Hugging Face 发布 2026 夏季开源模型观察：Qwen 衍生模型达 15 万个

2. Attention ≠ Adoption

We took the top 25 model repositories by downloads accumulated this year and the top 25 by likes. Exactly one repository appears in both lists.

We counted downloads inside the window rather than lifetime, so nothing is credited for merely having existed longer, and controlling for age makes the split sharper. Not one model published in 2026 reaches the download top 25, while thirteen of the twenty-five date from 2022. all-MiniLM-L6-v2 was pulled 1.55 billion times in seven months against 5,156 likes; Kimi-K3 was pulled about 60 times per like it received.

The two numbers record different acts. A like says a release matters, and goes to frontier models in the weeks after they ship. A download says something is wired into a pipeline that runs on a schedule, and accrues to small, stable models over years. Likes are the right instrument for reading what the field is excited about, downloads for reading what it currently depends on. Treating either as a proxy for the other is the most common mistake we see in coverage of the Hub, including our own earlier work. The same split appears at the level of the publisher.

China's frontier labs are the only accounts on the Hub where the heavy band carries the volume. Effectively all of MiniMax's 2026 downloads are of models above 70B, along with 88% of Moonshot's, 55% of DeepSeek's and 39% of Z.ai's. No large American account looks like this: Google, Microsoft and IBM Granite record essentially none of their 2026 downloads above 70B, and NVIDIA and Meta only 14% and 9%.

The difference becomes clearer in total downloads. Moonshot’s frontier-only portfolio recorded 37M downloads over the year, while Qwen’s broader release strategy across model sizes reached 2,045M (across repositories with declared parameter counts, 2,061M including all repositories) , about 55 times more. The continued expansion of the family, from the 2.4T-parameter Qwen 3.8 Max to smaller variants such as 27B, shows the same focus on coverage across different use cases.

Time also plays an important role. Most models experience a sharp decline in usage after release, followed by a long tail of steady activity. A model’s adoption is largely determined within its first few months.

This helps explain why today’s download volume is often driven not by the newest releases, but by a smaller group of models that have become established infrastructure over time.

3. Open weights shift where value accumulates

If frontier models were a licensing business, you would expect the biggest releases to carry the tightest terms. However, the data below shows a different story.

Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% carry MIT, and almost none carry non-commercial restrictions . However, in the last few weeks, we started to see a change on this trend for the really large models, with Kimi K3 and Qwen 3.8 2.4T starting to include some non-commercial restrictions and revenue share requirements to their licenses

DeepSeek and Z.ai ship models between 700 billion and 1.65 trillion parameters under plain MIT. Chinese labs license their largest models about as permissively as their smallest, and more permissively than American labs license theirs: on the American side of the same size band, 29% is Apache or MIT, 41% sits under custom terms and 30% declares nothing at all.

Whatever these releases are for, it is not licence revenue. The weights are given away on the most permissive terms available. The return has to come from somewhere else: API and cloud business, hardware and platform positioning, or the ecosystem position itself. For instance, the valuations of Z.ai and Kimi point to an effective open source strategy, getting traction and growth opportunities in the community. Going forward, however, the industry is likely to shift toward clearer monetization paths from open-source adoption.

4. Qwen has become the community's base model

A model’s ecosystem position is not defined only by its own releases, but by how much the community builds on top of it. As mentioned above, Qwen is one exception which is getting attention and adoption.

Data from Hugging Face

By this measure, Qwen has become one of the largest foundations in the open model ecosystem. Qwen-based models now account for 151,448 derivatives on the Hub, 2.6× Meta’s total footprint and 4.7× the Llama repositories specifically. Google follows with 82,506 derivatives. The third-largest source is Unsloth, a community account publishing quantized and fine-tuning-ready builds, many of which further extend the Qwen ecosystem.

Qwen derivatives have increased at roughly 180–210 new repositories per day throughout the first seven months of 2026, showing that adoption is not driven only by individual launches. Qwen has become part of the default workflow for developers deciding what models to fine-tune and deploy.

Several factors contributed to this position. First, consistency. Qwen has maintained a regular release cadence, continuously updating its model family rather than relying on occasional flagship releases. Second, coverage. It publishes models across a wide range of sizes and use cases, allowing developers to stay within the same ecosystem whether they need a small local model or a larger deployment model. Third, openness. Apache 2.0 licensing reduces friction for modification, redistribution, and commercial use.

These factors reinforce each other. A broad model family attracts more developers; more developers create more derivatives; and those derivatives make the ecosystem more attractive to future users.

This position was built largely by the community. The 151,448 derivatives represent downstream work created by other developers, not releases produced by Qwen itself. Even among the 28,531 GGUF conversions of Qwen models on the Hub, Qwen published only 54.

5. Small models remain the practical layer

Among models that declare a parameter count, those under 1B take 83% of all-time downloads and everything above 100B takes 1%. Restricting to downloads accumulated in 2026 changes nothing: 3% of the volume goes to models above 70B. This is the March finding that has held up most cleanly, for the same reason as before, small models are the only ones that run on the hardware most developers actually have.

So how does a trillion-parameter model reach anyone at all? Through llama.cpp.

In February the ggml team joined Hugging Face , with the project remaining fully open-source, community-governed and in the same technical direction. What changed is that the most important project in local inference now has durable resources behind it.

The ceiling moved with llama.cpp. The July snapshot carries GGUF builds of DeepSeek-V4-Flash at roughly 284B parameters and Kimi-K3 at roughly 2.8 trillion. Local inference used to mean an 8B model on a laptop. It now means a trillion-parameter mixture-of-experts spread across a few consumer machines, which is the alternative route the frontier did not have a year ago, and the reason a frontier-first release strategy is viable at all.

And that route runs on Qwen: 39.6 million GGUF downloads a month, nearly twice Gemma's 20.8 million and more than five times Llama's 7.5 million. The Llama gap is not a supply problem, Llama-derived GGUF repositories slightly outnumber Qwen's. Same shelf space, a fifth of the traffic.

Model repositories grew 21.5% over these seven months. Several things around them grew several times faster.

Repositories declaring the gguf library rose 464%, lerobot 194% and Apple's mlx148%, against 16% for transformers and peft and 21% for diffusers. The modelling core is growing at roughly the platform average. The layer that decides where a model can physically run local inference formats, Apple silicon, robot control stacks, is growing three to seven times faster than that.

Across the ten largest model families, the labs behind these models publish very few official GGUF conversions. Yet GGUF versions are often the ones used by developers running models locally. Providing an official conversion at release, documenting quantization choices, and signing the artifacts would require limited additional effort. Rather than maintaining this workflow internally, labs could collaborate with existing ecosystem contributors such as Unsloth. Doing so would narrow the gap between the weights tested by model creators and the versions adopted by the broader community.

6. Agents are the new user

We could not have written this section in March, because the instrument did not exist. The agent-usage dataset, published in July, records the agent/ token that coding agents send when they call the Hub through huggingface_hub or the hf CLI — searching for models, pushing datasets, running Jobs, creating Spaces. For the first time we can see how much agent traffic the Hub receives and which harnesses it comes from.

Claude Code led July with 44.4%, but a single month conceals the real finding: it held 67.8% in April and 64% in May, while Codex climbed steadily from 10.4% to 20.8%. This is a market with no incumbent, where one release or one changed default can move half the traffic in a month.

The second finding is the unregistered row. Nearly a quarter of agent-tagged traffic in July came from harnesses not yet named in the dataset, and in May that figure was 59.8%. Between April and July more than a dozen new client identifiers appeared. New entrants are arriving faster than any registry can name them — which is itself the finding.

We spent much of the year building for this reader rather than only for human browsers. Papers began serving machine-readable Markdown in March. April brought agent traces as a first-class dataset type and an agents.md endpoint on every Gradio Space, so an agent can read a Space's API and call it directly. July brought the hf_fs tool on our MCP server, exposing repositories, storage, docs and papers through a single interface in just over a thousand tokens, alongside attachable sandboxes for secure execution. The same consolidation happened at the protocol layer, with MCP moving into the Linux Foundation's Agentic AI Foundation.

Then, in July, an agent stopped being a reader and became an intruder. What appears to be the first documented case of an autonomous agent running a sustained intrusion on its own initiative happened to us. While our team tried to use frontier closed models to analyze the captured attack code, their safety guardrails declined the work. The analysis was completed in the end on a quantized open model GLM-5.2 running on our own infrastructure. We published a disclosure and a full technical timeline .

Looking forward

Compared to the spring report, the geographical rebalancing of power continues to accelerate. While U.S. open source models continue to be competitive, the race between several Chinese frontier model labs draws strong attention. Many likes on these frontier models point to what excites the community the most, and growth opportunity for companies leveraging the attention for valuations.

However, the AI race is not only sprints, but also a marathon; tools like llama.cpp helps deploying the big models locally, but a broad model family and its adoption is still the key, to build a positive feedback loop between developers, publisher and future users. Models to be embedded in the infrastructure and being part of the ecosystem, may lead to a commercially sound exit at the end of the tunnel.

In the end, with agents being the number 1 user on HF Hub for the first time, the next report may look very different.

In AI, a few months can reshape the ecosystem.

Notes on method

This analysis is based on activity observed on the Hugging Face Hub during the first seven months of 2026.

The metrics used in this report, including downloads, likes, derivatives, and model releases, represent different aspects of ecosystem activity. They should not be interpreted as direct measures of model quality, commercial adoption, or overall market share.

Downloads indicate usage within the Hub ecosystem, but they do not capture API usage, private deployments, or models distributed through other channels.

Likes reflect community attention and interest, while derivative models provide a signal of how much developers build on top of an existing model.

Because open-source AI adoption happens across many channels, Hub activity should be viewed as one perspective on ecosystem development rather than a complete measurement of the AI market.

Edited

This article was edited to include latest releases in early August.

Models mentioned in this article 4

Qwen/Qwen3.8-2.4T-A95B

Text Generation • 2.4T • Updated Aug 12 • 40.2k • 1.28k meta-models/Muse-Glimmer-30B Image-Text-to-Text • 30B • Updated Aug 11 • 282k • 1.96k moonshotai/Kimi-K3 Image-Text-to-Text • 2.8T • Updated 30 days ago • 1.27M • 11.6k sentence-transformers/all-MiniLM-L6-v2 Sentence Similarity • 22.7M • Updated Jun 1 • 242M • 6.16k Datasets mentioned in this article 1 huggingface/agent-usage Viewer • Updated 24 days ago • 2.96k • 872 • 10 Collections mentioned in this article 3 Trinity-Large-Thinking Collection 5 items • Updated Apr 10 • 33 NVIDIA Nemotron v3 Collection Open, Production-ready Enterprise Models • 33 items • Updated Aug 14 • 378 Inkling Collection Inkling is a versatile, customizable model that reasons over text, images, audio, with variable and efficient thinking effort. • 4 items • Updated Jul 27 • 68 More Articles from our Blog

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25 September 22, 2026 research community agents What We Learned by Reproducing 2,200 papers from ICML 118 August 13, 2026 Community \"Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% carry MIT, and exactly none carry a non-commercial restriction. Not true. Kimi models cannot be served by companies that make more than $20M/year unless explicit authorisation by Moonshot. \n","updatedAt":"2026-08-14T17:24:01.393Z","author":{"_id":"67631bea36819aebd4bf7a25","avatarUrl":"https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Pi7YYrvpc91yaZgavf38S.png","fullname":"hurl","name":"hurler98","type":"user","isPro":false,"isHf":false,"isHfAdmin":false,"isMod":false,"isUserFollowing":false}},"numEdits":0,"identifiedLanguage":{"language":"en","probability":0.8910983204841614},"editors":["hurler98"],"editorAvatarUrls":["https://cdn-avatars.huggingface.co/v1/production/uploads/no-auth/Pi7YYrvpc91yaZgavf38S.png"],"reactions":[{"reaction":"🔥","users":["Danyaus"],"count":1},{"reaction":"➕","users":["JagoWang"],"count":1}],"isReport":false}},{"id":"6a859b88c79fe15d0af448f1","createdAt":"2026-08-19T12:03:20.000Z","type":"comment","data":{"edited":true,"hidden":true,"hiddenBy":"","latest":{"raw":"This comment has been hidden","html":"This comment has been hidden","updatedAt":"2026-08-19T14:05:19.080Z"},"numEdits":0,"editors":[],"editorAvatarUrls":[],"reactions":[]}}],"status":"open","isReport":false,"pinned":false,"locked":false,"collection":"community_blogs"},"contextAuthors":["AdinaY","multimodalart","irenesolaiman"],"primaryEmailConfirmed":false,"discussionRole":0,"acceptLanguages":["en","zh"],"withThread":true,"cardDisplay":false,"repoDiscussionsLocked":false,"hideComments":true}"> hurler98 Aug 14 "Of 178 Chinese releases above 20B parameters this year, 59% carry Apache 2.0 and 22% carry MIT, and exactly none carry a non-commercial restriction. Not true. Kimi models cannot be served by companies that make more than $20M/year unless explicit authorisation by Moonshot.

Reply deleted Aug 19 • This comment has been hidden Edit Preview Upload images, audio, and videos by dragging in the text input, pasting, or clicking here . Tap or paste here to upload images Comment · Sign up or log in to comment

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Models mentioned in this article 4

Qwen/Qwen3.8-2.4T-A95B

meta-models/Muse-Glimmer-30B

moonshotai/Kimi-K3

sentence-transformers/all-MiniLM-L6-v2

Datasets mentioned in this article 1

huggingface/agent-usage

Collections mentioned in this article 3