Hugging Face

Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, Y. Zhuang

Solving complicated AI tasks with different domains and modalities is a key step toward artificial general intelligence. While there are numerous AI models available for various domains and modalities, they cannot handle complicated AI tasks autonomously. Considering large language models (LLMs) have exhibited exceptional abilities in language understanding, generation, interaction, and reasoning, we advocate that LLMs could act as a controller to manage existing AI models to solve complicated AI tasks, with language serving as a generic interface to empower this. Based on this philosophy, we present HuggingGPT, an LLM-powered agent that leverages LLMs (e.g., ChatGPT) to connect various AI models in machine learning communities (e.g., Hugging Face) to solve AI tasks. Specifically, we use ChatGPT to conduct task planning when receiving a user request, select models according to their function descriptions available in Hugging Face, execute each subtask with the selected AI model, and summarize the response according to the execution results. By leveraging the strong language capability of ChatGPT and abundant AI models in Hugging Face, HuggingGPT can tackle a wide range of sophisticated AI tasks spanning different modalities and domains and achieve impressive results in language, vision, speech, and other challenging tasks, which paves a new way towards the realization of artificial general intelligence.

Jia Li, E. Beeching, Lewis Tunstall, Ben Lipkin, Roman Soletskyi, Shengyi Huang, K. Rasul, Long Yu, Albert Qiaochu Jiang, Ziju Shen, Zihan Qin, Bin Dong, Li Zhou, Y. Fleureau, Guillaume Lample, Stanislas Polu, Hugging Face, AI Mistral

Cailean Osborne, Jennifer Ding, Hannah Rose Kirk

Open model developers have emerged as key actors in the political economy of artificial intelligence (AI), but we still have a limited understanding of collaborative practices in the open AI ecosystem. 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, 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. Furthermore, licenses matter: there are statistically significant differences in collaboration patterns in model repositories with permissive, restrictive, and no licenses. 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 prolific developers and a majority of isolate developers (89%). Upon removing these isolates from the network, 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, the findings show that various types of activity across the HF Hub are characterised by Pareto distributions, congruent with open source software development patterns on platforms like GitHub. We conclude with recommendations for researchers, and practitioners to advance our understanding of open AI development.

Federica Pepe, Vittoria Nardone, A. Mastropaolo, G. Bavota, G. Canfora, Massimiliano Di Penta

Pre-trained Machine Learning (ML) models help to create ML-intensive systems without having to spend conspicuous resources on traimng a new model from the ground up. However, the lack of transparency for such models could lead to undesired consequences in terms of bias, fairness, trustworthiness of the underlying data, and, potentially even legal implications. Taking as a case study the transformer models hosted by Hugging Face, a popular hub for pre-trained ML models, this paper empirically investigates the transparency of pre-trained transformer models. We look at the extent to which model descriptions (i) specify the datasets being used for their pre-training, (ii) discuss their possible training bias, (iii) declare their license, and whether projects using such models take these licenses into account. Results indicate that pre-trained models still have a limited exposure of their traimng datasets, possible biases, and adopted licenses. Also, we found several cases of possible licensing violations by client projects. Our findings motivate further research to improve the transparency of ML models, which may result in the definition, generation, and adoption of Artificial Intelligence Bills of Materials. CCS CONCEPTS • Software and its engineering $\rightarrow$ Software libraries and repositories.

Xinyu Yang, Weixin Liang, James Zou

Advances in machine learning are closely tied to the creation of datasets. While data documentation is widely recognized as essential to the reliability, reproducibility, and transparency of ML, we lack a systematic empirical understanding of current dataset documentation practices. To shed light on this question, here we take Hugging Face -- one of the largest platforms for sharing and collaborating on ML models and datasets -- as a prominent case study. By analyzing all 7,433 dataset documentation on Hugging Face, our investigation provides an overview of the Hugging Face dataset ecosystem and insights into dataset documentation practices, yielding 5 main findings: (1) The dataset card completion rate shows marked heterogeneity correlated with dataset popularity. (2) A granular examination of each section within the dataset card reveals that the practitioners seem to prioritize Dataset Description and Dataset Structure sections, while the Considerations for Using the Data section receives the lowest proportion of content. (3) By analyzing the subsections within each section and utilizing topic modeling to identify key topics, we uncover what is discussed in each section, and underscore significant themes encompassing both technical and social impacts, as well as limitations within the Considerations for Using the Data section. (4) Our findings also highlight the need for improved accessibility and reproducibility of datasets in the Usage sections. (5) In addition, our human annotation evaluation emphasizes the pivotal role of comprehensive dataset content in shaping individuals' perceptions of a dataset card's overall quality. Overall, our study offers a unique perspective on analyzing dataset documentation through large-scale data science analysis and underlines the need for more thorough dataset documentation in machine learning research.