Customization on AI VOID

Chapter 9: Customizing the Dashboard and Trackio's Extensibility

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

Chapter 9: Customizing the Dashboard and Trackio’s Extensibility

Welcome back, experimenter! So far, we’ve learned how to set up Trackio, log various metrics, manage experiments, and even sync with Hugging Face Spaces. You’re becoming a Trackio wizard!

In this chapter, we’re going to dive into making Trackio truly yours. While Trackio is designed to be lightweight and focused, its foundation on Gradio and Hugging Face Datasets provides powerful avenues for customization and extensibility. We’ll explore how to change the look and feel of your experiment dashboard and discuss how you can extend Trackio’s capabilities to fit unique tracking needs.

Chapter 11: Customizing Tunix: Loss Functions, Optimizers, and Callbacks

Fri, 30 Jan 2026 00:00:00 +0000

Introduction

Welcome to Chapter 11! So far, you’ve mastered the fundamentals of setting up Tunix, loading models, and initiating basic post-training runs. But what if the standard tools aren’t quite enough for your specific research or application? What if you need to guide your Language Model (LLM) with a unique objective, fine-tune its learning process with a specialized algorithm, or automate complex actions during training?

This chapter is your gateway to unlocking the full power of Tunix customization. We’ll dive deep into how you can define and integrate your own loss functions to precisely shape your LLM’s learning objective, craft sophisticated optimizers using JAX’s powerful Optax library to control parameter updates, and implement intelligent callbacks to monitor, control, and react to your training process. By the end of this chapter, you’ll be able to tailor Tunix to virtually any LLM post-training scenario, moving beyond off-the-shelf solutions to truly bespoke training pipelines.

Chapter 13: Custom LLM Providers and Integrations

Mon, 05 Jan 2026 00:00:00 +0000

Introduction to Custom LLM Providers

Welcome back, intrepid data explorer! In previous chapters, we’ve seen how LangExtract brilliantly orchestrates Large Language Models (LLMs) to extract structured information from unstructured text. We’ve used its default integrations, which are fantastic for getting started. But what if your needs are a bit more unique?

Perhaps you’re working with a highly specialized, fine-tuned LLM running on your company’s private cloud. Maybe you want to experiment with a bleeding-edge open-source model that just got released on Hugging Face, or you need to integrate with a less common commercial LLM API. This is where the power of LangExtract’s custom LLM provider interface shines!