On August 14, Hugging Face published State of Open Models: Summer 2026 Observations, drawing on Hub activity from January through August to make a point most open-source press releases tend to skip. The model community's attention economy and its usage economy are not the same market. Of the top 25 repositories by likes and the top 25 by downloads, only one repository appears on both lists. The conclusion is not abstract: it determines whether a company shipping open weights should pour its R&D into being exciting or into being deployable.
The baseline numbers set the stage. Public model repositories on the Hub grew from 2.43 million to 2.96 million during the period, datasets from 711,000 to 1 million, and Spaces from 1.00 million to 1.44 million. As the surface area expands, concentration deepens. 85.6% of model repositories have fewer than 200 lifetime downloads; 1.5% of repositories account for 99.2% of all downloads. That long-tail distribution means "open-source AI is thriving" and "any specific model is widely used" are unrelated stories.
Attention is not adoption
Cross-referencing the window's top-25 most-liked and top-25 most-downloaded repositories produces a single overlap. Normalising for model age sharpens the picture. None of the download top-25 was published in 2026; thirteen of the twenty-five date from 2022. The extremes are illustrative. all-MiniLM-L6-v2 was downloaded 1.55 billion times in seven months and earned 5,156 likes. Kimi-K3 went the other way, accruing roughly one like for every sixty downloads.
A like says this release matters; downloads accumulate in the weeks after launch, then a long tail of steady pipeline usage carries the number forward. Likes read what excites the field. Downloads read what it currently depends on. Treating either as a proxy for the other is, the report argues, the most common mistake made by people writing about the Hub.
Qwen has become the community's base model
Attention and usage do not align, but a third axis — the number of derivative repositories built on top of a base model — captures something else. By that measure, Qwen has effectively become the community default. The report counts 151,448 Qwen derivatives, 2.6x Meta's footprint and 4.7x the Llama repositories specifically. Google comes second with 82,506 derivatives. Qwen's derivative count grew at roughly 180 to 210 new repositories per day through the first seven months of 2026.
Of those 151,448 derivatives, Qwen itself published only 54 official GGUF builds. The rest are community work, much of it from accounts such as Unsloth that focus on quantization and fine-tuning-ready builds. The implication is that "open weights" is not just "drop the weights somewhere." What actually decides whether a model runs on consumer hardware is the invisible layer of quantization and conversion on the Hub.
GGUF growth at 464% brings trillion-parameter MoE to consumer machines
Model repositories as a whole grew 21.5% over the seven months. The runtime layer around them grew several times faster. Repositories declaring the gguf library rose 464%, lerobot 194%, and Apple's mlx 148%; in the same window, transformers and peft repositories grew 16% and diffusers 21%. The report names the gap: the runtime layer is growing faster than the model layer.
GGUF is llama.cpp's quantization format. The practical consequence is that "running a model locally" used to mean an 8B model on a laptop; the ceiling is now around 284B parameters for DeepSeek-V4-Flash and roughly 2.8 trillion for Kimi-K3 (the report writes "Kimi-K3 at roughly 2.8 trillion"). On monthly GGUF downloads, Qwen hits 39.6 million, nearly twice Gemma's 20.8 million and more than five times Llama's 7.5 million — even though Llama-derived GGUF repositories slightly outnumber Qwen's. Same shelf space, a fifth of the traffic.
The US-China parameter ceiling gap is widening, almost every month
The report's chart of the largest open-model release by month shows a pattern: in almost every month of 2026, the strongest open model from a Chinese lab was larger than anything an American lab released that month. China's monthly ceiling ran between 754B and 2.78 trillion parameters. US models stayed below 130B in five of seven months. The exceptions are NVIDIA's Nemotron 3 Ultra at 561B in May and June, and Thinking Machines' Inkling at 952B, which the report notes is built on top of Chinese models.
Kimi K3 and Qwen 3.8 2.4T add non-commercial and revenue-share terms
The licensing story is also shifting. Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% MIT, and the report says almost none have non-commercial restrictions. In recent weeks, however, the trend has reversed at the top: Kimi K3 and Qwen 3.8 2.4T have begun including non-commercial restrictions and revenue-share requirements. This is a watershed for open-weight commercialisation. The most permissive licences are drifting toward the most restrictive, and the change concentrates in the largest models.
Agents become the Hub's largest user group in July
The report also discloses, for the first time, an agent-usage dataset released in July that records the agent/
What this adds up to
Put the threads together and the picture is more complicated than the headline "Qwen crosses 3 billion downloads." Open-source LLMs are no longer one market but three parallel ones: the likes market (frontier, new releases, attention), the downloads market (stable, small, pipeline-bound), and the derivatives market (licence-friendly weights, full family coverage, community quantization). The three overlap less than the discourse suggests. Qwen's "base model" status is built on the second and third markets — Apache 2.0 licensing, a 1B-to-2.4T family, and a community willing to convert every variant into GGUF.
The other hidden signal in the report is that "open" is no longer "free." Kimi K3 and Qwen 3.8 2.4T have written non-commercial and revenue-share terms into their licences, which means the main monetization route for frontier open weights is moving away from licensing and toward API and cloud revenue, hardware and platform positioning, and the ecosystem position itself. This chapter of the open-source narrative may be shorter than the last, because the terms are getting tighter.
Sources: Hugging Face blog original; Solidot Chinese recap (corroborates Qwen 3 billion downloads and 151,448 derivatives); Bloomberg, 15 August 2026 (corroborates Qwen 3 billion downloads). (Compiled by newsforai.)