# Is Open Source the Future of AI? A Data-Driven Approach
## 2 Literature Review  
Due to its continuous growth, Hugging Face has emerged as the leading platform for sharing machine learning (ML) and artificial intelligence (AI) models, resulting in increasing levels of complexity. A relational database called HFCommunity was established to facilitate the analysis and resolution of this issue [5].  
[PERSON] et al. [6] conducted research with a similar goal, focusing on temporal changes on Hugging Face. They examined various trends, including fluctuations in user count, models, commits, and overall platform activities. Additionally, they analysed model maintenance, and categorised them into two groups. They used all Hugging Face data accessible via the HF Hub, in addition to the HFCommunity database established by [5]. However, their study overlooked the influence of Hugging Face on the AI community.  
The article by [PERSON] et al. [1] highlights the significance of the open-source AI community and explains its rapid growth in the wake of major industry leaders like Google, Microsoft, and OpenAI. A significant milestone in this area was the release of the Llama model, and the open-source community promptly recognised the possibilities and potential involved in this release.  
Fine-tuning has become a critical strategy for adapting large language models (LLMs) to specific tasks while leveraging the extensive knowledge embedded in pre-trained models. Numerous studies on open-source language models have proposed a parameter-efficient approach to fine-tuning, which greatly reduces the computational resources required when adapting the model to a specific task, making fine-tuning more accessible to larger number of people. Fine-tuning is especially prominent in the Hugging Face ecosystem, where models can be adapted for diverse applications with minimal overhead [7].  
In addition to fine-tuning, merging models has gained attention as a technique for integrating the capabilities of multiple models for broader applicability. [PERSON] et al. [8] systematically explored strategies for effectively merging large language models, highlighting the potential for combining complementary models to address complex tasks. Hugging Face serves as a central hub for this practice, providing tools to combine checkpoints and enable cross-model functionality.  
The article [9] examines the security risks associated with open-source AI. A much higher number of repositories with high vulnerabilities were found compared to those with low vulnerabilities, particularly in root repositories. This emphasises the importance of ensuring the security of the technology in order to facilitate its utilisation.  
In a recent paper [10], the authors analysed the transparency of Hugging Face's pre-trained models regarding database usage and licenses. The analysis revealed that there is often a lack of transparency regarding the training datasets, inherent biases, and licensing details in pre-trained models. Additionally, this research identified numerous potential licensing conflicts involving client projects. Of the 159,132 models analysed, merely 14 percent of these models clearly identified their datasets with specific tags. A detailed examination of a statistically significant sample comprising 389 of the most frequently downloaded models showed that 61 percent documented their training data in some form.