Hugging Face published "State of Open Models: Summer 2026 Observations" on August 14, 2026. While frontier labs race to ship the largest, newest weights, the report quietly reframes a question the field has been measuring the wrong way: which models people are excited about, and which models are actually running in production, are not the same list.

The data itself is counter-intuitive

The authors took the top 25 model repositories by downloads accumulated over 2026 and the top 25 by likes. Exactly one repository appears in both lists. No model published in 2026 reaches the download top 25, while thirteen of the twenty-five date from 2022.

The most striking number is for all-MiniLM-L6-v2, a sentence embedding model released in 2022. It was pulled roughly 1.55 billion times in seven months, against only 5,156 likes. In the same window, Kimi-K3 was pulled about 60 times per like. One is invisible to the hype cycle; the other dominates it.

Likes and downloads record different acts

The report draws a clean line between the two metrics: likes is attention, downloads is dependency. A like says "this release matters" and accrues to flagship releases within weeks of launch; a download says "this model is wired into a pipeline that runs on a schedule" and accrues to small, stable models over years. The authors note that using either as a proxy for the other is the most common mistake in coverage of the Hub.

The asymmetry shows up at the publisher level too:

  • Chinese frontier labs get essentially all their 2026 downloads from models above 70B parameters. 100% of MiniMax's 2026 downloads are above 70B, alongside 88% of Moonshot's, 55% of DeepSeek's, and 39% of Z.ai's. Moonshot's frontier-only portfolio recorded 37M downloads over the year.
  • American labs look different. Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B; NVIDIA only 14%, Meta only 9%.
  • By total downloads, Qwen's full-spectrum strategy reached 2,045M downloads in 2026 — roughly 55 times Moonshot's frontier-only total.

The report highlights Qwen's 151,448 derivative models on the Hub, 2.6× Meta's total footprint and 4.7× Llama derivatives specifically, growing at 180-210 new repositories per day.

The other headline: things outside the model are growing faster

The report devotes a section to the runtime layer, because what actually decides whether a model can be used is usually not the model itself, but where it runs.

  • Repositories declaring the gguf library (used by llama.cpp) rose 464% year over year
  • lerobot rose 194%
  • Apple MLX 148%
  • Meanwhile core libraries like transformers and peft grew only 16%
  • diffusers 21%

The other piece is that the ggml team joined Hugging Face in February 2026. The project remains fully open-source and community-governed, but now has durable resources behind it. The local inference ceiling moved with it: GGUF builds now carry DeepSeek-V4-Flash at roughly 284B parameters and Kimi-K3 at roughly 2.8 trillion.

This explains a contradiction that looks bizarre on the surface: the labs behind the ten largest model families publish very few official GGUF conversions, yet developers running models locally overwhelmingly use GGUF. Qwen gets 39.6 million GGUF downloads per month, nearly twice Gemma's 20.8 million and more than five times Llama's 7.5 million.

The takeaway the field hasn't fully internalized

The most useful sentence in the report is also its most understated: "Likes are the right instrument for reading what the field is excited about, downloads for reading what it currently depends on."

For a year, the open-source model conversation has been measured almost entirely on which release was the most "fancy." When 60% of new derivative repositories point at the Qwen family, when a 2022 sentence embedding model gets pulled 1.55 billion times, when GGUF repositories grow 464%, "being used" and "being noticed" stop being abstract observations and become something you can use to recalibrate how you report on the field.

For teams building downstream products, treating a model's like count as a proxy for production readiness is a habit that probably needs another calibration cycle.