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. ...

Python’s generators and the yield keyword are powerful features that enable memory-efficient iteration and lazy evaluation. Unlike regular functions that return a single value and terminate, generator functions return an iterator object that pauses and resumes execution on demand, preserving local state across calls.[1][2][5] This comprehensive guide explores generators from basics to advanced internals, including how Python implements them under the hood. Whether you’re optimizing data pipelines or diving into CPython source mechanics, you’ll gain actionable insights with code examples and explanations grounded in official specs and expert analyses.[7] ...

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. ...

Threading locks are a fundamental building block for writing correct concurrent programs in Python. Even though Python has the Global Interpreter Lock (GIL), locks in the threading module are still necessary to coordinate access to shared resources, prevent data races, and implement synchronization patterns (producer/consumer, condition waiting, critical sections, etc.). This article is a deep dive into how Python threading locks work: what primitives are available, their semantics and implementation ideas, common usage patterns, pitfalls (deadlocks, starvation, contention), and practical examples demonstrating correct usage. Expect code examples, explanations of the threading API, and guidance for real-world scenarios. ...

Python gives you a lot of flexibility with objects—but that flexibility comes at a cost. Instances normally carry a per-object dictionary to store attributes, which is powerful but memory‑hungry and a bit slower than it could be. __slots__ is a mechanism that lets you trade some of that flexibility for: Lower memory usage per instance Slightly faster attribute access A fixed, enforced set of attributes This article is a detailed, practical guide to __slots__: how it works, when it helps, when it hurts, and how to use it correctly in modern Python. ...