HuggingFace
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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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HuggingFace’s FinePDFs Dataset Release
Read Full Article: HuggingFace’s FinePDFs Dataset Release
HuggingFace has released a comprehensive resource called the FinePDFs dataset, comprising 3 trillion tokens, aimed at benefiting the open-source community. This initiative includes insights into creating state-of-the-art PDF datasets, the relevance of older internet content, and the choice of RolmOCR for optical character recognition. Additionally, it discusses the most Claude-like open-source model and the surprising prominence of a horse racing site in the dataset's URL list. This matters because it advances the understanding and accessibility of PDF data processing for developers and researchers in the open-source community.
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mlship: Easy Model Serving for Popular ML Frameworks
Read Full Article: mlship: Easy Model Serving for Popular ML Frameworks![[P] mlship – One-command model serving for sklearn, PyTorch, TensorFlow, and HuggingFace](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9100-300x171.png)
Python is the leading programming language for machine learning due to its extensive libraries, ease of use, and versatility. C++ and Rust are preferred for performance-critical tasks, with C++ being favored for inference and low-level optimizations, while Rust is noted for its safety features. Julia, Kotlin, Java, and C# are also used, each offering unique advantages for specific platforms or performance needs. Other languages like Go, Swift, Dart, R, SQL, and JavaScript serve niche roles in machine learning, from native code compilation to statistical analysis and web interface development. Understanding the strengths of each language can help in selecting the right tool for specific machine learning tasks.
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mlship: One-command Model Serving Tool
Read Full Article: mlship: One-command Model Serving Tool![[P] mlship - One-command model serving for sklearn, PyTorch, TensorFlow, and HuggingFace](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-9099-300x171.png)
mlship is a command-line interface tool designed to simplify the process of serving machine learning models by converting them into REST APIs with a single command. It supports models from popular frameworks such as sklearn, PyTorch, TensorFlow, and HuggingFace, even allowing direct integration from the HuggingFace Hub. The tool is open source under the MIT license and seeks contributors and feedback to enhance its functionality. This matters because it streamlines the deployment process for machine learning models, making it more accessible and efficient for developers and data scientists.
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Guide: Running Llama.cpp on Android
Read Full Article: Guide: Running Llama.cpp on Android![Llama.cpp running on Android with Snapdragon 888 and 8GB of ram. Compiled/Built on device. [Guide/Tutorial]](https://www.tweakedgeek.com/wp-content/uploads/2026/01/featured-article-8272-300x171.png)
Running Llama.cpp on an Android device with a Snapdragon 888 and 8GB of RAM involves a series of steps beginning with downloading Termux from F-droid. After setting up Termux, the process includes cloning the Llama.cpp repository, installing necessary packages like cmake, and building the project. Users need to select a quantized model from HuggingFace, preferably a 4-bit version, and configure the server command in Termux to launch the model. Once the server is running, it can be accessed via a web browser by navigating to 'localhost:8080'. This guide is significant as it enables users to leverage advanced AI models on mobile devices, enhancing accessibility and flexibility for developers and enthusiasts.
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HuggingFace Model Downloader v2.3.0: Web UI & Faster Scanning
Read Full Article: HuggingFace Model Downloader v2.3.0: Web UI & Faster Scanning
The HuggingFace Model Downloader v2.3.0 introduces significant improvements for users downloading models and datasets, including a new web UI that allows for easy management of downloads through a browser. This version supports concurrent connections, smart resume capabilities, and filtering options to download specific quantizations. Notably, it features a one-liner web mode for quick setup and a dramatic increase in repository scanning speed, reducing the time from over five minutes to approximately two seconds. These enhancements make the tool more efficient and user-friendly, particularly for those dealing with large repositories. Why this matters: The updates significantly streamline the process of downloading and managing machine learning models, saving time and simplifying tasks for developers and researchers.
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Qwen-Image-2512 Released on Huggingface
Read Full Article: Qwen-Image-2512 Released on Huggingface
Qwen-Image-2512, a new image model, has been released on Huggingface, a popular platform for sharing machine learning models. This release allows users to explore, post, and comment on the model, fostering a community of collaboration and innovation. The model is expected to enhance image processing capabilities, offering new opportunities for developers and researchers in the field of artificial intelligence. This matters because it democratizes access to advanced image processing technology, enabling a wider range of applications and advancements in AI-driven image analysis.