# Is Open Source the Future of AI? A Data-Driven Approach
## 4 Results
All architectures that contributed to a minimum of 5 percent of models were picked, and the remainder were categorised as 'other'. Our analysis also considered significant model releases from the respective companies, as the introduction of new models can significantly impact the popularity of their architectures. This is particularly true for the most prominent architectures such as Llama and Mistral as shown in Figure 6. The leaderboards include over 300 models that specify Mistral as the base model and over 200 that specify Llama3. Interestingly, when there is a new release from a popular source, a large portion of the experimental focus switches to the newly released model. However, not all releases trigger the same shift. The popularity of the source seems to play a large part in determining which model families tend to shift the experimentation in their favor. The Llama and Mistral models produced the largest shifts in popularity, while model families like Gemma and Phi received comparatively minor attention. Figure 4: This figure presents the weekly production of new models, categorized by type, from January 2023 to May 2024. The upper panel displays the absolute number of models released each week, with the y-axis labeled ‘Week’ representing the count of models. The lower panel shows the proportional distribution of these model types as a stacked area chart, with the y-axis labeled ‘Percentage’ representing the proportion of each type. The x-axis in both panels represents the ‘Date’, showing the progression of weeks. Bars are color-coded based on type. The stacked area chart in the lower panel illustrates the changing composition of model releases over time, highlighting the relative contributions of each type. Table 5 presents the distribution of model licenses in the open-source ecosystem, showing the number of models under each license and their respective percentage of the total dataset of models on the leaderboard. The table also categorizes the models based on their licensing permissions for commercial, academic, and personal use. Apache 2.0 is the most prevalent license, covering 39% of the models, allowing for full commercial and  
Figure 5: This figure illustrates the weekly production of new models from January 2023 to May 2024, categorized by their underlying architecture. The x-axis represents the ’Date’, showing the progression of weeks. The y-axis, labeled ’Total models per date’, indicates the cumulative number of models released each week. The stacked bars represent the different model architectures. Specific model releases are annotated above the bars, providing context for significant architectural contributions. The stacked bar format allows for the visualization of the composition of model releases by architecture over time, highlighting the relative contributions of each architecture to the total weekly production. Figure 6: This figure presents the total count of distinct models derived from each specified base model. The x-axis, labeled ’Model’, lists the base models considered, including various ’llama’, ’mistral’, ’mistral’, and ’phi’ models. The y-axis, labeled ’Total number of models’, represents the cumulative count of models generated from each base model. The data are displayed as a bar chart, with the height of each bar indicating the number of derived models for the corresponding base model. academic use without restrictions. A significant portion (7.87%) falls under CC-BY-NC-4.0, which restricts commercial use while permitting academic and personal applications. Additionally, MIT and Llama model licenses also represent a sizable fraction, indicating a preference for permissive licensing within the community. Notably, certain models such as Llama2, Llama3, and Gemma have limited commercial use, requiring explicit permissions or compliance with specific conditions. A substantial portion of models (35.4%) are categorized under \"Unknown License\". These models either do not specify their licensing terms or lack clearly defined usage permissions on the platform.