# The AI Community Building the Future? A Quantitative Analysis of Development
Activity on Hugging Face Hub

Cailean Osborne
Oxford Internet Institute
University of Oxford
Oxford, UK
<EMAIL_ADDRESS>
Jennifer Ding
The Alan Turing Institute
London, UK
<EMAIL_ADDRESS>
Hannah Rose Kirk
Oxford Internet Institute
University of Oxford
Oxford, UK
<EMAIL_ADDRESS>Corresponding author: Cailean Osborne,
<EMAIL_ADDRESS>
###### Abstract

Open source developers have emerged as key actors in the political economy of
artificial intelligence (AI), with open model development being recognised as
an alternative to closed-source AI development. However, we still have a
limited understanding of collaborative practices in open source AI. This paper
responds to this gap with a three-part quantitative analysis of development
activity on the Hugging Face (HF) Hub, a popular platform for building,
sharing, and demonstrating models. First, we find that various types of
activity across 348,181 model, 65,761 dataset, and 156,642 space repositories
exhibit right-skewed distributions. Activity is extremely imbalanced between
repositories; for example, over 70% of models have 0 downloads, while 1%
account for 99% of downloads. Second, we analyse a snapshot of the social
network structure of collaboration on models, finding that the community has a
core-periphery structure, with a core of prolific developers and a majority of
isolate developers (89%). Upon removing isolates, collaboration is
characterised by high reciprocity regardless of developers’ network positions.
Third, we examine model adoption through the lens of model usage in spaces,
finding that a minority of models, developed by a handful of companies, are
widely used on the HF Hub. Overall, we find that various types of activity on
the HF Hub are characterised by Pareto distributions, congruent with prior
observations about OSS development patterns on platforms like GitHub. We
conclude with a discussion of the implications of the findings and
recommendations for (open source) AI researchers, developers, and
policymakers.

_K_ eywords Open source AI $\cdot$ open source software $\cdot$ artificial
intelligence $\cdot$ Hugging Face Hub $\cdot$ repository mining

##  1 Introduction

Open source developers have become central actors in the political economy of
artificial intelligence (AI). The rise of open source AI, specifically the
practice of releasing and fine-tuning pre-trained models that are freely
available111N.B. We use “open models” because they do not meet the
requirements to be considered “open source” [1, 2]., has extended open science
practices crucial to AI advances [3, 4, 5], including open source software
(OSS)222As defined by the OSI [2], OSS is software source code that anyone can
inspect, use, modify, or redistribute. and open access to research papers [6]
and datasets [7, 8, 9]. Open source AI has attracted attention as a potential
challenger to the dominance of startups and Big Tech in AI research and
development (R&D) [10, 11]. Grassroots initiatives like EleutherAI [12],
BigScience [13], and BigCode [14] have shown the feasibility of open model
development [15], while the Hugging Face (HF) Hub has emerged as a popular
platform used by millions to host, download, and collaborate on a growing
number of models, datasets, and spaces (i.e., web applications to demonstrate
and try out models) [16].

While the benefits and risks of open source AI have been widely debated [17,
18, 19, 20], the practices and processes involved in open model development
have received relatively little attention. To date, only a handful of scholars
have explored various aspects of open model development, including user
contributions to grassroots initiatives [13, 15], the role of commercial
participation [15, 21], and model maintenance [22]. We contribute to this
nascent research agenda with a three-part quantitative analysis of development
activity on the HF Hub. First, we investigate typical patterns of various
types of activity on the HF Hub in 348,181 model, 65,761 dataset, and 156,642
space repositories (RQ1). Subsequently, we apply social network analysis (SNA)
of code contributions to model repositories to investigate the social network
structure of the developer community as well as collaboration practices
amongst developers (RQ2). We replicate this analysis for models in the sub-
fields of natural language processing (NLP), computer vision (CV), and
multimodal (MM) for comparative analysis. Finally, we quantify model adoption
through the lens of model usage in spaces on the HF Hub (RQ3), providing
insights into the widespread use of a minority of models in the HF Hub
developer community as well as the key actors driving the development of the
most widely used open models.

Overall, our analysis reveals that various aspects of development activity on
the HF Hub—e.g., interactions in model, dataset, and space repositories;
collaboration in model repositories; and model adoption in spaces—exhibit
right-skewed, Pareto distributions, which is a well-documented pattern in OSS
development [23, 24, 25, 26, 27]. While the open model development life-cycle
involves unique practices which differ from OSS development [22], such as
model training and fine-tuning, the observed similarities in the overall
patterns of activity suggests that future research on open source AI can
benefit from drawing on the extensive, multidisciplinary literature on the
social dynamics of OSS development. Based on our findings, we propose a number
of recommendations for researchers, policymakers, and platform providers to
facilitate research and evidence-based discussions on open source AI.

The paper has the following structure. First, the literature review provides
an overview of prior work on open source AI, as well as prior work on OSS
development in order to draw comparisons between OSS and open model
development practices. Second, we presents the RQs and research design. Third,
we introduce the main findings from the three-part analysis. Fourth, we
discuss the contributions of the findings to research and practice, and make
recommendations for research and practice. We conclude with a discussion of
what further clarification of the practices in open model development can
offer for (open source) AI researchers, developers, policymakers, and platform
providers.

##  2 Related Work

###  2.1 “We have no moat”: The Emergence of Open Models

Open science practices, from the development of open source software (OSS) to
the provision of open access to research papers (e.g., via arXiv [6]) and
datasets (e.g., via Kaggle [7], ImageNet [9], or Common Crawl [8]), have been
integral to advances in AI R&D and adoption [4, 28]. The culture and norms of
openness in AI has evolved significantly in the last 15 years [29]. For
example, in 2007, a coalition of 16 researchers lamented the lack of OSS that
standardised the implementation of ML algorithms, highlighting this as a major
obstacle to advances and reproducibility in AI research [30]. Yet today AI R&D
is simply unimaginable without OSS [3, 31], drawing on a growing commons of
over 300 OSS libraries [32], hundreds of thousands of open models [33], and
over a million OSS repositories [34].

Following years of debate about the safety of openly releasing AI models [35,
18, 19, 36], recent years have seen the emergence and proliferation of “open”
models, which individuals and organisations have shared on an open access
basis on platforms such as the HF Hub [4]. Prior to this, AI models, in
particular large language models (LLMs), were principally developed and
maintained behind closed doors, albeit with open science practices, such as
the sharing of publications on arXiv and code on platforms like GitHub. The
start of this trend is attributed to EleutherAI, a grassroots research group,
which formed on a Discord server with the intention to develop and release an
open source variant to OpenAI’s GPT, resulting in The Pile in December 2020
[37], a library of datasets for training LLMs, and GPT-Neo in March 2021 [38].
Subsequently, open models gained more visibility with the release of other
state-of-the-art AI models [11], including BLOOM by the BigScience workshop in
July 2022 [39], Stable Diffusion by Stability AI in August 2022 [40], and
LLaMA 2 by Meta in July 2023 [41], amongst others.

The proliferation of open models, especially foundation models, has ignited
heated debate about their potential benefits and risks [19, 17, 18, 42, 20].
On the one hand, open models are said to promise benefits for research,
innovation, and competition by lowering entry barriers and widening access to
state-of-the-art AI [43]. Drawing on Linus’ Law from OSS development that
“given enough eyeballs, all bugs are shallow” [44], proponents argue that open
model development and auditing offers safety advantages [45]. In addition,
open access to models lowers the barriers for adaptability and customisation
for diverse language contexts [46, 19]. On the other hand, open models can
pose risks of harm by both well-intended and malicious actors, including the
creation of deepfakes [47, 48, 49], disinformation [50, 51], and malware [52,
53]. A study by 25 experts concluded that open models have five distinctive
properties that present both benefits and risks: broader access, greater
customisability, local adaptation and inference ability, the inability to
rescind model access, and the inability to monitor or moderate model usage
[19].

The development of open models has been described as a potential challenge to
the dominance of Big Tech companies in AI R&D [10, 11]. This was underlined by
a leaked Google memo that claimed there is “no moat around closed-source AI
development” and “open source solutions will out-compete companies like Google
or OpenAI” [54]. Venture capitalists have bullishly invested in open source AI
startups [55, 56], and world leaders like President Macron of France have
pledged public funds to support open source AI [57]. In addition, the Mozilla
Foundation has launched mozilla.ai with $30 million in investment to build a
trustworthy, independent, and open source AI ecosystem “outside of Big Tech
and academia” [58]. While proponents champion open models as good news for
innovation and competition, others temper this optimism by pointing to market
concentrations at several layers of the AI stack, from chips to cloud compute
infrastructure, which remain unchallenged by innovations stemming from open
source AI communities [59, 60, 21].

It is important to note that a myriad of meanings are attached to “open
models” and “open source AI”. Oftentimes it is understood as making pre-
trained models, parameters (or “weights”), and documentation available on
platforms like the HF Hub. In some cases, it refers to the open collaboration
employed to build open models [15]. The description of open models as “open
source” has been fiercely contested for failing to meet OSS standards as
defined by the OSI [2, 4, 61, 62]. For example, a review found that, “[W]hile
there is a fast-growing list of projects billing themselves as ’open source’,
many inherit undocumented data of dubious legality, few share the all-
important instruction-tuning (a key site where human annotation labour is
involved), and careful scientific documentation is exceedingly rare” [63].
Furthermore, companies have been criticised for “open-washing” by describing
their models as "open source," to disguise their strategies to set open
standards and benefit from crowdsourced contributions [21, 60, 64, 11]. For
example, when Meta imposed limits on use of LLaMA 2, Stefano Maffulli from the
OSI commented, “Unfortunately, the tech giant has created the misunderstanding
that LLaMA 2 is ‘open source’ – it is not. Meta is confusing ’open source’
with ‘resources available to some users under some conditions,’ [which are]
two very different things” [61].

It remains an open question whether one can or should classify AI models as
either open or closed-source. Through a global, multi-stakeholder approach,
the OSI is currently developing a definition of open source AI as AI systems
that are made available under terms that grant the freedoms to use, study,
modify, and share the system [65, 1]. Countering binary approaches, Irene
Solaiman [18] makes the case that AI systems are not either fully open or
fully closed; rather, the openness of AI systems can be plotted along a
gradient with six degrees of openness. Each grade of openness involves trade-
offs between concentrating power and mitigating risks [18]. As the field
rapidly evolves, developing responsible practices, norms, and regulation
around open source AI remains a critical challenge [42, 43].

###  2.2 A Nascent Research Agenda on Open Source AI

While the benefits and risks of open models have been widely discussed, we
still have a limited understanding of the collaborative practices involved. In
this section, we review prior work on open model development and motivate our
empirical analysis of development activity on the HF Hub to address this
research gap.

The HF Hub has emerged as a popular platform used by individuals and
organisations to share, download, and collaborate on models, datasets, and
spaces [66, 67]. The HF Hub is a “model marketplace,” which is “a new form of
user-generated content platform, where users can upload AI systems and AI-
related datasets, which in turn can be downloaded, and depending on the
business model, queried, tweaked, or built upon by other users” [68]. Much of
the activity amongst the emerging developer community on this platform
concerns individuals fine-tuning pre-trained models that were released by
industry leaders for downstream use in research and applications [21]. In
addition to the hosting and fine-tuning of open models, a few grassroots
initiatives have embraced open collaboration methods to develop open models.
For example, the development of BLOOM, a 176B parameter multilingual LLM, and
its training dataset, ROOTS, was the largest “open source” AI collaboration to
date, involving over 1,000 volunteers from over 70 countries and over 250
institutions [13]. Such initiatives have demonstrated alternative pathways for
AI model development beyond the handful of companies that dominate the AI R&D
[10, 15]. Prior work has also highlighted the leadership role of companies,
such as Hugging Face, in organising “values-driven initiative[s]”, such as the
BigScience workshop, and attracting contributors who have diverse motivations,
from developing new skills and working on new problems to publishing research
giving back to the ecosystem [15, 13].

Due to the growing popularity of the HF Hub, scholars have examined the
suitability of the HF Hub for empirical research on open model development
[67, 69].333The authors define “suitability for empirical research” as “the
amount and adequacy of the features to enable software development practices
and the sufficient quantity of data to enable the conduction of empirical
studies about such practices” [67]. Castaño et al. [22] provide most
comprehensive empirical insights into maintenance practices in model
repositories on the HF Hub.444Model maintenance is defined as “a higher number
of commits, regular commit frequency, shorter intervals between commits, fewer
days without commits, and a slightly higher number of authors” [22]. They find
that commit activity follows a right-skewed distribution, with a few models
receiving extensive activity while the majority of repositories receive
limited activity [22]. While the majority of models are developed by singular
developers (1.18 mean, 1.0 median), some model repositories, such as
$bigscience/bloom$ or $bigcode/santacoder$, are co-developed and co-maintained
by up to 20 developers [22]. They also find that developers tend to prioritise
“perfective tasks” to enhance model performance and align with technological
advances, unlike OSS maintenance that focuses on bug fixes and feature
additions [22]. The authors contend this “reveals the need for methods and
tools specifically designed for the unique demands of ML model maintenance.
Such tools may include advanced version control systems optimized for data and
model tracking, as well as automated monitoring tools capable of detecting
model drift or degradation” [22]. Prior work has also examine carbon emission
reporting in model repositories, finding stagnation in emissions reporting by
developers and highlight and the need for improved reporting practices and
carbon-efficient model development on the HF Hub [70].

Our research builds on this prior work. As one of the first studies to
investigate open model development practices, in the next section we draw on
prior work on OSS development in order to be able to compare our findings to
prior research and to lay the groundwork for a more comprehensive
understanding of open model development in the future.

###  2.3 Learning from Prior Work on OSS development

Prior work on OSS development provides a valuable framework for understanding
the social dynamics of open model development. In the early 2000s, a number of
metaphors were used to describe the social structure of “the OSS community”.
For example, the Linux developer community was described as a “bazaar” that
vibrated with the activity of geeks, hackers, and hobbyists, who performed
various tasks, from bug-spotting to writing code to “serving the hacker
culture itself” [44]. However, prior work illustrates that OSS communities
have diverse social structures [71, 72], from “caves” with singular developers
[73] to “core-periphery” networks, akin to “layered onions” [74], with uneven
activity distributions ranging from core contributors (e.g., project
initiators) to users (e.g., bug-spotters) [75, 76, 77, 78].

Numerous studies highlight that various types of activity in OSS development,
such as discussions in mailing lists, bug-spotting in issue trackers, and
commit activity, exhibit right-skewed, Pareto distributions [23, 25, 27].
Indeed, it is well-documented observation that OSS development is typically
characterised by the Pareto principle, commonly known as the 80/20 rule or the
law of the vital few, which states that approximately 80% of effects come from
20% of causes [79]. These findings are congruent with a wide range of Internet
phenomena, which similarly exhibit right-skewed distributions, which follow
power laws [80, 81]. However, there are exceptions to the rule; for example, a
study of 2,496 projects on GitHub found that the Pareto principle does not
always characterise development activity in OSS repositories, thus
highlighting the need to be cautious about generalising the Pareto principle
as an incontestable law of OSS development [82]. Furthermore, many activities,
such as mentorship and hackathons, take place outside of the repository [83,
84, 85, 86] and are therefore invisible to quantitative scholars of OSS
development practices.

The various social structures of OSS communities are shaped, amongst others,
by the diverse incentives of individuals and companies that participate in OSS
development [87, 88]. Individual developers are typically motivated by factors
such as personal values, altruism, enjoyment, reputation-building, and career
benefits [89, 90, 91, 92]. However, there are also major barriers to
participation, including gender disparities [93, 94] and geographic
inequalities [84, 86]. Activity tends to be concentrated in the Global North
[95] and the English lingua franca is a barrier for many developers [86, 96].
Furthermore, the incentives of OSS developers vary by geography: while
developers in the USA show a relatively strong interest in “geek culture”,
developers in India and China tend to be motivated primarily by career
benefits [97]. Thus, “researchers studying open source should be mindful of
geographic variation in what motivates participation and what forms
participation may take, particularly outside of the code repository” [84].

Meanwhile companies primarily participate in OSS development for economic and
strategic reasons, such as recruiting developers [98, 64, 99], reducing costs
[100, 101, 64], influencing OSS projects [102, 101], promoting open standards
[100, 103, 104], and building a reputation as an OSS patron [88, 105, 31].
Commercial participation has mixed effects on the social structure of OSS
communities. Typically, one company or a few companies emerge as dominant
contributors in projects [27, 106]. The dominance of a company is negatively
associated with the participation of volunteers, while it is positively
associated with the productivity of contributors and the quality of issue
reports [107, 72]. It is also common for companies, which may be market
rivals, to collaborate in OSS ecosystems [108, 109, 106, 110, 111], which has
turned many OSS communities “from networks of individuals into networks of
companies” [98].

Building on this prior work, this study aims to provide novel insights into
the collaborative dynamics of the HF Hub. Specifically, we investigate typical
patterns of development activity across model, dataset, and space repositories
the HF Hub (RQ1), the social network structure of its developer community
(RQ2), as well as adoption and key actors driving the development of the most
widely-adopted models (RQ3). The research extends the literature by shedding
light on the practices involved in open model development on this increasingly
important platform. The findings contribute to a more comprehensive
understanding of and lay the groundwork for future research on open model
development.

##  3 Study Design

###  3.1 Research Aims & Research Questions

This study extends the nascent research agenda on open model development with
a quantitative analysis of development activity on the HF Hub. We adopted a
quantitative approach to explore large-scale patterns and trends in
development activity on the HF Hub, which is a suitable approach when one
seeks to generate baseline insights on a new phenomenon [112]. In particular,
we examine different aspects of development activity on the HF Hub via the
following RQs:

  * •

RQ1: What are typical patterns of development activity across the HF Hub?

  * •

RQ2: What is the social network structure of the HF Hub developer community?

  * •

RQ3: What is the distribution of model adoption on the HF Hub, and who are the
key actors driving the development of the most widely-adopted models?

These RQs examine different aspects of development activity on the HF Hub. RQ1
focuses on identifying common patterns across various types of activity, such
as likes, discussions, commits, and downloads, in the repositories of models,
datasets, and spaces. Concretely, this analysis expands prior work that
focuses on commit activity in model repositories [22]. RQ2 concerns the social
network structure of the developer community on the HF Hub. In particular, we
analyse a snapshot of collaboration interactions in model repositories amongst
around 100,000 developers, building on prior descriptions of collaboration on
open models [15, 13] and maintenance practices [22]. Lastly, RQ3 empirically
tests a prior observation of uneven model adoption and the influence of Big
Tech companies [21] by examining the distribution of model use in spaces and
identifying the developers of the most used models. In addition, we examine
model co-usage patterns to provide insights into the interdependencies and
ecosystems surrounding popular models.

###  3.2 The HF Hub: A New Platform & Source of Research Data

The HF Hub was launched in 2021 by Hugging Face, a startup whose mission is to
“democratize AI” [66]. The HF Hub is a Git-based social coding platform,
widely used by researchers, developers, and hobbyists to share, discover,
discuss, and collaborate on open models [113], datasets [114], and spaces
[115]. Spaces are interactive web applications that facilitate the creation of
demonstrations and make models hosted on the platform more accessible to end-
users. The platform provides a number of tools for open model development,
such as version control for collaboration and tracking [113], and evaluation
and benchmarking of model performance [116]. The HF Hub API allows
programmatic access to platform resources as well as metadata from
repositories hosted on the platform [16]. In light of its features and data
availability, prior work underlines the platform’s suitability for empirical
studies on open model development [67, 22]. Building on this prior work, this
paper aims to advance the research community’s understanding of the
development practices in open model development as well as methodological
considerations regarding the HF Hub.

When using data from the HF Hub, it is important to consider the ethical
implications and adhere to the platform’s terms of service. In the study, we
only collected publicly available data through the official HF Hub API,
respecting the privacy settings of users and repositories. For example, we did
not attempt to access or include data from private repositories in the
analysis. Additionally, we anonymised the collected data by focusing on
aggregate measures and avoiding the disclosure of personally identifiable
information in the findings. Ethical clearance for this study was obtained
from the CUREC institutional review board at the University of Oxford.

###  3.3 Data Collection

We collected data via the HF Hub’s API in October 2023 [16], using Python
scripts that are available on GitHub [117]. For RQ1, we collected and
processed metadata for a number of activities from the public repositories of
348,181 models, 65,761 datasets, and 156,642 spaces, using the list_models(),
list_datasets(), and list_spaces() API endpoints. These included: likes
(n_likes), downloads (n_downloads)555N.B. We do not report data for downloads
of spaces because spaces cannot be downloaded., discussions (n_discussions),
commits (n_commits), unique developers who have contributed commits
(n_commiters), unique developers who started discussions
(n_disc_starters),666N.B. As per the API, data collection for participation in
discussions was limited to users that had started discussions. It was not
possible to collect data about users that had made comments in discussion
threads. and the repository’s community size (n_community), calculated as the
cardinality of the set union of n_disc_starters and n_commiters. As per prior
work [118, 111, 119], we removed bots and merged multiple developer identities
before enumerating n_disc_starters, n_commiters, and n_community. As a result,
n_community is recorded as 0 if no user has made a commit or started a
discussion in the repository, which ignores the creator of the repository. We
acknowledge that alternatively such repositories could have the value 1.

For RQ2, we operationalised collaboration on models as instances where a pair
of developers contributed commits to the same model repository, with direct
edges recorded between developers that were weighted by the number of times a
developer contributed a commit to the same repository as the other developer
[120]. We operationalised commit activity as acts of collaboration because
commits are easily measurable, represent “validated” contributions, and
represent an accurate audit trail of collaboration [78, 111]. However, we
acknowledge that the fact that two developers commit to the same repository
does not necessarily imply direct interaction; for example, it would have been
more accurate to focus on developers’ contributions to the same file in a
repository, as we discuss in Section 5.3. Formally, we modelled collaboration
as a network $N=(D,E,W)$, where $D$ is the set of developers,
$E=\\{(i,j,w_{ij})\mid i,j\in D,w_{ij}\in\mathbb{N}\\}$ is the set of directed
edges denoting the relationships between developers, and
$W=\\{w_{ij}\mid(i,j,w_{ij})\in E\\}$ represents the weights associated with
each directed edge. For a developer pair $i$ and $j$, we denote the directed
relationship as $(i,j,w_{ij})$, where $w_{ij}$ signifies the number of times
developer $i$ has committed to the same repository as developer $j$.

To collect data for the analysis of RQ2, we collected commit data from public
model repositories via the HF Hub API. We started by retrieving a list of all
available model IDs using the list_models() endpoint. Then, for each model
repository, we used the list_repo_commits() endpoint to retrieve the commit
data, including the authors associated with each commit. For each commit, we
recorded an edge between the the developer who made the commit (source_node)
and all other developers who had contributed to the repository (target_node).
In cases where a repository had only one contributor, we created self-loop
edges to capture the isolate contributor’s activity. We did not take temporal
dynamics of commit activity into account, which we discuss as a threat to
construct validity under Section 5.3. We collected data for collaboration in
NLP, CV, and MM model repositories by filtering repositories based on the
tags, which developers add to their repositories to aid discoverability on the
HF Hub. We used the list of tags per sub-field provided by the HF Hub,
including computer-vision and image-classification for CV models; translation
and summarization for NLP models; and image-to-text and image-to-video for MM
models.

For RQ3, we collected data on model usage in spaces using the list_models()
and model_info() API endpoints. We modelled model usage in spaces as a
bipartite network, akin to the representation of software dependency networks
[121]. The bipartite model usage network is denoted as $D=(M,S,E)$, where $M$
is the set of models, $S$ is the set of spaces, and $E=\\{(m,s)\mid m\in
M,s\in S\\}$ is the set of undirected edges signifying that “space” $s$ uses
model $m$. The edges are unweighted, representing the model usage relationship
between a “space” and a model. From the bipartite network $D$, we derived an
undirected model co-usage network $C=(M,E,W)$. In this network, $M$ is the set
of models, $E=\\{(m_{i},m_{j})\mid m_{i},m_{j}\in M\\}$ is the set of
undirected edges connecting models based on their co-usage in a “space”, and
$W=\\{w_{ij}\mid(m_{i},m_{j})\in E\\}$ is the set of weights assigned to the
edges, reflecting the frequency of co-usage of models $m_{i}$ and $m_{j}$
across spaces. This analysis complements the former analysis of model usage
with insights into the interdependencies and ecosystems surrounding widely
used models on the HF Hub.

###  3.4 Username Merging

Following prior work, before the analysis, we undertook data preprocessing to
merge multiple developer identities per unique developer, which can be caused
by how Git records usernames based on users’ local repository credentials [75,
122, 123, 119, 27]. We assumed this might be an issue on the HF Hub, too. To
ensure the accuracy of the dataset of 101,144 developers, we applied a three-
pronged approach. First, we classified username string similarity
(threshold=90%) between pairs of developers who contributed to the same
repository, accepting 126 out of 180 (70.00%) pairs based on manual username
searches on the HF Hub. Second, in light of the presence of potential real
names (i.e. usernames with spaces like “Jessica Smith”), we examined string
similarity (threshold=90%) between 1,979 potential real names and the
remaining 99,041 usernames, accepting 358 out of 403 (87.75%) username pairs
after manual searches on the HF Hub. Finally, we inspected the usernames of
700 developers with a network degree of 10 or higher, who represented 0.7% of
developers but accounted for 44.78% of edges, via manual searches on the HF
Hub. This resulted in the identification of 212 username pairs. In total, we
merged 546 usernames after removing duplicates.

###  3.5 Data Analysis

To investigate development activity on the HF Hub (RQ1), we conducted a
descriptive analysis of various types of activity in 348,181 model
repositories, 65,761 dataset repositories, and 156,642 space repositories.
Pearson correlation coefficients were calculated to assess the pairwise
relationships between the activity variables. In addition, we employed the
Mann-Whitney U test to compare the levels of activity across repositories with
different licences (Permissive, Restrictive, and No licence). The Mann-Whitney
U test is a non-parametric test that examines whether two independent samples
come from the same distribution, which does not require the data to be
normally distributed or to meet the assumption of homogeneity of variance
[124]. Given the large sample sizes, the U values are expected to be large,
and the salient test statistic is the p-value which indicates the statistical
significance of observed differences. Due to capacity constraints in labelling
licences, we limited this analysis to repositories with licences used in at
least 100 repositories ($n=$339,502, 98% of all repositories). Subsequently,
we analysed a snapshot of the social network structure of collaboration on the
HF Hub (RQ2), using techniques defined in Table 1 in Appendix A. This analysis
provides insights into collaboration patterns in model repositories at this
point in time. Furthermore, we analysed collaboration patterns in the three AI
sub-fields (NLP, CV, and MM) to enable comparisons. Lastly, we examined model
adoption on the HF Hub (RQ3) by calculating the ranked degree of models in the
bipartite model usage networks and ranked degree of models in the model co-
usage networks to identify the most used models in spaces and their respective
developers. These two complementary approaches quantified model popularity
(i.e. which models are most frequently used in spaces) and model co-popularity
(i.e. which models are most commonly used in conjunction with other models).
We replicated this analysis for spaces with NLP, CV, and MM tags for
comparative analysis of the three AI sub-fields.

##  4 Results

We first report results for activity in the 348,181 model, 65,761 dataset, and
156,642 space repositories in Section 4.1, relying on the metrics described in
Section 3.3. We then report results on the structure and dynamics of
collaboration in Section 4.2, based on the analysis of collaboration
interactions between around 100,000 developers in model repositories. Finally,
we present the results of our analysis of model adoption in spaces in Section
4.3, where we examine the distribution of model usage in spaces on the HF Hub
and identify the developers of the most used models.

###  4.1 Development Activity on the HF Hub

In this section, we present the findings of development activity in the
repositories of 348,181 models, 65,761 datasets, and 156,642 spaces on the HF
Hub. We present three key findings: right-skewed distributions across
different types of activity (Section 4.1.1), strong correlations between
development activities (Section 4.1.2), and a significant lack of licences in
model and dataset repositories (Section 4.1.3).

####  4.1.1 Right-Skewed Distributions in development Activity

Activity per repository is extremely imbalanced. We observe right-skewed
distributions of n_likes, n_discussions, n_commits, and n_downloads across the
model, dataset, and space repositories (see Figure 1). For example, while the
maximum number of likes amongst models is over 9,000, the average model only
receives 1.14 likes (see Table 4). The majority of repositories receive
minimal engagement. For example, 91% of models and 88% of datasets have 0
likes; 84% of models, 91% of datasets, and 96% of spaces have 0 discussions;
and 71% of models and 70% of datasets have 0 downloads. Meanwhile, most
activity is concentrated in a small number of repositories. For example, $<1$%
of models account for 80% of likes, 10% for 80% discussions, 30% for 80%
commits, and $<1$% for 80% downloads. Upon increasing the threshold, 8% of
models account for 99% likes, 15% for 99% discussions, and 1% for 99%
downloads.

Most repositories have a community size of 1; for example, 87% of model
repositories have 1 contributor and the 75th quartile value of n_committers is
1 across repository types (see Table 4). The respective maximum values of
n_committers are 18, 100, and 282 across repository types, and the respective
maximum values of n_community are 246, 110, and 4,685. The differences between
n_committers and n_community are due to large n_disc_starters values,
indicating a division of roles in repositories, where many developers
participate in discussions but few are involved in model maintenance. The
model repositories with the most n_committers are bigscience/bloom ($n$=$18$),
bigcode/santacoder ($n$=$16$), and deepset/roberta-base-squad2 ($n$=$15$).

Figure 1: Distributions of Development Activity in Repositories on HF Hub

####  4.1.2 Correlation between Community Size and Engagement

We correlate frequency counts over the different types of activity described
in Section 3.3 (see Figure 2). In model repositories, we find a strong
positive correlations between n_community and n_likes ($\rho=0.75$,
$p<0.001$). In space repositories, we find strong correlations between various
activities, especially n_likes) and n_discussions ($\rho=0.74$, $p<0.001$),
n_disc_starters ($\rho=0.76$, $p<0.001$), and n_community ($\rho=0.76$,
$p<0.001$). However, in general, we observe weak correlations between most
activities in model and dataset repositories. Furthermore, we do not find a
strong correlation between commit activity (n_commits) and other types of
activity, indicating that commit activity is not strongly linked to community
engagement.

####  4.1.3 Impact of Licences on Collaboration

A significant proportion of model and dataset repositories lack licences,
which can create uncertainty and potential legal issues for users and
developers. Specifying a licence is not the norm: the majority of model
repositories (65%) and datasets (72%) do not have a licence. Amongst the
licenced models, the most commonly used licences are Apache v2.0 (37%), MIT
(17%), OpenRAIL (14%), and CreativeML OpenRAIL-M (10%). The most used licences
for datasets are MIT (28%), Apache v2.0 (15%), OpenRAIL (9%), and licences
from the family of Creative Commons v4.0 (7%).

The choice of licence matters: there is a moderate to strong correlation
between the use of a licence and level of activity in model repositories (see
Figure 3). Furthermore, the Mann-Whitney U tests provide strong evidence of
statistically significant differences between collaboration dynamics in model
repositories with different types of licences (all tests have $p<0.001$).
Specifically, model repositories with permissive licences consistently have
the highest levels of activity compared to model repositories with no licence
and those with restrictive licences (see Table 5). However, repositories with
restrictive licences also exhibit significantly higher activity than those
with no licence. This pattern holds across all activity metrics measured,
suggesting that while permissive licences foster the highest engagement,
restrictive licences also promote more collaboration compared to model
repositories that do not have a licence.

Figure 2: Correlations of Activity in Model, Dataset, and Space Repositories
Figure 3: Correlations of Activity in Model Repositories with Different
Licences

###  4.2 Social Network Structure and Dynamics of Collaboration

In this section, we present findings from our analysis of a snapshot of the
social network structure of collaboration in model repositories on the HF Hub.
We begin with the structure and dynamics of collaboration in all model
repositories (see Section 4.2.1), and then we compare collaboration patterns
in Natural Language Processing (NLP), Computer Vision (CV), and Multimodal
(MM) model repositories (see Section 4.2.2).

####  4.2.1 Collaboration in Model Repositories on the HF Hub

The HF Hub collaboration network exhibits a right-skewed degree and PageRank
centrality distributions, which indicates that influence in the HF developer
community is concentrated amongst a small subset of developers. The majority
of developers (89%) have not collaborated with others. Excluding these isolate
developers, the remaining 10,524 developers have an average degree of 4.10
(SD: 32.63) and node degrees range from 1 to 3,140. The right-skewed
distributions of degree and PageRank centrality (see Figure 4) suggest that a
small group of influential developers plays a central role in driving
collaboration on open models on the HF Hub. Specifically, the degree
centrality distribution has a mean of 4 and a median of 2, with a maximum of
3,140 and a standard deviation of 33, while the PageRank centrality
distribution has a mean and median of 0.0001, a maximum of 0.04, and a
standard deviation of 0.0005.

The HF Hub developer community exhibits a core-periphery structure, with a
tightly interconnected core of prolific developers. The k-core decomposition
analysis reveals that as the k-core value increases, the number of distinct
communities decreases, ultimately converging into a single densely
interconnected core at k=26 (see Table 9). The high modularity (0.81) at k=1
suggests that the whole network consists of loosely connected groups of
developers. As the k-core value increases, the modularity decreases to 0.00 at
k=26, indicating a transition from a compartmentalised community structure
with distinct clusters or modules to an integrated core characterised by high
cohesion and a lack of discernible sub-groups. Concurrently, the sub-network
density increases, reaching unity at k=26.

Collaboration is characterised by high reciprocity values, ranging from 0.81
to 1.00 across all k-core levels (see Table 9), indicating the prevalence of
mutual relationships amongst developers. The low assortativity values, ranging
from -0.49 to 0.08, suggest that developers collaborate regardless of their
centrality in the network, implying that other factors, such as shared
interests, skills, or project roles, may be more significant in driving
collaboration than their network centrality. Furthermore, the relatively low
average rich club coefficients, ranging from 0.04 to 0.41, indicate that
highly central developers do not primarily collaborate with each other,
further emphasising that collaboration is not solely driven by the network
centrality of developers.

Figure 4: PageRank and Degree Distributions of Developers on the HF Hub

####  4.2.2 Collaboration in Model Repositories in AI Sub-Fields

Collaborations on models in sub-fields of natural language processing (NLP),
computer vision (CV), and multimodal (MM), despite the different sizes of the
respective communities, are similarly characterised by core-periphery
structures ith high modularity and low density (see Tables 9,9, and 9). At
k=1, all networks are highly modular (CV: 0.80, NLP: 0.82, MM: 0.71) and have
very low density (CV: 0.01, NLP: 0.00, MM: 0.00), implying that collaborations
in the respective AI sub-fields are clustered into distinct communities of
collaborators. As the k threshold increases, the networks undergo a similar
transformation process, with modularity decreasing to 0.00 and the number of
communities reducing to a single cohesive community at the maximal k values
(CV: 10, NLP: 25, MM: 26). Concurrently, density increases, reaching 1.00 for
CV and MM and 0.97 for NLP at their respective maximal k values.

Collaboration in sub-fields is also similarly characterised by reciprocity and
connectivity in the core. At k=1, reciprocity values range from 0.84 to 0.93
and increase to 1.00 at the maximal k for CV and MM, while NLP maintains a
high reciprocity of 0.98 at its maximal k. The average degree increases with k
for all networks, reaching the corresponding maximal k value at the highest
threshold. This suggests that as we move towards the core of the collaboration
networks, developers become more interconnected and collaborate with a larger
number of peers. However, the low average clustering coefficients and low
average rich club coefficients across all networks indicate that the more
prolific developers in the respective sub-fields tend to collaborate with a
diverse set of individuals rather than forming tightly-knit groups.

###  4.3 Model Adoption in Spaces on HF Hub

In this section, we present the results of the analysis of model usage in
spaces on the HF Hub, shedding light on model adoption and key developers in
this ecosystem. Specifically, we present two key findings: model adoption in
spaces is characterised by a right-skewed distribution (Section 4.3.1), and a
small cohort of developers (in particular, Big Tech companies) build the most
used models across all spaces as well as in the three AI sub-fields (Section
4.3.2).

####  4.3.1 Right-Skewed Distribution of Model Adoption

The bipartite model usage network displays a disparity in model adoption in
spaces. The degree distribution of the bipartite network is right-skewed, as
shown in Figure 5. Only three models are used in 1,000 or more spaces,
including runwayml/stable-diffusion-v1-5 ($n$=$1747$), skytnt/anime-seg
($n$=$1162$), and gpt2 ($n$=$1002$). The mean degree (6.68) is significantly
higher than the median (1.00), and the large standard deviation (34.75)
confirms the high variability in model usage. The majority of models have a
low degree of usage, with at least 50% being used in only one space, while a
small number of highly popular models dominate the usage, with the maximum
degree reaching 1,747. This suggests that a few key models are widely adopted
in AI applications, while many other models have limited use cases. The model
co-usage network provides an additional perspective on the uneven
interdependencies of models in spaces, complementing insights gained from
examining model downloads or individual model usage in spaces. Specifically,
the degree distribution of this network exhibits a multi-modal pattern, with
five distinct clusters, each exhibiting a right-skewed shape (see Figure 5). A
small cluster at the far-right tail of the distribution represents a few
highly interconnected models with significantly higher co-usage degrees
compared to the other clusters.

Figure 5: Degree Distribution of Model Adoption in Spaces on the HF Hub

####  4.3.2 Dominance of a Few Models by a Few Developers

When we rank the models by their usage in spaces, we observe that major
organisations, rather than individual developers or grassroots initiatives,
have developed the most used models. Amongst the 100 most used models in
spaces, the following organisations have developed the most models: Meta
($n$=$8$), Google ($n$=$7$), StabilityAI ($n$=$5$), OpenAI ($n$=$4$),
Microsoft ($n$=$4$), and Fudan University ($n$=$4$). These five organisations
account for 33% of the 100 most used models in spaces. We note that the
individual user nitrosocke ($n$=$5$), an employee at StabilityAI, ranked
highly amongst these organisations. With regards to the model co-usage
network, the key developers of the 100 most co-used models in all spaces are:
EleutherAI ($n$=$15$), Meta ($n$=$12$), h20ai ($n$=$11$), BigScience
($n$=$9$), and lmsys ($n$=$9$). These five organisations account for 56% of
the 100 most co-used models in spaces.

The model usage networks in the sub-fields similarly exhibit right-skewed
degree distributions, highlighting the dominance of a minority of models in
each sub-field. The most used models in spaces with NLP tags ($n$=$3,995$) are
gpt2 ($n$=$1,001$), bertbaseuncased ($n$=$621$), and gpt2medium ($n$=$445$).
The organisations that developed the most models amongst the 100 most used
models are Google ($n$=$9$), Meta ($n$=$5$), and Fudan University ($n$=$5$).
For comparison, in the NLP model co-usage network, EleutherAI ranks first
($n$=$16$), followed by h20ai ($n$=$12$) and Meta ($n$=$11$). The most used
models in spaces with CV tags ($n$=$416$) are saltacc/anime-ai-detect
($n$=$500$), openai/clip-vit-large-patch14 ($n$=$454$), and openai/clip-vit-
base-patch32 ($n$=$277$). The most prolific developer of models in spaces with
CV tags is the user lllyasviel ($n$=$20$), followed by Meta ($n$=$8$) and the
user DucHaiten ($n$=$7$). For comparison, in the CV model co-usage network,
LAION AI ranks as the developer of the most models amongst the top 100
($n$=$17$). Finally, the most used models in spaces with MM tags ($n$=$2,394$)
are runwayml/stable-diffusion-v1-5 ($n$=$1748$), CompVis/stable-diffusion-v1-4
($n$=$925$), and stabilityai/stable-diffusion-2-1 ($n$=$854$). Amongst the
developers of the 100 most used models, Stability AI ranks first, with 15 of
the 100 most used models and 22 of the top-ranked co-used models in such
spaces. These findings highlight the key models and players in the NLP, CV,
and MM communities.

####  4.3.3 Correlations between Model Likes and Model Usage

We observe a strong positive correlation between n_likes of models and
n_usage_spaces ($\rho=0.66$, $p<0.001$), and a weak positive correlation
between n_downloads and n_usage_spaces ($\rho=0.29,p<.001$). These findings
suggest that the number of likes is more strongly associated with the usage of
models in spaces compared to the number of downloads, and that likes in model
repositories are a good indicator of its adoption in applications on the HF
platform. However, as mentioned in Section 3.3, we note that download counts
are limited and therefore may only provide a snapshot of correlations between
downloads and likes or usage, which may not generalise in all time periods.

##  5 Discussion

In this section, we discuss the key implications of our findings for research
and practice. We highlight the study’s contributions to the literature in
Section 5.1.1. We then reflect on the methodological considerations of using
the HF Hub as a data source for research on open source AI in Section 5.1.2.
Building on these insights, we make five recommendations for future research
to advance the research agenda on open source AI in Section 5.1.3. Finally, we
discuss the implications for practice and make recommendations to
practitioners in Section 5.2.

###  5.1 Implications for Research

####  5.1.1 Contributions to Academic Literature

Uneven influence in the HF Hub developer community: We extend prior findings
of right-skewed distributions of commit activity in model repositories [22]
with observations of right-skewed distributions of various development
activities on the HF Hub, including interactions in model, dataset, and space
repositories; code collaborations between developers; and model usage in
spaces. Activity distributions follow power law patterns, with a small
fraction of repositories accounting for most interactions (e.g., $<1$% for 80%
of likes, 10% for 80% discussions, 30% for 80% commits, <1% for 80%
downloads). Similarly, the collaboration networks exhibit right-skewed
centrality distributions, indicating that influence is concentrated amongst
few developers, congruent with prior observations that OSS development
patterns generally follow Pareto distributions [23, 24, 25, 26, 27]. Influence
also flows across the HF Hub, with likes per model having strong correlations
with their usage in spaces ($\rho=0.66$, $p<0.001$).

Impact of licence on collaboration: The Mann-Whitney U tests show that licence
choice significantly impacts the level of activity and engagement in
repositories, with permissive licences exhibiting the highest activity levels,
followed by repositories with restrictive licences, and finally ones with no
licence. Furthermore, the Pearson correlations indicate that the use of a
licence (permissive or restrictive) is associated with stronger correlations
between various types activity compared to repositories without licences.
These findings highlight the important role of licencing decisions in
influencing the collaborative and community dynamics in open model development
and open source AI projects.

Core-periphery structure of the HF developer community: To the best of our
knowledge, only one prior study has investigated model development practices
in the HF developer community, showing that most models only have one
contributor and that model maintenance chiefly involves “perfective tasks” to
enhance model performance [22]. We extend this finding with three insights.
First, we corroborate the findings that most developers (89%) are islands, who
have not collaborated with other developers in model repositories on the HF
Hub. This is not unique to the HF Hub: the majority of OSS projects are
developed by individuals [73]. However, what may be specific about the small
community sizes in model development is the nature of the model development
life-cycle (“code once, train often”). Second, the social network structure of
collaboration patterns amongst developers in model repositories is
characterised by a core-periphery structure, with a dense core of highly
active developers, akin to the “layered onion” structure common in OSS [74].
Third, collaborations have high reciprocity and low assortativity, signifying
the prevalence of mutual relationships amongst developers, regardless of their
social positions in the community.

Uneven model adoption in spaces: By examining model adoption in spaces, we
empirically tested the observation of uneven model adoption and the
disproportionate influence of industry-leading companies in the open source AI
ecosystem [21]. We identified the popularity of a relatively small number of
models used in spaces as well as the influential role of a few organisations,
including Meta, Google, Stability AI, OpenAI, Microsoft, and EleutherAI, who
have developed the most widely used models. Some critics of the open-source
model of AI development fear that too many unknown actors will introduce
distributed safety issues, while advocates of the development model tout
democratisation of power as a core benefit. Our findings show that a few
organisations possess majority influence in this ecosystem, which challenges
both of these narratives. In many cases, the most influential actors in the
open source AI ecosystem are one in the same as those in closed-source AI
[21].

####  5.1.2 HF Hub: A New Source of Research Data

This paper contributes to the research effort to use the HF Hub as a data
source for empirical studies on open model development [67, 69, 22]. We share
two reflections on methodological considerations. First, informed by prior
work that underlines the importance of merging usernames for unique
developers, we anticipated that this might be an issue on the HF Hub [75, 122,
123, 119, 27]. While our three-pronged approach strikes a balance between the
impracticality of manually inspecting over 100,000 developers versus the risk
of misclassification through a fully automated approach, it is still
imperfect. Future research may consider more sophisticated approaches to this
problem. Second, the API is not optimised for research purposes, which makes
data collection time-consuming (e.g., one must make a unique API call to
retrieve commit histories of each model and handle rate limits) and limited
(e.g., user metadata is not available). The lack of user metadata hinders the
ability to study the characteristics and behaviours of individual developers,
such as their expertise and affiliations, as well as automated approaches to
username merging that incorporate user metadata. To overcome these
limitations, researchers may explore alternative approaches and tools, such as
the HFCOMMUNITY database developed by Ait et al. to facilitate empirical
studies of activity on the platform [69].

####  5.1.3 Recommendations for Future Research

We recommend five research directions that can advance the research agenda on
open source AI.

1\. Implications of concentrations in the HF Hub developer community: Our
analysis confirmed prior observations that the models of a handful of
industry-leading companies are dominant amongst the HF Hub developer community
[21]. We encourage future research to investigate what these concentrations
mean in practice. This includes examining the potential benefits that
developers of these popular models may accrue from the model ecosystems, such
as increased visibility, crowdsourced contributions (e.g., via commits and
discussions), and access to diverse fine-tuned versions shared by other
developers on the HF Hub. Furthermore, there is a concern that dominant
companies may benefit from developers being locked-in to their ecosystems,
potentially limiting competition and entrenching already dominant actors.
Future research should investigate the factors contributing to this
concentration, such as the reputation of the organisations developing the
models, their access to resources and support, or the perceived performance
and versatility of their models. Additionally, it would be valuable to examine
the broader implications of these concentrations for the AI community,
including the impact on research, innovation, and the distribution of benefits
and resources.

2\. Collaboration in repositories with large communities: Extending our
analysis of collaboration across all model repositories on the HF Hub, future
research could investigate the social network structure of collaboration in
the repositories of models and datasets that have large communities, providing
insights into the social practices and processes involved in such
collaborations. Furthermore, future research may also compare collaboration
practices with regards discussions and commits, building on our observation of
differences in the count of unique developers that participate in discussions
versus those who make commits (e.g., model repositories have up to 240
discussion starters but only up to 18 committers). Future analyses should also
take into account temporal dynamics in this fast-moving field. For example, a
temporal analysis of collaboration in the open source AI ecosystem can provide
insights into the evolving social structure, trends, and dynamics of open
model ecosystems and their communities. In turn, these insights can inform
strategies aimed at fostering sustainable growth, innovation, and governance
in said ecosystem.

3\. Incentives and modes of participation: Future research could investigate
the incentives of individual developers and companies. A number of companies
have released open models on the HF Hub, such as Meta’s LlaMA models [125],
Mistral AI’s Mixtral models [126], and OpenAI’s Whisper models [127]. Often
these releases are presented as acts of “AI democratisation” [128]. Future
research could critically examine the commercial incentives behind these
releases. In addition, future research could examine commercial approaches to
model governance and maintenance—for example, if and how companies welcome or
engage with community contributions—and if and how companies collaborate with
each other on open model development, as they do in OSS development [108, 109,
106, 110, 111, 98].

4\. Impact of model size on collaboration Future research should examine the
impact of model size (i.e., parameters) on the nature of collaboration in
repositories on the HF Hub. For instance, it could examine how resource
constraints (e.g., computational power or data availability) influence
collaboration for various stakeholders (e.g., individual developers or
developers from industry labs) on models of different sizes. By shedding light
on facilitators and barriers for collaboration on open models, such research
could guide efforts to foster inclusive and diverse communities.

5\. Collaboration beyond the HF Hub: While this analysis provides insights
into the community that shares and fine-tunes open models on the HF Hub, we
still have a limited understanding of the development of open models that
largely takes place in proprietary settings or in the case of collaborative
initiatives on other platforms, such as GitHub [15]. Therefore, future
research should examine how the HF Hub is used in the wider ecosystem of
online platforms as well as offline venues for the collaborative development
of open models and datasets. This research direction would enable comparisons
of the collaboration patterns amongst model developers and model fine-tuners.
Future research could also undertake a multi-sited analysis, examining
collaboration on the same project across platforms.

###  5.2 Implications for Practice

####  5.2.1 Recommendations for Policymakers

As open models become increasingly widely available and used, policymakers
need empirical data to inform discussions about the benefits, risks, and
governance of these models. Our analysis provides one empirical lens on the
extent of model proliferation and adoption, which can help ground policy
decisions. For example, it is illuminating to observe that most models
(70.99%) have not been downloaded once or that 1% of models account for 99% of
downloads. This is a reminder that the availability of a model does not mean
it will be (widely) used. Furthermore, while download counts were limited to
the past 30 days, the fact that only 86 models had over one million downloads
indicates that the number of widely used models is not excessively large and
governable. What is more, the analysis revealed the impact of models developed
by a number of non-profit, grassroots initiatives like EleutherAI, BigScience,
and BigCode. Following the charge of the French government to fund the digital
commons to support open source AI development [57], policymakers may use such
data to identify non-commercial projects that could be supported. Overall, the
data points reported in Section 4.1 could help policymakers assess the real-
world impact of open models and develop appropriate governance frameworks to
maximise their benefits while mitigating potential risks of open source AI.

####  5.2.2 Recommendations for Platform Providers

We make two recommendations to HF as a platform for open model development.
First, HF could work with researchers to identify features and API
improvements that would aid research efforts concerning open model development
on its platform, building on efforts by members of the HF community, such as
Weyaxi/huggingface-leaderboard. This collaboration could include collecting
and publishing data on open model development patterns and collaboration,
which would help fill the current “data gap” in this area. HF may take
inspiration from GitHub’s Innovation Graph [129] or its annual Octoverse
reports [130], which provide access to data and insights on development
activity on its platform. Second, a concerning proportion of models (64.67%)
and datasets (72.13%) lack licences, which may be due to uncertainty about how
or whether they should be licenced [131, 65]. For comparison, the number of
unlicenced repositories on GitHub is lower at 46% or 53% if including “other
licences” [132]. In the interest of promoting responsible development, HF
should consider developing educational resources on licences, such as guides
or tutorial videos, or developing features, such as a licence drop-down menu,
which can inform developers of the options available as well as their merits
and drawbacks. Such a feature could be considered amongst other
recommendations to moderate models on the HF Hub, such as hiring AI safety
researchers and proactively red-teaming unsafe models [52, 68].

###  5.3 Threats to Validity

We evaluate the validity of our findings by following guidance for empirical
software engineering research [112, 133].

####  5.3.1 Construct validity

Construct validity concerns the extent to which a measurement accurately
assesses the theoretical construct it intends to measure. Our study aimed to
measure typical patterns of development activity on the HF Hub, but we
acknowledge several threats to construct validity. First, our analysis is
limited to activity in public repositories and does not account for
collaboration in private repositories. Second, download counts have a few
limitations: they are limited to the past 30 days, download counts may be
incorrectly reported (e.g., if the repository lacks a configuration file or if
the model is used on-device versus in continuous integration), and dataset
downloads are limited to the count of load_dataset() calls [113, 114]. Third,
our operationalisation of collaboration relies on commits to model
repositories, assuming that the co-occurrence of commits indicates
collaboration. However, this assumption may not always hold true, especially
in large repositories where developers may work on independent tasks. Future
research could operationalise collaboration on specific files and quantify the
relative contribution of developers to specific files [78]. Furthermore, this
analysis is limited to snapshot of the HF Hub developer community in October
2023, which does not capture the dynamics of collaboration and activity over
time, which should be considered in future research, as discussed in Section
5.1.3.

####  5.3.2 Internal validity

Internal validity concerns the extent to which a study can confidently
attribute the observed results to the investigated variables, minimising the
influence of confounding factors or alternative explanations. As explained in
section 3.4, there may be a slight inaccuracy in the enumeration of community
size per repository and the number of developers included in the collaboration
networks due to discrepancies in username data, such as multiple accounts or
usernames per developer. This is a common problem in OSS research, and there
is no perfect solution to username merging [75, 123, 119]. API limitations
prevent the use of methods that incorporate user metadata for username merging
[134, 27]. For example, we rejected 34 username pairs due to insufficient
evidence to confirm the match with confidence.

####  5.3.3 External validity

External validity concerns the generalisability of the findings. While the HF
Hub has gained significant popularity, it is important to acknowledge that
there may be other platforms where open model development takes place and that
our findings may not generalise to those platforms. Future research could
explore collaboration practices across different platforms to provide a more
comprehensive view of the open source AI ecosystem. That being said, we
observe that development activity on the HF Hub is characterised by the Pareto
principle, conforming with OSS development patterns on platforms like GitHub
[23, 24, 25, 26, 27]. Another threat to the external validity of the findings
concerns the analysis of model usage. While there were as many as 156,642
spaces at the time of data collection, they do not represent the use of open
models beyond the HF Hub platform, thus limiting the generalisability of our
claims, with the exception of finding a strong positive correlation between
likes of model repositories and their usage in spaces ($\rho=0.66$,
$p<0.001$). Future research could address this limitation by exploring other
sources of data on model adoption, such as academic publications, industry
reports, or user surveys, to triangulate the findings.

####  5.3.4 Reliability

Reliability refers to the consistency and reproducibility of the study’s
results. To enhance the reliability of our study, we have uploaded the Python
scripts used for data collection and processing to a public GitHub repository
[117]. Due to privacy and ethical considerations, we do not share the raw data
(see Data Availability statement).

##  6 Conclusion

The burgeoning open source AI ecosystem has become a focal point of discussion
amongst AI researchers, developers, and policymakers. This study offers
empirical insights on practices in this emerging ecosystem via a quantitative
analysis of development activity on the HF Hub. Concretely, we make three
empirical contributions to the nascent research agenda on open source AI.
First, we find that various types of development activity, from likes and
downloads to discussions and commits, across 348,181 model, 65,761 dataset,
and 156,642 space repositories exhibit right-skewed distributions. In
addition, activity and engagement is highly imbalanced between repositories;
for example, over 70% of models have 0 downloads and 1% account for 99% of
downloads. Second, we analyse a snapshot of the social network structure of
collaboration in model repositories, finding that the community has a core-
periphery structure, with a core of highly prolific developers and a majority
of isolate developers (89%) who do not collaborate with others. However,
collaboration is characterised by high reciprocity and low levels of
assortativity regardless of developers’ social positions in the HF developer
community. Third, we examine model adoption through the lens of model usage in
spaces, finding that a minority of models are widely used and developed by a
handful of industry-leading companies, which signifies the concentrated
influence of a handful of actors in the HF Hub ecosystem. These findings are a
timely reminder that open source AI is not immune to the influence of dominant
industry leaders [21]. We conclude with a discussion of the implications of
our findings and recommendations for (open source) AI researchers,
practitioners, and policymakers, with the hope that the practices in open
model development can be more deeply investigated in the future.

## Acknowledgements

The authors would like to thank Loubna Ben Allal, Daniel van Strien, Peter
Cihon, Mer Joyce, Stefano Maffulli, Matt White, Seb Elmes, David Gray Widder,
and Alek Tarkowski as well as the peer-reviewers at the Journal of
Computational Social Science for their generous feedback on prior versions of
this manuscript.

## Funding

Cailean Osborne was supported by the Economic and Social Research Council
Grant for Digital Social Science [ES/P000649/1]; Hannah Rose Kirk was
supported by the Economic and Social Research Council Grant for Digital Social
Science [ES/P000649/1]. Jennifer Ding was supported by the Ecosystem
Leadership Award under the Engineering and Physical Sciences Research Council
Grant [EP/X03870X/1] & the Alan Turing Institute.

## Data Availability Statement

The authors may be contacted with enquiries about the research data, which may
be shared upon reasonable request and only in compliance with applicable data
protection regulations.

## Conflict of Interests

On behalf of all authors, the corresponding author states that there is no
conflict of interest.

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##  Appendix A Definition of Network Properties for Social Network Analysis

Table 1: Definition of Network Properties Property |  Definition
---|---
Nodes |  Number of nodes in the network (e.g., developers or models).
Edges |  Number of edges in the network (e.g., links between developers).
Degree centrality |  Degree centrality is a measure of the importance of a node in a network based on the number of connections it has. It is calculated as the number of edges a node has with other nodes.
PageRank centrality |  PageRank is a network centrality measure that assesses a node’s importance based on the quantity and quality of incoming links, considering the recursive influence of nodes pointing to it [135]. PageRank values range from 0 to 1, with higher values indicating greater global influence and importance in the network.
k-core |  k-core decomposition identifies the maximal subgraph in which every node is connected to at least k other nodes, helping to reveal the network’s core structure [136].
Modularity (MOD) |  Modularity measures the extent to which a network can be divided into distinct and densely interconnected communities. The modularity value ranges from -1 to 1, with positive values indicating a high degree of community structure and negative values implying the absence of community structure [137]. We used the Clauset-Newman-Moore greedy modularity maximization algorithm to calculate the modularity of networks [138].
Communities (COM) |  Community detection identifies cohesive groups in a network by optimizing a measure of modular structure. The goal is to find a partition of the network that maximizes the density of connections in communities while minimizing the connections between them. We used the Clauset-Newman-Moore greedy modularity maximization algorithm [138] to find the community partition with the highest modularity value and to determine the number of communities in the network (COM).
Density (DENS) |  Density is a measure of how connected a network is, calculated as the ratio of the number of edges present in the network to the maximum possible number of edges in the network [139].
Reciprocity (RECIP) |  The reciprocity of a directed graph is the ratio of the number of edges pointing in both directions to the total number of edges in the graph.[140]. A value of 1 means that all edges are bidirectional, while a value of 0 means there are no mutual connections.
Average Rich Club Coefficient (ARCC) |  The average rich club coefficient measures the extent to which high-degree nodes tend to be more connected than expected by chance [141]. Detecting the rich-club phenomenon reveals high-level semantic insights about the network [142], a key property of power-law networks [141]. The coefficient ranges from 0 to 1, where 0 indicates no preferential connection and 1 indicates a fully connected high-degree subgraph. We implemented this calculation on directed networks [143]. For each network, we set the threshold k as the degree value corresponding to the minimum degree amongst the top 10% of highest-degree nodes.
Assortativity (ASS) |  Assortativity measures the tendency of nodes to be connected to nodes with similar degrees, with values ranging from [-1,1] [144].
Average Degree (AD) |  The average number of edges per node.
Average Clustering Coefficient (ACC) |  The average clustering coefficient [0, 1] measures the extent to which nodes in a network tend to cluster together. It quantifies the level of triadic closure in the network, which is the tendency for a node’s neighbours to be connected to each other [145].

##  Appendix B Summary Statistics of Development Activity in Repositories

Table 2: Summary Statistics of Development Activity in Model Repositories

| n_likes | n_discussions | n_disc_starters | n_commits | n_committers | n_community | n_downloads
---|---|---|---|---|---|---|---
mean | 1.14 | 0.28 | 0.10 | 7.28 | 1.06 | 1.13 | 1,693.93
std | 30.56 | 5.42 | 1.17 | 167.14 | 0.41 | 1.26 | 158,207.09
min | 0 | 0 | 0 | 0 | 0 | 0 | 0
25% | 0 | 0 | 0 | 2 | 1 | 1 | 0
50% | 0 | 0 | 0 | 3 | 1 | 1 | 0
75% | 0 | 0 | 0 | 5 | 1 | 1 | 0
max | 9,314 | 3,006 | 240 | 75,653 | 18 | 246 | 65,729,394

N.B. n_community equals 0 if no user, apart from the repository creator, has
made a commit or started a discussion in the repository.

Table 3: Summary Statistics of Development Activity in Dataset Repositories

| n_likes | n_discussions | n_disc_starters | n_commits | n_committers | n_community | n_downloads
---|---|---|---|---|---|---|---
mean | 0.92 | 1.41 | 0.12 | 30.04 | 1.13 | 1.20 | 476.70
std | 18.14 | 309.14 | 0.61 | 1298.11 | 0.67 | 0.87 | 43786.43
min | 0 | 0 | 0 | 0 | 0 | 0 | 0
25% | 0 | 0 | 0 | 2 | 1 | 1 | 0
50% | 0 | 0 | 0 | 4 | 1 | 1 | 0
75% | 0 | 0 | 0 | 7 | 1 | 1 | 2
max | 3493 | 79,256 | 64 | 314,813 | 100 | 110 | 9,651,261

N.B. n_community equals 0 if no user, apart from the repository creator, has
made a commit or started a discussion in the repository.

Table 4: Summary Statistics of Development Activity in Space Repositories

| n_likes | n_discussions | n_disc_starters | n_commits | n_committers | n_community | n_downloads
---|---|---|---|---|---|---|---
mean | 1.33 | 0.34 | 0.15 | 8.22 | 1.27 | 1.40 | N/A
std | 37.35 | 54.58 | 15.36 | 40.36 | 0.99 | 15.39 | N/A
min | 0 | 0 | 0 | 0 | 0 | 0 | N/A
25% | 0 | 0 | 0 | 1 | 1 | 1 | N/A
50% | 0 | 0 | 0 | 3 | 1 | 1 | N/A
75% | 0 | 0 | 0 | 5 | 1 | 2 | N/A
max | 9,124 | 18,061 | 4,684 | 2,150 | 282 | 4,685 | N/A

N.B. n_community equals 0 if no user, apart from the repository creator, has
made a commit or started a discussion in the repository.

##  Appendix C Mann-Whitney U Tests for Activity in Model Repositories

Table 5: Mann-Whitney U Test Results Activity | Permissive vs. | Permissive vs. | Restrictive vs.
---|---|---|---
| Restrictive | No licence | No licence
n_likes | 1,464,356,547 | 8,712,552,137 | 5,558,887,829
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_disc_starters | 1,501,884,124 | 8,323,015,321 | 5,200,616,824
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_discussions | 1,727,651,315 | 9,180,111,459 | 4,983,923,782
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_committers | 1,573,347,885 | 8,744,559,271 | 5,238,059,429
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_commits | 2,013,736,368 | 11,333,305,055 | 5,451,995,655
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_downloads | 1,884,325,690 | 10,136,574,180 | 4,983,179,068
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_community | 1,563,484,573 | 8,856,297,978 | 5,333,406,861
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)
n_usage_spaces | 1,509,785,617 | 8,276,280,946 | 5,148,816,091
| ($p<0.001$) | ($p<0.001$) | ($p<0.001$)

##  Appendix D Social Network Structure of Collaboration on HF Hub

Table 6: Network Structure of Collaboration in All Model Repositories

k-core | Nodes | Edges | MOD | COM | DENS | RECIP | ARCC | ASS | AD | ACC
---|---|---|---|---|---|---|---|---|---|---
1 | 10,524 | 21,598 | 0.81 | 2,894 | 0.00 | 0.83 | 0.04 | 0.08 | 4.10 | 0.00
5 | 1,109 | 7,849 | 0.60 | 68 | 0.01 | 0.81 | 0.08 | 0.01 | 14.16 | 0.00
10 | 330 | 3,769 | 0.52 | 18 | 0.03 | 0.85 | 0.11 | 0.07 | 22.84 | 0.00
26 (MAX) | 14 | 182 | 0.00 | 1 | 1.00 | 1.00 | 0.15 | NaN | 26.00 | 0.04

Table 7: Network Structure of Collaboration in Computer Vision Model
Repositories

k-core | Nodes | Edges | MOD | COM | DENS | RECIP | ARCC | ASS | AD | ACC
---|---|---|---|---|---|---|---|---|---|---
1 | 371 | 697 | 0.80 | 112 | 0.01 | 0.90 | 0.26 | 0.26 | 3.76 | 0.01
5 | 41 | 203 | 0.52 | 4 | 0.12 | 0.93 | 0.67 | -0.00 | 9.90 | 0.04
10 (MAX) | 6 | 30 | 0.00 | 1 | 1.00 | 1.00 | 0.40 | NaN | 10.00 | 0.16

Table 8: Network Structure of Collaboration in Natural Language Processing
Model Repositories

k-core | Nodes | Edges | MOD | COM | DENS | RECIP | ARCC | ASS | AD | ACC
---|---|---|---|---|---|---|---|---|---|---
1 | 4,606 | 10,010 | 0.82 | 1,366 | 0.00 | 0.93 | 0.07 | 0.33 | 4.35 | 0.00
5 | 565 | 3,980 | 0.62 | 41 | 0.01 | 0.91 | 0.11 | 0.14 | 14.09 | 0.00
10 | 182 | 1,980 | 0.52 | 13 | 0.06 | 0.90 | 0.15 | 0.07 | 21.76 | 0.00
25 (MAX) | 15 | 204 | 0.00 | 1 | 0.97 | 0.98 | 0.25 | NaN | 27.20 | 0.01

Table 9: Network Structure of Collaboration in Multimodal Model Repositories

k-core | Nodes | Edges | MOD | COM | DENS | RECIP | ARCC | ASS | AD | ACC
---|---|---|---|---|---|---|---|---|---|---
1 | 1,546 | 3,661 | 0.71 | 321 | 0.00 | 0.84 | 0.10 | 0.19 | 4.74 | 0.00
5 | 218 | 1,480 | 0.57 | 18 | 0.03 | 0.89 | 0.16 | -0.00 | 13.58 | 0.00
10 | 82 | 805 | 0.50 | 6 | 0.12 | 0.91 | 0.37 | -0.33 | 19.63 | 0.02
26 (MAX) | 14 | 182 | 0.00 | 1 | 1.00 | 1.00 | 0.15 | NaN | 26.00 | 0.06