GITFIND

open-weight adoption — tracked daily

THE OPEN MODEL INDEX

Which open-weight models are developers actually building with? Hugging Face weight downloads and GitHub velocity for DeepSeek, Llama, Qwen, Kimi, Mistral, and GLM — plus the runtime layer that runs them. Every number says what it is; nothing here is made up.

30-day pulls: 12.1Mmodels tracked: 8 + 3 runtimesleader: deepseek r1 8.5M/30dfastest riser: kimi k3 · 2d

§ 1 — weight downloads, daily

fig. 01

5.0M10.0Mapr 30 — 4.7M commitsmay 2 — 154k commitsmay 7 — 795k commitsmay 8 — 4.7M commitsmay 9 — 463k commitsmay 10 — 580k commitsmay 11 — 795k commitsmay 13 — 693k commitsmay 15 — 396k commitsmay 16 — 947k commitsmay 19 — 4.9M commitsmay 21 — 5.3M commitsmay 23 — 592k commitsmay 25 — 4.4M commitsmay 27 — 4.7M commitsmay 29 — 138k commitsmay 30 — 5.0M commitsmay 31 — 1.0M commitsjun 1 — 682k commitsjun 2 — 328k commitsjun 4 — 7.4M commitsjun 6 — 5.7M commitsjun 7 — 5.7M commitsjun 8 — 5.7M commitsjun 11 — 6.0M commitsjun 15 — 218k commitsjun 16 — 100.0k commitsjun 17 — 558k commitsjun 18 — 204k commitsjun 19 — 996k commitsjun 21 — 6.8M commitsjun 24 — 7.0M commitsjun 29 — 696k commitsjun 30 — 171k commitsjul 1 — 8.8M commitsjul 3 — 8.0M commitsjul 4 — 151k commitsjul 5 — 745k commitsjul 7 — 1.1M commitsjul 9 — 8.6M commitsjul 10 — 11.1M commitsjul 11 — 175k commitsjul 12 — 8.7M commitsjul 14 — 142k commitsjul 15 — 10.8M commitsjul 17 — 229k commitsjul 18 — 917k commitsjul 19 — 69.4k commitsjul 20 — 8.6M commitsjul 22 — 10.1M commitsjul 24 — 208k commitsjul 26 — 130k commitsjul 28 — 195k commitsjul 29 — 11.4M commitsjul 30 — 11.4M commitsjul 31 — 11.9M commitsaug 1 — 12.2M commitsaug 2 — 12.0M commitsaug 3 — 12.5M commitsaug 4 — 12.6M commitsaug 5 — 13.1M commitsaug 6 — 13.1M commitsaug 7 — 13.0M commitsaug 8 — 12.8M commitsaug 9 — 12.6M commitsaug 10 — 12.3M commitsaug 11 — 12.2M commitsaug 12 — 12.1M commits12.1Mapr 30may 15may 30jun 11jun 29jul 10jul 20aug 1aug 12

hugging face trailing 30-day weight downloads per day, across all tracked models.

§ 2 — share of new downloads

fig. 02

70%13%9%
deepseek r1 70.3%kimi k3 12.9%deepseek v3 9.2%llama 3.3 70b 2.8%mistral small 3.1 2.3%kimi k2 1.2%glm 4.5 1.0%qwen3 235b 0.3%

share of the trailing 30-day window.

§ 3 — each model, same scale

fig. 03

deepseek r1

8.5M/30d · ▲ 71%

kimi k3

1.6M/30d · ▲ 54829%

deepseek v3

1.1M/30d · ▲ 92%

llama 3.3 70b

335k/30d · ▼ 46%

mistral small 3.1

275k/30d · ▼ 40%

kimi k2

150k/30d · ▼ 81%

glm 4.5

118k/30d · ▲ 1%

qwen3 235b

42.1k/30d · ▼ 73%

trailing 30-day weight downloads per day, averaged over 3-day buckets · all charts share one scale.

§ 4 — momentum, ranked

fig. 04

  • 01kimi k3fastest

    1.6M/30d · ▲ 54829% · 2d to double · 12.9%

  • 02deepseek v3

    1.1M/30d · ▲ 92% · 22d to double · 9.2%

  • 03deepseek r1

    8.5M/30d · ▲ 71% · 46d to double · 70.3%

  • 04glm 4.5

    118k/30d · ▲ 1% · 2687d to double · 1.0%

  • 05mistral small 3.1

    275k/30d · ▼ 40% · — · 2.3%

  • 06llama 3.3 70b

    335k/30d · ▼ 46% · — · 2.8%

  • 07qwen3 235b

    42.1k/30d · ▼ 73% · — · 0.3%

  • 08kimi k2

    150k/30d · ▼ 81% · — · 1.2%

§ 5 — the runtime layer

fig. 05

fig. 05 — the runtime layer (what people run open models with)

toolstarsforkscontributors
ollama178k17.4k596
llama.cpp124k21.6k1.9k
vllm88.8k20.6k2.4k

runtimes don’t publish weight downloads — repo traction is the honest proxy here.

§ 6 — intelligence brief

fig. 06

$ tail -f /var/log/gitfind/models.log

09:41:02 8 open models + 3 runtimes tracked — hf trailing-30d downloads + github velocity, nothing fabricated

09:41:03 deepseek r1 leads the 30-day window at 8.5M

09:41:04 deepseek r1 gained the most rate this month — +3.5M on the 30d window

09:41:05 kimi k3 is the fastest riser — doubling every 2 days at current pace

09:41:06 ollama leads the runtime layer at 178k★

09:41:07 router share coming when there’s a stable public source — we don’t scrape

09:41:08 █

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