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In this tutorial, we build a pipeline on Phi-4-mini to explore how a compact yet highly capable language model can handle a full range of modern LLM workflows within a single notebook. We begin by setting up a stable environment, loading Microsoft’s Phi-4-mini-instruct in efficient 4-bit quantization, and then move step by step through streaming […]
Tabular data—structured information stored in rows and columns—is at the heart of most real-world machine learning problems, from healthcare records to financial transactions. Over the years, models based on decision trees, such as Random Forest, XGBoost, and CatBoost, have become the default choice for these tasks. Their strength lies in handling mixed data types, capturing
In this tutorial, we build a workflow that combines Magika’s deep-learning-based file type detection with OpenAI’s language intelligence to create a practical and insightful analysis pipeline. We begin by setting up the required libraries, securely connecting to the OpenAI API, and initializing Magika to classify files directly from raw bytes rather than relying on filenames
In this tutorial, we implement how to run the Bonsai 1-bit large language model efficiently using GPU acceleration and PrismML’s optimized GGUF deployment stack. We set up the environment, install the required dependencies, and download the prebuilt llama.cpp binaries, and load the Bonsai-1.7B model for fast inference on CUDA. As we progress, we examine how
In this tutorial, we explore property-based testing using Hypothesis and build a rigorous testing pipeline that goes far beyond traditional unit testing. We implement invariants, differential testing, metamorphic testing, targeted exploration, and stateful testing to validate both functional correctness and behavioral guarantees of our systems. Instead of manually crafting edge cases, we let Hypothesis generate
In this tutorial, we explore how to run OpenAI’s open-weight GPT-OSS models in Google Colab with a strong focus on their technical behavior, deployment requirements, and practical inference workflows. We begin by setting up the exact dependencies needed for Transformers-based execution, verifying GPU availability, and loading openai/gpt-oss-20b with the correct configuration using native MXFP4 quantization,
In this tutorial, we explore how to build a fully functional background task processing system using Huey directly, without relying on Redis. We configure a SQLite-backed Huey instance, start a real consumer in the notebook, and implement advanced task patterns, including retries, priorities, scheduling, pipelines, locking, and monitoring via signals. As we move step by
The intersection of many-body physics and deep learning has opened a new frontier: Neural Quantum States (NQS). While traditional methods struggle with high-dimensional frustrated systems, the global attention mechanism of Transformers provides a powerful tool for capturing complex quantum correlations. In this tutorial, we implement a research-grade Variational Monte Carlo (VMC) pipeline using NetKet and
Building Transformer-Based NQS for Frustrated Spin Systems with NetKet Read More »
In this tutorial, we build a universal long-term memory layer for AI agents using Mem0, OpenAI models, and ChromaDB. We design a system that can extract structured memories from natural conversations, store them semantically, retrieve them intelligently, and integrate them directly into personalized agent responses. We move beyond simple chat history and implement persistent, user-scoped
How to Build a Universal Long-Term Memory Layer for AI Agents Using Mem0 and OpenAI Read More »
In this tutorial, we build an advanced, production-ready agentic system using SmolAgents and demonstrate how modern, lightweight AI agents can reason, execute code, dynamically manage tools, and collaborate across multiple agents. We start by installing dependencies and configuring a powerful yet efficient LLM backend, and then progressively design custom tools, including mathematical utilities, memory storage,
