Hugging Face on AI VOID

Chapter 8: Syncing Local Experiments to Hugging Face Spaces

Thu, 01 Jan 2026 00:00:00 +0000

Chapter 8: Syncing Local Experiments to Hugging Face Spaces

Introduction

Welcome back, intrepid experimenter! So far, you’ve mastered tracking your machine learning experiments locally with Trackio, enjoying the simplicity of its Gradio dashboard right on your machine. But what if you need to share your progress with a teammate across the globe? Or perhaps you want to monitor a long-running experiment from your phone while away from your desk? That’s where remote syncing comes in!

Chapter 10: Fine-Tuning Large Language Models (LLMs)

Sat, 17 Jan 2026 00:00:00 +0000

Chapter 10: Fine-Tuning Large Language Models (LLMs)

Introduction

Welcome to Chapter 10, where we unlock the incredible power of Large Language Models (LLMs) by teaching them new tricks! You’ve already built a strong foundation in deep learning, understood neural network architectures, and learned how to train and evaluate models. Now, imagine taking a highly intelligent, pre-trained LLM and making it even smarter for your specific needs. That’s exactly what fine-tuning allows us to do.

Chapter 23: Project: Fine-Tuning an LLM for a Specific Task

Sat, 17 Jan 2026 00:00:00 +0000

Chapter 23: Project: Fine-Tuning an LLM for a Specific Task

Introduction

Welcome to an exciting hands-on chapter where we’ll dive deep into the practical art of fine-tuning Large Language Models (LLMs)! You’ve learned about the power of these models, their architectures, and how they process language. Now, it’s time to make them truly yours by adapting them to perform a specific task that their general pre-training might not have fully covered.

Run MTP LLMs with llama.cpp & vLLM

Tue, 19 May 2026 00:00:00 +0000

What you’ll build: By the end of this tutorial, you will be able to set up and run Multi-Token Prediction (MTP) capable LLMs locally using llama.cpp and vLLM, compare their performance against standard generation, and understand fallback options. Time needed: ~90 minutes Prerequisites: Basic command-line interface (CLI) familiarity, Git installed, C++ compiler (GCC/Clang for Linux/macOS, MSVC for Windows), CMake installed, Python 3.9+ installed, NVIDIA GPU with CUDA (11.8+ recommended) or AMD GPU with ROCm, or Apple Silicon (Metal), Sufficient RAM (16GB+ recommended) and VRAM (8GB+ recommended) Version used: llama.cpp: main branch (post MTP merge); vLLM: latest stable/developer preview with MTP support

Trackio Practical Field Guide

Thu, 01 Jan 2026 00:00:00 +0000

Welcome to the world of efficient machine learning experiment tracking! In this comprehensive guide, we’ll dive deep into Trackio, a powerful yet lightweight tool designed to streamline your ML workflows. Whether you’re a beginner just starting with machine learning or an experienced practitioner looking for a robust, local-first tracking solution with seamless Hugging Face integration, this guide is for you.

What is Trackio?

Trackio is an innovative, open-source Python library meticulously crafted for experiment tracking in machine learning projects. Built on top of Hugging Face Datasets and Spaces, it provides a lightweight, local-first approach to logging and visualizing your experiment metrics, parameters, and artifacts. What makes Trackio particularly appealing is its design as an API-compatible alternative to popular tools like Weights & Biases (WandB), offering a familiar experience with the added benefit of tight integration with the Hugging Face ecosystem. It’s designed for clarity, ease of use, and extensibility, allowing you to focus on your models, not your tracking setup.

Learn Transformers.js: Revolutionizing AI in the Browser

Sun, 26 Oct 2025 00:00:00 +0000

Welcome to “Learn Transformers.js: Revolutionizing AI in the Browser”! This guide is designed for absolute beginners eager to dive into the exciting world of running state-of-the-art machine learning models directly within web browsers using JavaScript. No prior AI or machine learning experience is required. We’ll start from the very basics and progressively build your understanding, equipping you with the knowledge and practical skills to integrate powerful AI capabilities into your web applications.

Mastering LLM Fine-tuning: Pre-training, SFT, and PEFT for Custom Models

Fri, 22 Aug 2025 00:00:00 +0000

LLM Pre-training and Fine-tuning Concepts

Introduction

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence, demonstrating remarkable capabilities in understanding, generating, and processing human language. These powerful models are at the heart of many cutting-edge applications, from sophisticated chatbots and content generators to complex code assistants. This document serves as a comprehensive guide to understanding the lifecycle of LLMs, from their initial pre-training to the crucial process of fine-tuning them for specific tasks and data.