Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging Face

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 traits exhibit more overlap when they belong to the same model family. However, these similarities depart in certain ways from standard models of asexual reproduction, because mutations are fast and directed, such that two `sibling' models tend to exhibit more similarity than parent/child pairs. Further analysis of the directional drifts of these mutations reveals qualitative insights about the open machine learning ecosystem: Licenses counter-intuitively drift from restrictive, commercial licenses towards permissive or copyleft licenses, often in violation of upstream license's terms; models evolve from multi-lingual compatibility towards english-only compatibility; and model cards reduce in length and standardize by turning, more often, to templates and automatically generated text. Overall, this work takes a step toward an empirically grounded understanding of model fine-tuning and suggests that ecological models and methods can yield novel scientific insights.