+By the linked model's task, text-generation comes first (173k Spaces), then text-to-image (44k) and sentence-similarity (31k).
+The most-linked models are Qwen2.5-Coder-32B (35k Spaces), zephyr-7b (21k), all-MiniLM-L6-v2 (20k), gpt-oss-20b (16k) and sdxl-turbo (12k). The Qwen count is probably inflated by people copying course template Spaces.
+
+
+2. Space creation over time
+I have a monthly series back to 2022, split by SDK and by whether the Space links an open model. It only counts Spaces that still exist; deleted ones aren't included.
+
+
+3. Space traffic over time (data starts Oct 2023)
+Monthly visits grew from about 17–37M in late 2023 to 60–110M in 2026. The peak was 113M in June 2026.
+The number of Spaces getting any visits in a month went from about 50k to about 320k.
+
+
+4. Agent vs human Hub usage
+huggingface_hub only started adding the agent tag to its User-Agent around April 2026, so nothing before that can be measured.
+Repo creation: agent-tagged creations rose from 1.6% of new repos in early April to 13–18% since mid-July. That's about 307k repos so far (6% of new models, 6.5% of datasets, 2.2% of Spaces). By agent: Claude Code 111k, an unrecognized agent 77k, Codex 64k, Cursor CLI 28k, Antigravity 10k.
+Hub request traffic: request logs only go back about 6 weeks, so I sampled 101 ten-minute windows between Aug 21 and Sep 30. Agent-tagged requests are about 3.5% of all requests and about 7% of huggingface_hub requests. Claude Code makes about 51% of those, then Codex, unrecognized agents, Cursor CLI and Hermes. By repo type, agents account for about 4% of model and dataset traffic but under 0.5% of Space traffic.
+Traffic to agent-created vs human-created repos (repos created Apr–Aug 2026):
+◦ Survival: about 72–77% of agent-created repos still exist, vs about 58% for scripts with no agent tag and 70–82% for browser-created repos.
+◦ Usage: the typical agent-created repo gets about as much use as human-created ones, or a bit more. Median all-time downloads are 33 for agent-created models vs 31 (scripts) and 0 (browser). Median visits are 29 for agent-created Spaces vs 12–13.
+◦ The biggest hits are still human-made: browser-created models average about 7.9k downloads each vs about 2.7k for agent-created ones.
+◦ Some of the traffic to agent-created repos is probably the agent reading back its own repo.
+
+
+Caveats for the blog: agent tagging only covers recent huggingface_hub versions. It's switched off when users set HF_HUB_DISABLE_TELEMETRY, and it misses curl or custom clients. So "agent" really means "tagged agent traffic", which is a lower bound. Space categories are AI-generated labels, and 52% of public Spaces have none.
+
+Still to do: attach the CSVs and build an HTML report with charts, and fill in the remaining request-log sample windows for a full daily series. Reply "go" and I'll pick it up from there.
+:warning: 1 error(s) during runAvijit  [10:32 AM]
+(Also in the followup: What are the models that are being attached to these spaces)
+Eliott Coyac  [10:35 AM]
+@Moon Bot continue - also see avijit's asks
+Daniel van Strien  [10:44 AM]
+Don't want to derail, but for the blog it might also be good to give a hint that some of this info can be queried directly via hub apis/#hub-client-library i.e.
+hf spaces ls --expand models --limit 100000000 --json > spaces_models.json+
+python3 -c "
+import json
+from collections import Counter
+d=json.load(open('spaces_models.json'))
+n=len(d)
+withm=[x for x in d if x.get('models')]
+print('spaces',n,'with models',len(withm), f'{len(withm)/n:.1%}')
+c=Counter()
+orgs=Counter()
+for x in withm:
+    for m in set(x['models']):
+        c[m]+=1
+    for o in {m.split('/')[0] if '/' in m else '(no org)' for m in x['models']}:
+        orgs[o]+=1
+print('distinct models',len(c))
+nm=[len(set(x['models'])) for x in withm]
+print('mean models/space',sum(nm)/len(nm),'max',max(nm),'one-model spaces',sum(1 for v in nm if v==1))
+print('models with 1 space',sum(1 for v in c.values() if v==1))
+print('--- top 40 models')
+for m,v in c.most_common(40): print(v,m)
+print('--- top 20 orgs (spaces using >=1 model from org)')
+for m,v in orgs.most_common(20): print(v,m)Moon Bot  [10:46 AM]
+State of OSS: Spaces & agent usage report (download and open in a browser) State of OSS: Spaces & agent usage report (download and open in a browser)HTML[10:46 AM]open_model_authors_in_new_spaces_quarterly.csv 
+
+quarternew_public_spaces_linking_open_modelsQwenopenaisentence-transformersfacebookstabilityaiHuggingFaceH4googlemeta-llamamicrosoftmistralaiopenai-communityNousResearchdistilbertblack-forest-labsdeepseek-aiSalesforcecross-encoderBAAIWan-AIgoogle-bertthenlperXenovaCompVisHelsinki-NLPTinyLlama2022-Q11096124291200146141042063007400590203502022-Q28240152780102402614402900431034001902022-Q31194222291751083238137028101015103510682902022-Q42641010246276140760660590410015173084002526502023-Q16065420367287200204519901750680295223092013463002023-Q27578435032751515921420143277649142374030618150316162996402023-Q38612544683756246813044886045853274521012531963011567214710282023-Q410667945754038466882594013964315772538691447302401060180269168121202024-Q111373875763221143747187790423489850270811441793532813601912323418888133
+
+[10:46 AM]CSV Every open model linked from public Spaces (84k models)CSV[10:46 AM]linked_model_task_in_new_spaces_quarterly.csv 
+
+quarternew_public_spaces_linking_open_modelstext-generation(none)text-to-imagesentence-similarityimage-text-to-textautomatic-speech-recognitiontext-classificationtext-to-speechfill-maskimage-to-imagefeature-extractionimage-classificationtranslationzero-shot-image-classificationimage-to-textsummarizationobject-detectiontext-rankingimage-to-videotext-to-video2022-Q110961982761237656122171446204869231465194002022-Q2824165262433245868773712393315736233002022-Q311942283391123579383167651872702026825113002022-Q426413164707786363331021513117276613480281208117002023-Q16065863996142898242342794920813283565103136149103106210462023-Q2757820691541995424524085112225412861293792842514512221841701212023-Q386123238158612135443853644935133431517527429722236517713651482023-Q4106673841220813375701218045534543722792703003003313472151383922332024-Q111373337324661652500134951524695448304351422279338422181359282132
+
+[10:46 AM]open_model_authors_linked_in_spaces.csv 
+
+model_authordistinct_models_linkedspace_linksvisits_12m_of_linking_spacesQwen36479351160385835facebook69948992260370528openai303810055523819Helsinki-NLP15293094951757334google54628731161789170sentence-transformers742735773961014meta-llama632550623340792stabilityai792450016404747HuggingFaceH413217942112655
+
+[10:46 AM]spaces_by_ai_category.csv 
+
+ai_categorypublicwith_open_modelvisits_12mvisits_12m_with_open_model(uncategorized)9972561827211135912034311695Other3348602360817755331130111236Chatbots94770488663606543413108410Text Generation80862290417130945233486848Data Visualization456283253261097522771330Text Analysis36803128931567059010377431Image Generation31529190085230351141310533Image29979384698258324035887Question Answering252451092474745511874649
+
+[10:46 AM]spaces_by_sdk.csv 
+
+sdkpublicwith_open_modelvisits_12mvisits_12m_with_open_modelgradio713411302207268045975189181622static54711710000407574619839654docker5012465731440937903969910018streamlit1319942294779886153028581(none)15683224412184211833
+
+[10:46 AM]spaces_by_linked_model_task.csv 
+
+linked_model_pipeline_tagspacesvisits_12mtext-generation17318648133093(none)72172119344717text-to-image4427153964192sentence-similarity3154310248644image-text-to-text1877142524240automatic-speech-recognition1700414334100text-classification1641721389192text-to-speech1380416167786fill-mask109749638518
+
+[10:46 AM]space_creation_monthly.csv 
+
+monthcreated_surviving_allpublicprivatepublic_with_open_modelsdk_(none)sdk_dockersdk_gradiosdk_staticsdk_streamlit2022-03419528471348109610618081458782022-04106178527626040545711652022-051381101137027171726801972022-0617041145559293716842012522022-0719251417508401439181453472022-082361160176035211111571372952022-0933212235108644112414951995252022-1043912713167856618419062045812022-1159163423249377120122337259795
+
+[10:46 AM]space_visits_monthly.csv 
+
+monthvisitsspaces_with_visits2023-10882618129352023-1117583558480202023-1236778232689562024-0121389153619812024-0213325800636652024-0324053093817912024-0436347576828622024-0539821551933492024-063959771491710
+
+[10:46 AM]repo_creation_weekly_agent_vs_human.csv 
+
+week_starttotalagentbrowserhuggingface_hub_no_agent_tagother_clientno_user_agent_recordedmodel_totalmodel_agentdataset_totaldataset_agentspace_totalspace_agentbucket_totalbucket_agentagent_pct2021-11-1046600004663880430350000.02021-11-173096000030962355047902620000.02021-11-242026000020261436038502050000.02021-12-012616000026162031044001450000.02021-12-082528000025281928043001700000.02021-12-152450000024501698049802540000.02021-12-221814000018141329028701980000.02021-12-292079000020791543037101650000.02022-01-052749000027491948052702740000.0
+
+[10:46 AM]repo_creation_weekly_by_agent.csv 
+
+week_startclaude-codeunknowncodexcursor-cliantigravitypihermes-agentopencodeopenclawcursorgeminizedother_agents2026-04-01101077137911650002375360112026-04-0829728167919923100066169350232026-04-153525755162351400005884420392026-04-22276563996242027150014167420432026-04-296723137105830813832008771480352026-05-0627441061583304209180345044580362026-05-1320050681592363150180537538540182026-05-2035654082588269250510655610670452026-05-271357070151848824935061107549054
+
+[10:46 AM]hub_requests_agent_share_daily_sampled.csv 
+
+daten_10min_samplessampled_requestsagent (hf_hub tagged)huggingface_hubbrowserhuggingface.jsothernoneagent_pct_of_allagent_pct_of_huggingface_hub2026-08-21446177215154187122838301491014401546735414195453.346.322026-08-22441815517134457420821654429526101402431613297123.226.072026-08-23439167078115006619129598439992601306689614205922.945.672026-08-24444967581162218023012750496408501384946815190983.616.582026-08-25449045212192885626017945485657601433817219036633.936.92026-08-26450063575183776325686758481866401543579322845973.676.682026-08-27448748452172909323388833483531801652728422679243.556.882026-08-28448598487175650422117827482429501765109522487663.617.362026-08-29440738231177888619510602481611301262943220031984.378.36
+
+[10:46 AM]hub_requests_by_agent_sampled.csv 
+
+agentsampled_requestspct_of_agent_requestsclaude-code3573211652.5codex999953714.69unknown898955213.21cursor-cli50841277.47hermes-agent47143806.93antigravity15147272.23pi5429220.8openclaw3895030.57opencode3025240.44
+
+[10:46 AM]hub_requests_by_repo_type_sampled.csv 
+
+repoTypeagent (hf_hub tagged)huggingface_hubbrowserhuggingface.jsothernoneagent_pctmodel37760814637102336641060840204701487755784.0dataset18667839171765803417583670140269244544103294.37(no repo)101180109178683283196357099874879357968393.15bucket116081195471251189795301710951966029020.6space27395627802342894956102547828150051280.44kernel7988041191310789012662015.5container005018432010.0
+
+[10:46 AM]agent_vs_human_repo_cohort_apr_aug_2026.csv 
+
+repoTypecreator_clientcreatedstill_existsnow_privatepublic_nowwith_any_traffictraffic_ge_100trafficlikeswith_likespct_still_existspct_public_with_traffictraffic_per_public_repotraffic_metricdatasetagent752655812331992261312612718190320330306783220977.2100.01225.9downloadsAllTimedatasethuman/script: huggingface_hub (no agent tag)493125285183136431148752148745784569720273722196756257.8100.0653.5downloadsAllTimedatasethuman: browser115689910413301458027580253281312455330527276795178.7100.02146.5downloadsAllTimedatasetother client210101248776774810481022184899502653931259.4100.010186.1downloadsAllTimemodelagent115978838572687156986403161210915173594952673619672.370.72662.7downloadsAllTimemodelhuman/script: huggingface_hub (no agent tag)733845423858138806285052192487686806807514741486272412257.867.52388.2downloadsAllTimemodelhuman: browser1845431516204627610534447693204748345164012912571465682.245.37921.8downloadsAllTimemodelother client293081449034471104310503954343399238953649.49.5393.3downloadsAllTimespaceagent3044123547491018637164103206732645117550190777.488.1393.1visits (since creation)
+
+[10:47 AM]@Eliott Coyac @Avijit All the files are attached above: an HTML report with 18 charts (download it and open it in a browser) plus the raw CSVs. Every series now goes back as far as the data allows. Not every source goes back far, so here's where each one actually starts:DatasetStartsWhySpaces by type, SDK and linked models (spaces_by_*, space_creation_monthly)
+Mar 2022Earliest Space record; only Spaces that still exist are countedSpace visits (space_visits_monthly)
+Oct 30 2023The visit counter on Space pages started thenRepo creation, all types, including deleted repos (repo_creation_weekly_*)
+Nov 15 2021First repo.create event
+↳ split by client (browser vs huggingface_hub)
+Oct 19 2023The User-Agent wasn't recorded on events before that (shown as no_user_agent_recorded)
+↳ agent vs human~Apr 1 2026The agent/<name> tag only exists from then
+Hub request traffic, agent vs human (hub_requests_*_sampled)
+Aug 21 2026The request logs only keep about 6 weeks. I took four 10-minute samples per day (166 samples, 42 days)Agent- vs human-created repos: survival and traffic (agent_vs_human_repo_cohort)Repos created Apr–Aug 2026Matches when the agent tag started; each repo has had at least 30 daysModels attached to Spaces (Avijit's follow-up)
+• open_models_linked_in_spaces_all.csv lists every one of the 84k public models linked from a public Space. For each model it gives the author, the number of Spaces linking it, 12-month visits to those Spaces, task, library, license (raw and grouped), whether it's gated, parameter count, and the model's own downloads and likes.
+• Top authors by number of Space links: Qwen (79k), facebook (49k), openai (38k), Helsinki-NLP (31k), google (29k), sentence-transformers (27k), meta-llama (26k), stabilityai (25k).
+• Top single models: Qwen2.5-Coder-32B (35k Spaces), zephyr-7b-beta (21k), all-MiniLM-L6-v2 (20k), gpt-oss-20b (16k), sdxl-turbo (12k). The Qwen-Coder count is probably inflated by people copying course template Spaces.
+• Two more files show the trend per quarter: open_model_authors_in_new_spaces_quarterly.csv and linked_model_task_in_new_spaces_quarterly.csv.
+
+Updated headline numbers
+• Repo creation: agents made about 307k repos since April. They're now 13–18% of weekly creations (Claude Code 111k, an unrecognized agent 77k, Codex 64k, Cursor CLI 28k).
+• Hub requests: about 3.5% of all requests and about 7% of huggingface_hub requests are agent-tagged. Claude Code makes about half of them. By repo type, agents account for about 4% of model and dataset traffic but only about 0.4% of Space traffic.
+• Agent-created repos: they survive as well as or better than human-created ones (72–77% still exist). The typical agent-created repo gets as much use as human-created ones, or a bit more. Median all-time downloads are 33 for agent-created models vs 31 (scripts) and 0 (browser). Median visits are 29 for agent-created Spaces vs 12–13. The biggest hits are still human-made: browser-created models average about 7.9k downloads vs about 2.7k for agent-created ones.
+
+Caveats
+• "Agent" means "tagged by huggingface_hub", so it's a lower bound. The tag is missing when telemetry is disabled and for curl or custom clients.
+• ai_category is an AI-generated label, and about 52% of public Spaces don't have one.
+• New Spaces linking open models dropped from 62k in Q2 2026 to 27k in Q3, and Q3 was almost complete when I pulled this. I haven't checked whether that's real or because linked models get filled in some time after a Space is created. Check it before quoting.
+• In the author file, visits are counted once per linked model, so a Space linking several models from the same author is counted more than once.

spaces_by_creator_client_2026-04.csv

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spaces_by_creator_client_apr_sep_2026_part2_jul_sep.csv

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+size 17314494

state_of_oss_report.html

@@ -0,0 +1,152 @@
+<!doctype html><html><head><meta charset="utf-8"><title>State of OSS — Spaces & agent usage</title>
+<script src="https://cdn.jsdelivr.net/npm/chart.js@4"></script>
+<style>body{font-family:system-ui;max-width:1100px;margin:auto;padding:20px}canvas{max-height:460px}.c{margin:40px 0}.n{color:#666;font-size:13px}pre{background:#f5f5f5;padding:10px;font-size:12px}</style></head><body>
+<h1>State of OSS — Spaces & agentic usage</h1><p class="n">Generated 2026-10-01 from Hub MongoDB (space_info_cache, model_info_cache, events) and Elasticsearch (by-24h-spaces-visits, ds-all-requests-reports). Every series starts at the earliest data available.</p>
+<h3>Totals</h3><pre>{
+ &quot;totals&quot;: {
+  &quot;public&quot;: 1909451,
+  &quot;public_with_open_model&quot;: 394712,
+  &quot;visits&quot;: 726292932,
+  &quot;visits_with_open_model&quot;: 271971708,
+  &quot;private&quot;: 1059267
+ },
+ &quot;license_class_spaces&quot;: {
+  &quot;permissive/OSI-style&quot;: 284624,
+  &quot;no license tag&quot;: 65512,
+  &quot;open-weight w/ custom or restricted license&quot;: 105178
+ },
+ &quot;library_spaces&quot;: [
+  [
+   &quot;transformers&quot;,
+   277609
+  ],
+  [
+   &quot;diffusers&quot;,
+   58197
+  ],
+  [
+   &quot;(none)&quot;,
+   48690
+  ],
+  [
+   &quot;sentence-transformers&quot;,
+   36736
+  ],
+  [
+   &quot;vllm&quot;,
+   6613
+  ],
+  [
+   &quot;transformers.js&quot;,
+   4179
+  ],
+  [
+   &quot;fairseq&quot;,
+   3654
+  ],
+  [
+   &quot;peft&quot;,
+   3124
+  ],
+  [
+   &quot;diffusion-single-file&quot;,
+   2319
+  ],
+  [
+   &quot;kernels&quot;,
+   1706
+  ],
+  [
+   &quot;ultralytics&quot;,
+   1690
+  ],
+  [
+   &quot;open_clip&quot;,
+   1322
+  ],
+  [
+   &quot;anime_segmentation&quot;,
+   1099
+  ],
+  [
+   &quot;pyannote-audio&quot;,
+   1090
+  ],
+  [
+   &quot;keras&quot;,
+   1055
+  ],
+  [
+   &quot;pytorch&quot;,
+   1015
+  ],
+  [
+   &quot;nemo&quot;,
+   815
+  ],
+  [
+   &quot;speechbrain&quot;,
+   804
+  ],
+  [
+   &quot;birefnet&quot;,
+   790
+  ],
+  [
+   &quot;tf-keras&quot;,
+   753
+  ],
+  [
+   &quot;wan2.2&quot;,
+   720
+  ],
+  [
+   &quot;ctranslate2&quot;,
+   620
+  ],
+  [
+   &quot;coqui&quot;,
+   595
+  ],
+  [
+   &quot;hunyuan3d-2&quot;,
+   563
+  ],
+  [
+   &quot;chatterbox&quot;,
+   497
+  ]
+ ]
+}</pre>
+<div class="c"><h3>Spaces created per month (surviving Spaces only)</h3><canvas id="c0"></canvas><div class="n">From space_info_cache (_id timestamp). Deleted Spaces are not counted; see repo_creation_weekly for gross creations incl. deleted.</div></div>
+<div class="c"><h3>Public Spaces created per month that link &gt;=1 open (public Hub) model</h3><canvas id="c1"></canvas><div class="n"></div></div>
+<div class="c"><h3>Public Spaces created per month by SDK</h3><canvas id="c2"></canvas><div class="n"></div></div>
+<div class="c"><h3>Space visits per month (all Spaces)</h3><canvas id="c3"></canvas><div class="n">Source: by-24h-spaces-visits (same data as the visit counter on Space pages). Starts Oct 30 2023 — earliest available.</div></div>
+<div class="c"><h3>Spaces receiving &gt;=1 visit per month</h3><canvas id="c4"></canvas><div class="n"></div></div>
+<div class="c"><h3>Public Spaces by AI category (top 25)</h3><canvas id="c5"></canvas><div class="n">ai_category is an AI-generated label; ~52% of public Spaces are uncategorized.</div></div>
+<div class="c"><h3>Last-12-month visits to Spaces using open models, by category</h3><canvas id="c6"></canvas><div class="n"></div></div>
+<div class="c"><h3>Spaces by task of the linked open model</h3><canvas id="c7"></canvas><div class="n"></div></div>
+<div class="c"><h3>Most-linked open models in public Spaces (top 30)</h3><canvas id="c8"></canvas><div class="n"></div></div>
+<div class="c"><h3>Model authors most linked by Spaces (top 25)</h3><canvas id="c9"></canvas><div class="n"></div></div>
+<div class="c"><h3>New public Spaces per quarter linking each model author (top 12)</h3><canvas id="c10"></canvas><div class="n"></div></div>
+<div class="c"><h3>Repos created per week by client</h3><canvas id="c11"></canvas><div class="n">From events repo.create (back to Nov 2021). User-Agent is only recorded from Oct 2023, so before that everything is &quot;other_client&quot;/none. The agent tag exists from ~Apr 2026.</div></div>
+<div class="c"><h3>Agent share of repo creation (%)</h3><canvas id="c12"></canvas><div class="n"></div></div>
+<div class="c"><h3>Agent-created repos per week by agent</h3><canvas id="c13"></canvas><div class="n"></div></div>
+<div class="c"><h3>Hub API/web requests: agent share (sampled, %)</h3><canvas id="c14"></canvas><div class="n">ds-all-requests-reports keeps ~6 weeks (earliest ~Aug 20 2026). 4 x 10-minute samples per day.</div></div>
+<div class="c"><h3>Agent-tagged Hub requests by agent (sampled)</h3><canvas id="c15"></canvas><div class="n"></div></div>
+<div class="c"><h3>Repos created Apr–Aug 2026: % still existing (Oct 1 2026)</h3><canvas id="c16"></canvas><div class="n"></div></div>
+<div class="c"><h3>Repos created Apr–Aug 2026: avg traffic per public repo</h3><canvas id="c17"></canvas><div class="n"></div></div>
+<h3>Agent vs human cohort — median / p90 traffic per public repo</h3><pre>(&#x27;dataset&#x27;, &#x27;agent&#x27;) 26131 median 160 p90 1239 p99 14063 share_top1pct 54.4
+(&#x27;dataset&#x27;, &#x27;browser&#x27;) 58027 median 122 p90 1834 p99 26079 share_top1pct 64.2
+(&#x27;dataset&#x27;, &#x27;hf_hub_no_agent&#x27;) 148752 median 105 p90 635 p99 9048 share_top1pct 51.1
+(&#x27;dataset&#x27;, &#x27;other&#x27;) 4810 median 88 p90 5598 p99 169463 share_top1pct 20.7
+(&#x27;model&#x27;, &#x27;agent&#x27;) 56986 median 33 p90 486 p99 12201 share_top1pct 91.3
+(&#x27;model&#x27;, &#x27;browser&#x27;) 105344 median 0 p90 399 p99 15138 share_top1pct 97.1
+(&#x27;model&#x27;, &#x27;hf_hub_no_agent&#x27;) 285052 median 31 p90 506 p99 10029 share_top1pct 90.5
+(&#x27;model&#x27;, &#x27;other&#x27;) 11043 median 0 p90 0 p99 1351 share_top1pct 96.9
+(&#x27;space&#x27;, &#x27;agent&#x27;) 18637 median 29 p90 204 p99 4917 share_top1pct 73.3
+(&#x27;space&#x27;, &#x27;browser&#x27;) 283227 median 12 p90 110 p99 3210 share_top1pct 90.1
+(&#x27;space&#x27;, &#x27;hf_hub_no_agent&#x27;) 29179 median 13 p90 111 p99 3061 share_top1pct 84.5
+(&#x27;space&#x27;, &#x27;other&#x27;) 17285 median 21 p90 95 p99 1705 share_top1pct 81.7
+</pre>
+<script>const C=[{"title": "Spaces created per month (surviving Spaces only)", "labels": ["2022-03", "2022-04", "2022-05", "2022-06", "2022-07", "2022-08", "2022-09", "2022-10", "2022-11", "2022-12", "2023-01", "2023-02", "2023-03", "2023-04", "2023-05", "2023-06", "2023-07", "2023-08", "2023-09", "2023-10", "2023-11", "2023-12", "2024-01", "2024-02", "2024-03", "2024-04", "2024-05", "2024-06", "2024-07", "2024-08", "2024-09", "2024-10", "2024-11", "2024-12", "2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06", "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12", "2026-01", "2026-02", "2026-03", "2026-04", "2026-05", "2026-06", "2026-07", "2026-08", "2026-09", "2026-10"], "datasets": [{"label": "public", "data": [2847.0, 785.0, 1011.0, 1145.0, 1417.0, 1601.0, 2235.0, 2713.0, 3423.0, 4388.0, 5822.0, 6172.0, 9763.0, 10257.0, 9510.0, 10669.0, 16380.0, 22229.0, 10151.0, 10596.0, 12448.0, 13295.0, 13790.0, 12989.0, 18481.0, 14934.0, 15883.0, 16164.0, 16979.0, 19226.0, 18605.0, 21040.0, 22179.0, 25621.0, 27747.0, 37598.0, 40768.0, 88395.0, 95020.0, 76333.0, 90537.0, 71969.0, 103125.0, 130794.0, 94521.0, 80901.0, 71211.0, 69544.0, 74942.0, 97831.0, 85477.0, 84195.0, 49620.0, 31211.0, 32264.0, 700.0]}, {"label": "private", "data": [1348.0, 276.0, 370.0, 559.0, 508.0, 760.0, 1086.0, 1678.0, 2493.0, 9804.0, 9725.0, 9779.0, 18842.0, 23792.0, 16865.0, 17057.0, 15045.0, 20155.0, 16420.0, 18960.0, 19591.0, 23072.0, 22568.0, 19481.0, 21619.0, 21525.0, 21993.0, 22315.0, 23568.0, 22992.0, 22955.0, 27005.0, 28118.0, 24908.0, 31550.0, 35180.0, 33860.0, 35178.0, 35778.0, 31080.0, 30881.0, 39540.0, 29535.0, 25792.0, 22278.0, 17510.0, 17878.0, 19073.0, 20851.0, 22316.0, 26149.0, 26382.0, 20288.0, 14420.0, 16142.0, 374.0]}], "kind": "bar", "stacked": true, "note": "From space_info_cache (_id timestamp). Deleted Spaces are not counted; see repo_creation_weekly for gross creations incl. deleted.", "ylog": false}, {"title": "Public Spaces created per month that link >=1 open (public Hub) model", "labels": ["2022-03", "2022-04", "2022-05", "2022-06", "2022-07", "2022-08", "2022-09", "2022-10", "2022-11", "2022-12", "2023-01", "2023-02", "2023-03", "2023-04", "2023-05", "2023-06", "2023-07", "2023-08", "2023-09", "2023-10", "2023-11", "2023-12", "2024-01", "2024-02", "2024-03", "2024-04", "2024-05", "2024-06", "2024-07", "2024-08", "2024-09", "2024-10", "2024-11", "2024-12", "2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06", "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12", "2026-01", "2026-02", "2026-03", "2026-04", "2026-05", "2026-06", "2026-07", "2026-08", "2026-09", "2026-10"], "datasets": [{"label": "links open model", "data": [1096.0, 260.0, 271.0, 293.0, 401.0, 352.0, 441.0, 566.0, 771.0, 1304.0, 1717.0, 1987.0, 2361.0, 2682.0, 2523.0, 2373.0, 2876.0, 2967.0, 2769.0, 3275.0, 3721.0, 3671.0, 3798.0, 3494.0, 4081.0, 4223.0, 5402.0, 5631.0, 5575.0, 6128.0, 5980.0, 6919.0, 6968.0, 6459.0, 7627.0, 15326.0, 13820.0, 13382.0, 13750.0, 13436.0, 12488.0, 13744.0, 12697.0, 12601.0, 13277.0, 12959.0, 14091.0, 14732.0, 18390.0, 24582.0, 18763.0, 18752.0, 10248.0, 7691.0, 8848.0, 173.0]}, {"label": "no linked model", "data": [1751.0, 525.0, 740.0, 852.0, 1016.0, 1249.0, 1794.0, 2147.0, 2652.0, 3084.0, 4105.0, 4185.0, 7402.0, 7575.0, 6987.0, 8296.0, 13504.0, 19262.0, 7382.0, 7321.0, 8727.0, 9624.0, 9992.0, 9495.0, 14400.0, 10711.0, 10481.0, 10533.0, 11404.0, 13098.0, 12625.0, 14121.0, 15211.0, 19162.0, 20120.0, 22272.0, 26948.0, 75013.0, 81270.0, 62897.0, 78049.0, 58225.0, 90428.0, 118193.0, 81244.0, 67942.0, 57120.0, 54812.0, 56552.0, 73249.0, 66714.0, 65443.0, 39372.0, 23520.0, 23416.0, 527.0]}], "kind": "bar", "stacked": true, "note": "", "ylog": false}, {"title": "Public Spaces created per month by SDK", "labels": ["2022-03", "2022-04", "2022-05", "2022-06", "2022-07", "2022-08", "2022-09", "2022-10", "2022-11", "2022-12", "2023-01", "2023-02", "2023-03", "2023-04", "2023-05", "2023-06", "2023-07", "2023-08", "2023-09", "2023-10", "2023-11", "2023-12", "2024-01", "2024-02", "2024-03", "2024-04", "2024-05", "2024-06", "2024-07", "2024-08", "2024-09", "2024-10", "2024-11", "2024-12", "2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06", "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12", "2026-01", "2026-02", "2026-03", "2026-04", "2026-05", "2026-06", "2026-07", "2026-08", "2026-09", "2026-10"], "datasets": [{"label": "(none)", "data": [10.0, 4.0, 7.0, 7.0, 4.0, 11.0, 12.0, 18.0, 20.0, 19.0, 35.0, 15.0, 40.0, 58.0, 96.0, 80.0, 49.0, 119.0, 75.0, 63.0, 90.0, 75.0, 51.0, 46.0, 82.0, 63.0, 258.0, 88.0, 101.0, 92.0, 87.0, 91.0, 119.0, 138.0, 152.0, 200.0, 284.0, 551.0, 578.0, 601.0, 709.0, 672.0, 645.0, 922.0, 782.0, 641.0, 652.0, 652.0, 816.0, 1443.0, 901.0, 938.0, 554.0, 330.0, 530.0, 7.0]}, {"label": "docker", "data": [6.0, 0.0, 1.0, 1.0, 3.0, 1.0, 4.0, 4.0, 12.0, 128.0, 287.0, 494.0, 679.0, 876.0, 1026.0, 3039.0, 2314.0, 6300.0, 2162.0, 2267.0, 2450.0, 3714.0, 3732.0, 3560.0, 4643.0, 4095.0, 3771.0, 3870.0, 4426.0, 4129.0, 4372.0, 5200.0, 5672.0, 6968.0, 8477.0, 8139.0, 9102.0, 14069.0, 18111.0, 17150.0, 19565.0, 18712.0, 15843.0, 14591.0, 17941.0, 21124.0, 21096.0, 28370.0, 32244.0, 50035.0, 43601.0, 44462.0, 13164.0, 2857.0, 2354.0, 33.0]}, {"label": "gradio", "data": [1808.0, 545.0, 726.0, 684.0, 918.0, 1157.0, 1495.0, 1906.0, 2337.0, 3060.0, 4429.0, 4364.0, 7059.0, 6797.0, 6051.0, 5370.0, 11430.0, 13122.0, 5670.0, 5662.0, 6899.0, 6362.0, 6710.0, 6009.0, 9736.0, 6821.0, 7849.0, 8293.0, 8299.0, 10558.0, 9784.0, 11069.0, 11074.0, 10760.0, 12217.0, 21640.0, 21095.0, 21743.0, 26630.0, 27697.0, 25986.0, 25256.0, 26398.0, 26664.0, 28801.0, 26823.0, 25164.0, 26776.0, 30862.0, 34498.0, 30509.0, 29768.0, 15806.0, 11595.0, 12417.0, 253.0]}, {"label": "static", "data": [145.0, 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"labels": ["2023-10", "2023-11", "2023-12", "2024-01", "2024-02", "2024-03", "2024-04", "2024-05", "2024-06", "2024-07", "2024-08", "2024-09", "2024-10", "2024-11", "2024-12", "2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06", "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12", "2026-01", "2026-02", "2026-03", "2026-04", "2026-05", "2026-06", "2026-07", "2026-08", "2026-09"], "datasets": [{"label": "visits", "data": [882618.0, 17583558.0, 36778232.0, 21389153.0, 13325800.0, 24053093.0, 36347576.0, 39821551.0, 39597714.0, 53705915.0, 65603061.0, 57460291.0, 70876125.0, 63048142.0, 62203279.0, 32013908.0, 66146981.0, 81960845.0, 72036483.0, 84519690.0, 95327583.0, 74240031.0, 53116444.0, 74063528.0, 47649955.0, 46386541.0, 53792975.0, 65876476.0, 62932020.0, 84513370.0, 67589110.0, 93399169.0, 112820608.0, 88322609.0, 65071802.0, 56650342.0]}], "kind": "bar", "stacked": false, "note": "Source: by-24h-spaces-visits (same data as the visit counter on Space pages). Starts Oct 30 2023 \u2014 earliest available.", "ylog": false}, {"title": "Spaces receiving >=1 visit per month", "labels": ["2023-10", "2023-11", "2023-12", "2024-01", "2024-02", "2024-03", "2024-04", "2024-05", "2024-06", "2024-07", "2024-08", "2024-09", "2024-10", "2024-11", "2024-12", "2025-01", "2025-02", "2025-03", "2025-04", "2025-05", "2025-06", "2025-07", "2025-08", "2025-09", "2025-10", "2025-11", "2025-12", "2026-01", "2026-02", "2026-03", "2026-04", "2026-05", "2026-06", "2026-07", "2026-08", "2026-09"], "datasets": [{"label": "spaces", "data": [12935.0, 48020.0, 68956.0, 61981.0, 63665.0, 81791.0, 82862.0, 93349.0, 91710.0, 105188.0, 91684.0, 88459.0, 102874.0, 109205.0, 106825.0, 94079.0, 128908.0, 141848.0, 196167.0, 225645.0, 202020.0, 234381.0, 222867.0, 248803.0, 302123.0, 280345.0, 256713.0, 251437.0, 243838.0, 250099.0, 272606.0, 273351.0, 276914.0, 279596.0, 244776.0, 323163.0]}], "kind": "bar", "stacked": false, "note": "", "ylog": false}, {"title": "Public Spaces by AI category (top 25)", "labels": ["(uncategorized)", "Other", "Chatbots", "Text Generation", "Data Visualization", "Text Analysis", "Image Generation", "Image", "Question Answering", "Video Generation", "Document Analysis", "Financial Analysis", "Image Editing", "Speech Synthesis", "Code Generation", "Object Detection", "Game AI", "Medical Imaging", "Sentiment Analysis", "Image Classification", "Agent Environment", "Language Translation", "Music Generation", "3D Modeling", "Image Captioning"], "datasets": [{"label": "links open model", "data": [182721.0, 23608.0, 48866.0, 29041.0, 3253.0, 12893.0, 19008.0, 3846.0, 10924.0, 6669.0, 2432.0, 817.0, 3974.0, 6248.0, 4558.0, 4236.0, 162.0, 1410.0, 2829.0, 1640.0, 3343.0, 2414.0, 1001.0, 1008.0, 2892.0]}, {"label": "no linked model", "data": [814535.0, 311252.0, 45904.0, 51821.0, 42375.0, 23910.0, 12521.0, 26133.0, 14321.0, 14073.0, 15894.0, 15167.0, 11784.0, 6006.0, 5978.0, 6125.0, 8990.0, 7024.0, 4762.0, 5045.0, 2668.0, 2744.0, 3268.0, 3248.0, 1224.0]}], "kind": "bar", "stacked": true, "note": "ai_category is an AI-generated label; ~52% of public Spaces are uncategorized.", "ylog": false}, {"title": "Last-12-month visits to Spaces using open models, by category", "labels": ["Image Generation", "Video Generation", "Text Generation", "Image Editing", "Other", "Chatbots", "Speech Synthesis", "Text Analysis", "Music Generation", "3D Modeling", "Image Captioning", "OCR", "(uncategorized)", "Code Generation", "Image", "Image Upscaling", "Voice Cloning", "Data Visualization", "Question Answering", "Background Removal"], "datasets": [{"label": "visits 12m", "data": [41310533.0, 34228847.0, 33486848.0, 32219180.0, 30111236.0, 13108410.0, 12521953.0, 10377431.0, 9220267.0, 7728512.0, 6433717.0, 5372668.0, 4311695.0, 4230171.0, 4035887.0, 3347281.0, 3023365.0, 2771330.0, 1874649.0, 1810739.0]}], "kind": "bar", "stacked": false, "note": "", "ylog": false}, {"title": "Spaces by task of the linked open model", "labels": ["text-generation", "(none)", "text-to-image", "sentence-similarity", "image-text-to-text", "automatic-speech-recognition", "text-classification", "text-to-speech", "fill-mask", "image-to-image", "feature-extraction", "image-classification", "translation", "zero-shot-image-classification", "image-to-text", "summarization", "object-detection", "text-ranking", "image-to-video", "text-to-video"], "datasets": [{"label": "spaces", "data": [173186.0, 72172.0, 44271.0, 31543.0, 18771.0, 17004.0, 16417.0, 13804.0, 10974.0, 9402.0, 9300.0, 8710.0, 8267.0, 7873.0, 7772.0, 7056.0, 6720.0, 5979.0, 5364.0, 5028.0]}], "kind": "bar", "stacked": false, "note": "", "ylog": false}, {"title": "Most-linked open models in public Spaces (top 30)", "labels": ["Qwen/Qwen2.5-Coder-32B-Instruct", "HuggingFaceH4/zephyr-7b-beta", "sentence-transformers/all-MiniLM-L6-v2", "openai/gpt-oss-20b", "stabilityai/sdxl-turbo", "openai-community/gpt2", "NousResearch/DeepHermes-3-Mistral-24B-Preview", "openai/gpt-oss-120b", "Qwen/Qwen2.5-72B-Instruct", "cross-encoder/ms-marco-MiniLM-L6-v2", "stabilityai/stable-diffusion-xl-base-1.0", "facebook/wav2vec2-base-960h", "Qwen/Qwen2.5-7B-Instruct", "google/flan-t5-base", "Salesforce/blip-image-captioning-base", "meta-llama/Llama-3.1-8B-Instruct", "openai/clip-vit-base-patch32", "black-forest-labs/FLUX.1-dev", "facebook/bart-large-cnn", "google/flan-t5-small", "mistralai/Mistral-7B-Instruct-v0.2", "facebook/fastspeech2-en-ljspeech", "thenlper/gte-small", "google-bert/bert-base-uncased", "mistralai/Mistral-7B-Instruct-v0.3", "distilbert/distilbert-base-uncased-finetuned-sst-2-english", "black-forest-labs/FLUX.1-schnell", "Xenova/detr-resnet-50", "TinyLlama/TinyLlama-1.1B-Chat-v1.0", "sentence-transformers/all-mpnet-base-v2"], "datasets": [{"label": "spaces linking", "data": [35068.0, 21007.0, 20234.0, 15936.0, 11732.0, 8189.0, 6929.0, 6254.0, 5572.0, 5409.0, 4628.0, 4124.0, 4087.0, 4084.0, 4049.0, 3998.0, 3958.0, 3840.0, 3791.0, 3734.0, 3566.0, 3446.0, 3352.0, 3336.0, 3153.0, 2865.0, 2833.0, 2775.0, 2759.0, 2714.0]}], "kind": "bar", "stacked": false, "note": "", "ylog": false}, {"title": "Model authors most linked by Spaces (top 25)", "labels": ["Qwen", "facebook", "openai", "Helsinki-NLP", "google", "sentence-transformers", "meta-llama", "stabilityai", "HuggingFaceH4", "microsoft", "Yntec", "mistralai", "digiplay", "csukuangfj", "openai-community", "deepseek-ai", "NousResearch", "black-forest-labs", "distilbert", "Salesforce", "prithivMLmods", "BAAI", "cross-encoder", "lllyasviel", "TheBloke"], "datasets": [{"label": "space links", "data": [79351.0, 48992.0, 38100.0, 30949.0, 28731.0, 27357.0, 25506.0, 24500.0, 21794.0, 17952.0, 17935.0, 14092.0, 13791.0, 12456.0, 10637.0, 10038.0, 8849.0, 8725.0, 8190.0, 8051.0, 7325.0, 7127.0, 6167.0, 5532.0, 5475.0]}], "kind": "bar", "stacked": false, "note": "", "ylog": false}, {"title": "New public Spaces per quarter linking each model author (top 12)", "labels": ["2022-Q1", "2022-Q2", "2022-Q3", "2022-Q4", "2023-Q1", "2023-Q2", "2023-Q3", "2023-Q4", "2024-Q1", "2024-Q2", "2024-Q3", "2024-Q4", "2025-Q1", "2025-Q2", "2025-Q3", "2025-Q4", "2026-Q1", "2026-Q2", "2026-Q3", "2026-Q4"], "datasets": [{"label": "Qwen", "data": [1.0, 0.0, 2.0, 0.0, 4.0, 4.0, 54.0, 94.0, 87.0, 174.0, 355.0, 867.0, 12212.0, 8305.0, 5677.0, 5022.0, 9123.0, 15919.0, 6325.0, 60.0]}, {"label": "openai", "data": [24.0, 15.0, 22.0, 102.0, 203.0, 350.0, 468.0, 575.0, 576.0, 624.0, 780.0, 983.0, 967.0, 1333.0, 4431.0, 6172.0, 7128.0, 7780.0, 2922.0, 16.0]}, {"label": "sentence-transformers", "data": [29.0, 27.0, 29.0, 46.0, 67.0, 327.0, 375.0, 403.0, 322.0, 413.0, 424.0, 773.0, 1208.0, 2142.0, 4078.0, 4431.0, 4610.0, 5258.0, 1154.0, 5.0]}, {"label": "facebook", "data": [120.0, 80.0, 175.0, 276.0, 287.0, 515.0, 624.0, 846.0, 1143.0, 1049.0, 1137.0, 1357.0, 1779.0, 2060.0, 1903.0, 2248.0, 2301.0, 2850.0, 1273.0, 5.0]}, {"label": "stabilityai", "data": [0.0, 1.0, 1.0, 14.0, 20.0, 159.0, 681.0, 688.0, 747.0, 1285.0, 1522.0, 1772.0, 1911.0, 2630.0, 2729.0, 2170.0, 2178.0, 2432.0, 1021.0, 4.0]}, {"label": "HuggingFaceH4", "data": [1.0, 0.0, 0.0, 0.0, 0.0, 21.0, 30.0, 259.0, 187.0, 1512.0, 2520.0, 2949.0, 3799.0, 5781.0, 3037.0, 318.0, 485.0, 424.0, 115.0, 1.0]}, {"label": "google", "data": [46.0, 24.0, 83.0, 76.0, 204.0, 420.0, 448.0, 401.0, 790.0, 928.0, 868.0, 852.0, 1037.0, 2147.0, 2431.0, 2558.0, 3117.0, 3564.0, 1336.0, 3.0]}, {"label": "meta-llama", "data": [1.0, 0.0, 2.0, 0.0, 5.0, 14.0, 860.0, 396.0, 423.0, 1398.0, 1496.0, 1537.0, 1271.0, 1584.0, 1313.0, 1519.0, 2432.0, 5345.0, 1083.0, 4.0]}, {"label": "microsoft", "data": [41.0, 26.0, 38.0, 66.0, 199.0, 327.0, 458.0, 431.0, 489.0, 663.0, 841.0, 791.0, 1139.0, 1400.0, 1660.0, 1859.0, 1420.0, 1780.0, 659.0, 2.0]}, {"label": "mistralai", "data": [0.0, 1.0, 1.0, 0.0, 0.0, 7.0, 53.0, 577.0, 850.0, 950.0, 745.0, 869.0, 1049.0, 1284.0, 1017.0, 1001.0, 1302.0, 1466.0, 364.0, 0.0]}, {"label": "openai-community", "data": [42.0, 44.0, 37.0, 59.0, 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