Apple Silicon
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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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Unsloth-MLX: Fine-tune LLMs on Mac
Read Full Article: Unsloth-MLX: Fine-tune LLMs on Mac
Unsloth-MLX is a new library designed for Mac users in the machine learning space, allowing for the fine-tuning of large language models (LLMs) on Apple Silicon. This tool enables users to prototype LLM fine-tuning locally on their Macs, leveraging the device's unified memory, and then seamlessly transition to cloud GPUs using the original Unsloth without any API changes. This approach helps mitigate the high costs associated with cloud GPU usage during experimentation, offering a cost-effective solution for local development before scaling up. Feedback and contributions are encouraged to refine and expand the tool's capabilities. This matters because it provides a cost-efficient way for developers to experiment with machine learning models locally, reducing reliance on expensive cloud resources.
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Qwen-Image-2512 MLX Ports for Apple Silicon
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Qwen-Image-2512, the latest text-to-image model from Qwen, is now available with MLX ports for Apple Silicon, offering five quantization levels ranging from 8-bit to 3-bit. These options allow users to run the model locally on their Mac, with sizes from 34GB for the 8-bit version down to 22GB for the 3-bit version. By installing the necessary tools via pip, users can generate images using prompts and specified steps, providing flexibility and accessibility for Mac users interested in advanced text-to-image generation. This matters as it enhances the capability for local AI-driven creativity on widely used Apple devices.
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TensorFlow 2.16 Release Highlights
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TensorFlow 2.16 introduces several key updates, including the use of Clang as the default compiler for building TensorFlow CPU wheels on Windows and the adoption of Keras 3 as the default version. The release also supports Python 3.12 and marks the removal of the tf.estimator API, requiring users to revert to TensorFlow 2.15 or earlier if they need this functionality. Additionally, for Apple Silicon users, future updates will be available through the standard TensorFlow package rather than tensorflow-macos. These changes are significant as they streamline development processes and ensure compatibility with the latest software environments.