Streamlit is a popular framework for creating data applications with ease, and its capabilities are being explored through a project involving small Vision Transformers (VITs). The project involves performing a grid search on custom-built VITs to identify the most effective configuration for real-time digit classification. By leveraging Streamlit, the project not only facilitates the classification process but also provides a platform to visualize attention maps, which are crucial for understanding how the model focuses on different parts of the input data. The use of VITs in this context is significant as they represent a modern approach to handling image data, often outperforming traditional convolutional neural networks in various tasks. The project demonstrates how VITs can be effectively implemented from scratch and highlights the flexibility of Streamlit in deploying machine learning models. This exploration serves as a practical example for those looking to understand the integration of advanced machine learning techniques with user-friendly application frameworks. Sharing the code and application through platforms like GitHub and Streamlit allows others to replicate and learn from the project, fostering a collaborative learning environment. This is particularly useful for individuals new to Streamlit or those interested in experimenting with VITs, providing them with a tangible example to build upon. The project not only showcases the potential of Streamlit in machine learning applications but also encourages others to explore and innovate within the field. This matters because it highlights the accessibility and power of modern tools in democratizing machine learning development.
![[Project] I built a complete ui for Fine-Tuning LLMs on Mac (MLX) – No more CLI arguments! (Open Source and Non-profit)](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9427-300x171.png)
A new visual UI has been developed for fine-tuning large language models (LLMs) on Apple Silicon, eliminating the need for complex command-line interface (CLI) arguments. This tool, built using Streamlit, allows users to visually configure model parameters, prepare training data, and monitor training progress in real-time. It supports models like Mistral and Qwen, integrates with OpenRouter for data preparation, and provides sliders for hyperparameter tuning. Additionally, users can test their models in a chat interface and easily upload them to HuggingFace. This matters because it simplifies the fine-tuning process, making it more accessible and user-friendly for those working with machine learning on Apple devices.![[Project] Emergent Attractor Framework – now a Streamlit app for alignment & entropy research](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8289-300x171.png)
The Emergent Attractor Framework, now available as a Streamlit app, offers a novel approach to alignment and entropy research. This tool allows users to engage with complex concepts through an interactive platform, facilitating a deeper understanding of how systems self-organize and reach equilibrium states. By providing a space for community interaction, the app encourages collaborative exploration and discussion, making it a valuable resource for researchers and enthusiasts alike. This matters because it democratizes access to advanced research tools, fostering innovation and collaboration in the study of dynamic systems.