Llm

In 2017, a team at Google published a paper that would fundamentally reshape the landscape of machine learning. “Attention Is All You Need” by Vaswani et al. introduced the Transformer architecture—a bold departure from the recurrent and convolutional approaches that had dominated sequence modeling for years. The paper’s central thesis was radical: you don’t need recurrence or convolution at all. Just attention mechanisms and feed-forward networks are sufficient to achieve state-of-the-art results in sequence-to-sequence tasks. ...

This guide is a comprehensive, production-grade walkthrough for building Agent-to-Agent (A2A) systems — from first principles to real-world deployment. It is written for engineers who already understand APIs, cloud infrastructure, and LLMs, but are new to multi-agent interoperability. The focus is on practical engineering, not demos. 1. What Is Agent-to-Agent (A2A)? A2A (Agent-to-Agent) is an architectural pattern and emerging protocol standard that enables autonomous software agents to: Discover each other Advertise capabilities Exchange structured tasks Stream intermediate progress Exchange artifacts and results Operate independently across services, teams, or organizations Think of A2A as: ...

Unsloth has quickly become one of the most practical ways to fine‑tune large language models (LLMs) efficiently on modest GPUs. It wraps popular open‑source models (like Llama, Mistral, Gemma, Phi) and optimizes training with techniques such as QLoRA, gradient checkpointing, and fused kernels—often cutting memory use by 50–60% and speeding up training significantly. This guide walks you from zero to production: Understanding what Unsloth is and when to use it Setting up your environment Preparing your dataset for instruction tuning Loading and configuring a base model with Unsloth Fine‑tuning with LoRA/QLoRA step by step Evaluating the model Exporting and deploying to production (vLLM, Hugging Face, etc.) Practical tips and traps to avoid All examples use Python and the Hugging Face ecosystem. ...

Introduction Retrieval-Augmented Generation (RAG) has become a foundational pattern for building accurate, scalable, and fact-grounded applications with large language models (LLMs). At its core, RAG combines a retrieval component (to fetch relevant pieces of knowledge) with a generation component (the LLM) that produces answers conditioned on that retrieved context. This article breaks RAG down from first principles: the indexing and retrieval stages, the augmentation of prompts, the generation step, common challenges, practical mitigations, and code examples to get you started. ...

Large Language Model (LLM) sandboxes are isolated, secure environments designed to run powerful AI models while protecting user data, preventing unauthorized access, and mitigating risks like code execution vulnerabilities. These setups enable safe experimentation, research, and deployment of LLMs in institutional or enterprise settings.[1][2][3] What is an LLM Sandbox? An LLM sandbox creates a controlled “playground” for interacting with LLMs, shielding sensitive data from external providers and reducing security risks. Unlike direct API calls to cloud services like OpenAI, sandboxes often host models locally or in managed cloud instances, ensuring inputs aren’t used for training vendor models.[2] ...