In this tutorial, we demonstrate how to efficiently fine-tune a large language model using Unsloth and QLoRA. We focus on building a stable, end-to-end supervised fine-tuning pipeline that handles common Colab issues such as GPU detection failures, runtime crashes, and library incompatibilities. By carefully controlling the environment, model configuration, and training loop, we show how…
In this tutorial, we design an end-to-end, production-style analytics and modeling pipeline using Vaex to operate efficiently on millions of rows without materializing data in memory. We generate a realistic, large-scale dataset, engineer rich behavioral and city-level features using lazy expressions and approximate statistics, and aggregate insights at scale. We then integrate Vaex with scikit-learn…
In this tutorial, we build an advanced explainable AI analysis pipeline using SHAP-IQ to understand both feature importance and interaction effects directly inside our Python environment. We load a real-world dataset, train a high-performance Random Forest model, and then apply the SHAP-IQ interaction index to compute precise, theoretically grounded explanations of model predictions. We extract…
In this tutorial, we design a practical image-generation workflow using the Diffusers library. We start by stabilizing the environment, then generate high-quality images from text prompts using Stable Diffusion with an optimized scheduler. We accelerate inference with a LoRA-based latent consistency approach, guide composition with ControlNet under edge conditioning, and finally perform localized edits via…
Google is officially turning Chrome into a playground for AI agents. For years, AI ‘browsers’ have relied on a messy process: taking screenshots of websites, running them through vision models, and guessing where to click. This method is slow, breaks easily, and consumes massive amounts of compute. Google has introduced a better way: the Web…
In this tutorial, we fine-tune a Sentence-Transformers embedding model using Matryoshka Representation Learning so that the earliest dimensions of the vector carry the most useful semantic signal. We train with MatryoshkaLoss on triplet data and then validate the key promise of MRL by benchmarking retrieval quality after truncating embeddings to 64, 128, and 256 dimensions….
In this tutorial, we walk through advanced usage of Einops to express complex tensor transformations in a clear, readable, and mathematically precise way. We demonstrate how rearrange, reduce, repeat, einsum, and pack/unpack let us reshape, aggregate, and combine tensors without relying on error-prone manual dimension handling. We focus on real deep-learning patterns, such as vision…
In this tutorial, we demonstrate how to federate fine-tuning of a large language model using LoRA without ever centralizing private text data. We simulate multiple organizations as virtual clients and show how each client adapts a shared base model locally while exchanging only lightweight LoRA adapter parameters. By combining Flower’s federated learning simulation engine with…
Robots are entering their GPT-3 era. For years, researchers have tried to train robots using the same autoregressive (AR) models that power large language models (LLMs). If a model can predict the next word in a sentence, it should be able to predict the next move for a robotic arm. However, a technical wall has…
In this tutorial, we show how we treat prompts as first-class, versioned artifacts and apply rigorous regression testing to large language model behavior using MLflow. We design an evaluation pipeline that logs prompt versions, prompt diffs, model outputs, and multiple quality metrics in a fully reproducible manner. By combining classical text metrics with semantic similarity…