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What's happening in robotics and AI — curated by the wujec.ai editors.
Two thirds of Z.ai's model repositories are older than 2025. One of them carries most of what is left of their traffic.
Z.ai keeps 154 model repositories on Hugging Face. Ninety-eight of them - just under two thirds - were published before 2025, and together they were downloaded 810,718 times in the last thirty days. That is eight per cent of the account's 10.08 million downloads. The fourteen repositories created in 2026 took 8.38 million, or eighty-three per cent. The surprise is how concentrated the remainder is. ChatGLM2-6B, published in June 2023, accounts for 435,810 of those downloads on its own - more than half of everything the pre-2025 archive collects, and more than the vendor's current GLM-5, GLM-5.1, GLM-5.3 or GLM-4.7 individually pull in the same period. A three-year-old six-billion-parameter chat model outranks four of the company's own flagships on the download counter. Size is the readable explanation. ChatGLM2-6B runs on a 6 GB graphics card at INT4 quantisation and holds about 8,000 tokens of conversation there; the 2026 flagships do not fit consumer hardware at all. The rest is inertia: tutorials, university course material and fine-tuning recipes written in 2023 still point at that repository by name. One caveat belongs with the numbers. Hugging Face reports downloads over a rolling thirty-day window only, so these figures describe present-day pulls, not lifetime totals - a model released last week and a model released three years ago are measured on the same month. wujec.ai has added profiles for ChatGLM-6B, ChatGLM2-6B and GLM-4-9B-Chat, the three models this history runs through.
ChatGLM2-6B →Qwen's three billion downloads led the headlines — the report behind them says the giants are 1% of the traffic
Hugging Face published its half-yearly stocktake of open-weight models on 14 August 2026, and by the next morning one number from it was everywhere: Alibaba's Qwen family has passed three billion downloads, ahead of Google and Meta combined. The figure is Alibaba's own cumulative count across all platforms. The report's own measurement is narrower and more useful: on Hugging Face alone, Qwen repositories were downloaded 2,045 million times during 2026, against 418 million for Google and 227 million for Meta. Qwen now carries more than half of all open-model downloads on the hub, and its models have been forked into 151,448 derivatives — roughly 180 to 210 new ones every day, against 82,506 for Google and 32,155 for Llama. The finding that went unreported sits a few charts further down, and it undercuts the way this race is usually described. Models below one billion parameters account for 83% of all downloads ever recorded on the hub. Models above 100 billion parameters — the frontier, the ones that get the launch events and the benchmark tables — account for 1%. Restrict the count to 2026 alone and everything above 70 billion parameters is still only 3% of the volume. Distribution is brutally concentrated in another direction too: 85.6% of all models on the hub have fewer than 200 lifetime downloads, while 1.5% of them take 99.2% of the traffic. The same asymmetry shows up in what people actually run at home. In GGUF, the quantised format used for local inference on ordinary hardware, Qwen is pulled 39.6 million times a month, Google's Gemma 20.8 million and Llama 7.5 million — the last despite Llama having more GGUF repositories published than Qwen. The report reads this as a strategy difference rather than a quality verdict: Qwen ships across every size class, from sub-billion models that run on a laptop up to the frontier, while a lab that publishes only large models collects a fraction of the traffic. Moonshot, whose open portfolio is frontier-only, recorded 37 million downloads — 55 times less than Qwen. Two further findings are worth recording for anyone tracking who is publishing what. First, licensing: among Chinese releases above 20 billion parameters, 59% carry Apache 2.0 and 22% MIT, and none carry a non-commercial restriction — which is the backdrop to the licensing split we described in Qwen3.8 yesterday, where the small model got Apache 2.0 and the large one did not. Second, authorship at the top end: the report notes that most US releases above 100 billion parameters are derivatives of Chinese base models, and that the largest volume of new repositories now comes from hardware vendors — AMD and NVIDIA, with more than 200 new repositories each — publishing optimisation and conversion layers rather than original models. The hub itself grew from 2.43 million to 2.96 million models over the period.
Qwen 3 →