Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging
  Face
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.
689bcbb2fab6fdd2e52ac7b7
Benjamin Laufer
claimed_verified
2025-08-13T07:13:18.472Z
686e8063b7e195d8b69a4a76
https://cdn-avatars.huggingface.co/v1/production/uploads/686e8063b7e195d8b69a4a76/gilhXW6nxy-lEbUFKrIb0.jpeg
Benjamin Laufer
user
benlaufer
689bcbb2fab6fdd2e52ac7b8
Hamidah Oderinwale
claimed_verified
2025-08-13T07:13:21.765Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/679b0f84009d1e01589437a5/6w6BfIPPdj9SoedZRgN9J.png
Hamidah Oderinwale
user
midah
689bcbb2fab6fdd2e52ac7b9
Jon Kleinberg
Anatomy of a Machine Learning Ecosystem: 2 Million Models on Hugging
  Face
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.
Analysis of model family trees on Hugging Face reveals patterns in model fine-tuning, including family resemblance, license changes, and model card standardization.
model family trees genetic similarity mutation asexual reproduction licenses multi-lingual compatibility model cards ecological models