Auto Added by WPeMatico
In this tutorial, we fine-tune Liquid AI’s LFM2 model through a complete open-source workflow. We start by loading the base LFM2 checkpoint with QLoRA, preparing a chat-style supervised fine-tuning dataset, training a lightweight LoRA adapter using TRL and PEFT, and then merging the adapter back into the model. We also extend the workflow with DPO […]
In this tutorial, we work through an implementation of NVIDIA Apex, focusing on the components that still matter in modern GPU training workflows. Instead of treating Apex as a general mixed-precision library, we separate the older parts from the still-useful ones and test them directly. We begin by checking the CUDA runtime, building Apex with
In this tutorial, we build a governed AI-agent workflow using Microsoft’s Agent Governance Toolkit as the reference point. We create a Colab-ready implementation where agents do not directly execute tools; instead, every action first passes through a governance layer that checks the agent’s identity, trust score, risk tier, requested tool, action type, sensitivity level, and
In this tutorial, we implement a practical use case with Loguru, a powerful, flexible, and production-ready logging library for Python. We start by building a clean, idempotent logging setup that can be safely rerun without duplicating handlers or producing messy output. From there, we move step by step through structured logging, contextual logging, custom log
In this tutorial, we implement a SkillNet use case as a practical framework for discovering, installing, inspecting, evaluating, and organizing reusable AI skills. We start by setting up a robust SkillNet client with SDK and REST fallback support, then compare keyword search with semantic search to understand how skills can be found for different task
In this tutorial, we explore AgentTrove, one of the largest open-source collections of agentic interaction traces, and learn how to work with it efficiently. Instead of downloading the full dataset, we use streaming to inspect rows, detect the conversation schema, normalize agent turns, and understand how user, assistant, system, and tool messages are structured. We
In this tutorial, we build a complete Ansible lab that runs end-to-end in Google Colab or any Linux environment. We start by installing ansible-core, setting up a local workspace, creating an Ansible configuration file, and defining both static and dynamic inventories. We then explore key Ansible concepts, including group variables, host variables, variable precedence, ad
In this tutorial, we build a complete pgvector playground inside Google Colab and explore how PostgreSQL can work as a powerful vector database for modern AI applications. We start by installing PostgreSQL, compiling the pgvector extension, connecting through Psycopg, and registering vector types for smooth Python integration. Then, we create embeddings with SentenceTransformers, store them
In this tutorial, we use zeroentropy/zerank-2-reranker, a 4B Qwen3-based cross-encoder reranker, to improve retrieval quality. We start by setting up the runtime, loading the reranker, and understanding how it scores query-document pairs. Then, we move from simple pairwise scoring to a practical two-stage retrieve-and-rerank pipeline, where a fast bi-encoder first retrieves candidates and zerank-2 reranks
Design a High-Precision Retrieve-and-Rerank Pipeline with ZeroEntropy Zerank-2 Reranker Read More »
In this tutorial, we explore the TuringEnterprises/Open-MM-RL dataset as a practical foundation for multimodal reasoning and reinforcement learning with verifiable rewards. We load the dataset, inspect its schema, analyze domains, formats, question lengths, answer types, and image distributions, and visualize representative examples from each domain. We also build a lightweight reward function that checks exact,
