Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face
Benjamin Laufer, Hamidah Oderinwale, Jon Kleinberg

TL;DR
This study analyzes 1.86 million models on Hugging Face, revealing complex fine-tuning lineages, genetic similarities, and evolutionary patterns that provide new insights into the structure and evolution of machine learning ecosystems.
Contribution
It offers the first large-scale empirical analysis of model family trees and evolutionary dynamics in ML models, applying biological concepts to understand model development.
Findings
Models exhibit family resemblance with shared traits within families.
Mutations in models are fast and directed, leading to greater similarity among siblings.
License types and model capabilities evolve in unexpected, directional ways.
Abstract
Many have observed that the development and deployment of generative machine learning (ML) and artificial intelligence (AI) models follow a distinctive pattern in which pre-trained models are adapted and fine-tuned for specific downstream tasks. However, there is limited empirical work that examines the structure of these interactions. This paper analyzes 1.86 million models on Hugging Face, a leading peer production platform for model development. Our study of model family trees -- networks that connect fine-tuned models to their base or parent -- reveals sprawling fine-tuning lineages that vary widely in size and structure. Using an evolutionary biology lens to study ML models, we use model metadata and model cards to measure the genetic similarity and mutation of traits over model families. We find that models tend to exhibit a family resemblance, meaning their genetic markers and…
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Taxonomy
TopicsEthics and Social Impacts of AI · Language and cultural evolution · Artificial Intelligence in Healthcare and Education
