Skip to content

Qwen Tops 151,000 Derivatives as Small Models Dominate Real Usage

Original: Qwen Tops 151,000 Derivatives as Small Models Dominate Real Usage View original →

Read in other languages: 한국어日本語
LLM Aug 15, 2026 By Insights AI (Twitter) 2 min read 1 views Source

Attention and deployed usage point to different winners

Hugging Face’s Summer 2026 review shows why open-model competition cannot be read from parameter counts or launch popularity alone. Among repositories that declare model size, systems below one billion parameters account for 83% of all-time downloads, while models above 100 billion account for 1%. Looking only at 2026 activity changes little: models above 70 billion parameters receive just 3% of downloads.

“Frontier models are getting larger, but small models still dominate real-world usage. Qwen leads local inference, followed by Gemma. AI agents are becoming a major force on the Hub.” — Hugging Face

Hugging Face’s account covers its model hub, datasets, open-source tooling, and ecosystem measurements. The linked report distinguishes likes, which capture attention around a release, from downloads, which accumulate when a model is wired into a recurring pipeline. On that second axis, Qwen has become a foundation for 151,448 derivative repositories—2.6 times Meta’s total footprint and 4.7 times the number specifically derived from Llama.

Local formats and agents form a new distribution layer

Qwen-based GGUF files receive 39.6 million downloads per month, nearly twice Gemma’s 20.8 million and more than five times Llama’s 7.5 million. Qwen derivatives grew by roughly 180 to 210 repositories per day during the first seven months of 2026. Small models remain practical on common hardware, while quantization tools increasingly make very large mixture-of-experts systems runnable across consumer machines.

Agent traffic is changing just as quickly. Claude Code represented 44.4% of identified agent-tagged Hub traffic in July, while Codex climbed from 10.4% in April to 20.8% in July. Nearly one quarter of July traffic came from harnesses not yet named in the dataset. That volatility suggests defaults and new client releases can move the distribution faster than a stable incumbent can form.

Watch whether download counts continue to correlate with active deployments, whether model labs publish signed official quantizations, and whether agent identifiers become consistent enough for durable market-share comparisons. The source post is on X, and Hugging Face’s full report explains the dataset and measurements.

Share: Long

Related Articles