# Is Open Source the Future of AI? A Data-Driven Approach
## 4 Results
The x-axis represents the timeline from January 2019 to June 2024, with monthly intervals. The y-axis indicates the number of authors. The orange line depicts the ‘Total Authors’, representing the cumulative number of contributors. The green line shows ‘New Authors’, indicating the number of authors making their first contribution within each month. Figure 2: This figure displays the distribution of the number of repositories (models) contributed by each author. The x-axis represents the number of repositories, ranging from 0 to 100. The y-axis, labeled ‘Count’, indicates the number of authors who have contributed the corresponding number of repositories. Despite the significant increase in contributors, their collaboration is at a sub-optimal level. As shown in Figure 3, a large number of repositories are created and maintained by a single profile. The distribution is highly skewed toward individual contributions, suggesting a lack of coordinated efforts that include several contributors. This pattern represents the challenges faced by collaborative networks within this community, primarily due to technological difficulties. Additionally, it is common for models to be published by smaller research teams that are frequently attributed to a single individual or profile. Even when models are the product of team effort, these teams are often closed and do not foster open collaboration, which limits the flow of ideas and expertise within the community. While publishing models on platforms like Hugging Face facilitate some level of collaboration, it often involves individuals or teams working independently rather than collectively coordinating efforts. Based on the findings, it would be beneficial to concentrate on promoting more integrated and collaborative approaches to enhance the potential and expand the boundaries of the open-source community. We were particularly interested in the types of models represented on the leaderboard. Hugging Face categorises models into five types: pre-trained, continually pre-trained, fine-tuned on domain-specific datasets, chat models, and base merges and merges. Figure 4 highlights trends in model types over time. The prevailing pattern indicates a constant yet uneven increase in the number of newly released models. It is evident that there have been no instances of unknown types in recent months. This suggests that the open-source community is increasingly adopting a more rigorous and professional approach. The fine-tuned models and chat models seem to represent a larger proportion of the models. However, it is important to note that the dataset represents models submitted for the leaderboard benchmark testing and not the whole Hugging Face ecosystem. This suggests that the sample includes models for which the authors believed they could compete with the best-performing models on the platform. Therefore, it can be reasonably assumed that the models in the sample are competitive in terms of performance. Mergers, a relatively recent trend appears to be growing in popularity. This demonstrates a need within the community for more sophisticated and proficient models. As stated in the literature review, combining models can yield impressive results since each model specialises in specific tasks. The rationale behind merging these models is to create more comprehensive models. Figure 3: This figure presents the distribution of the number of authors contributing to each repository. The x-axis represents the number of authors, ranging from 0 to 20. The y-axis, labeled ‘Count’, indicates the number of repositories that have the corresponding number of authors. The data are displayed as a histogram, illustrating the frequency of repositories based on the number of authors contributing to them. In addition to analysing model types, we examined different model architectures. Figure 5 illustrates the weekly distribution of each important model architecture. Nine architectures were selected based on the frequency of occurrences: Gemma, GPT-2, Mistral, Opt, Phi, GPT-NeoX, Llama, Mistral, and Qwen2.