hyperparameter tuning
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Visual UI for Fine-Tuning LLMs on Apple Silicon
Read Full Article: Visual UI for Fine-Tuning LLMs on Apple Silicon![[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.
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Simple ML Digit Classifier in Vanilla Python
Read Full Article: Simple ML Digit Classifier in Vanilla Python
A simple digit classifier has been developed as a toy project using vanilla Python, without relying on libraries like PyTorch. This project aims to provide a basic understanding of how a neural network functions. It includes a command line interface for training and predicting, allowing users to specify the number of training loops, or epochs, to observe the model's predictions over time. This matters because it offers an accessible way to learn the fundamentals of neural networks and machine learning through hands-on experience with basic Python coding.
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Dynamic Learning Rate Scheduling
Read Full Article: Dynamic Learning Rate Scheduling
Training a machine learning model often requires adjusting the learning rate as the process progresses. Initially, a larger learning rate is beneficial for rapid progress, but as the model nears optimal performance, a smaller learning rate is necessary for fine-tuning and precise adjustments. Without adapting the learning rate, the model may overshoot the optimal point, causing oscillations and preventing further improvement. Implementing a learning rate schedule can significantly enhance model performance, potentially increasing accuracy from 85 percent to 95 percent with the same model and data. This matters because it can lead to more efficient training and better-performing models in machine learning applications.