Hugging Face
The AI community platform. Builds open-source tools (Transformers, Datasets) and hosts the largest model hub.
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This paper analyzes the Hugging Face Model Hub download history from June 2020 to August 2025, encompassing 851,000 models and 2.2B downloads, to understand concentration dynamics in the open model economy. The study reveals a shift away from US industry dominance by Google, Meta, and OpenAI towards unaffiliated developers, community organizations, and Chinese industry players like DeepSeek and Qwen. The analysis also identifies trends in model properties, including increased model size, multimodal generation, quantization, and MoE architectures, alongside decreased data transparency.
Provides a comprehensive longitudinal analysis of the open-weight AI model ecosystem, revealing shifts in economic power and model characteristics.
This paper introduces Nash Mirror Prox ($\mathtt{Nash-MP}$), an online algorithm for Nash Learning from Human Feedback (NLHF) that directly optimizes for a Nash equilibrium based on human preferences, avoiding explicit reward modeling. The key result is a theoretical proof of last-iterate linear convergence for $\mathtt{Nash-MP}$ to the $\beta$-regularized Nash equilibrium, with convergence rates independent of the action space size. The authors also provide an approximate version using stochastic policy gradients and demonstrate its practical application in fine-tuning large language models.
Proves last-iterate linear convergence for the proposed Nash Mirror Prox algorithm in Nash Learning from Human Feedback, with rates independent of the action space size.