In this tutorial, we’ll learn how to leverage the Adala framework to build a modular active learning pipeline for medical symptom classification. We begin by installing and verifying Adala alongside required dependencies, then integrate Google Gemini as a custom annotator to categorize symptoms into predefined medical domains. Through a simple three-iteration active learning loop, prioritizing… →

In this tutorial, we walk you through setting up a fully functional bot in Google Colab that leverages Anthropic’s Claude model alongside mem0 for seamless memory recall. Combining LangGraph’s intuitive state-machine orchestration with mem0’s powerful vector-based memory store will empower our assistant to remember past conversations, retrieve relevant details on demand, and maintain natural continuity… →

Sparse large language models (LLMs) based on the Mixture of Experts (MoE) framework have gained traction for their ability to scale efficiently by activating only a subset of parameters per token. This dynamic sparsity allows MoE models to retain high representational capacity while limiting computation per token. However, with their increasing complexity and model size… →

Large language models are now central to various applications, from coding to academic tutoring and automated assistants. However, a critical limitation persists in how these models are designed; they are trained on static datasets that become outdated over time. This creates a fundamental challenge because the language models cannot update their knowledge or validate responses… →

LLMs have made impressive gains in complex reasoning, primarily through innovations in architecture, scale, and training approaches like RL. RL enhances LLMs by using reward signals to guide the model towards more effective reasoning strategies, resulting in longer and more coherent thought processes that adapt dynamically to a task’s complexity. Despite this, most RL-enhanced LLMs… →