Fine-Tuning

Large Language Models (LLMs) power tools like ChatGPT, revolutionizing how we interact with AI. This zero-to-hero guide takes you from foundational machine learning concepts to building, fine-tuning, and deploying LLMs, with curated link resources for hands-on learning.[1][2][3] Whether you’re a beginner with basic Python skills or an intermediate learner aiming for expertise, this post provides a structured path. We’ll cover theory, practical implementations, and pitfalls, drawing from top courses and tutorials. ...

Introduction As large language models (LLMs) have grown into the tens and hundreds of billions of parameters, full fine‑tuning has become prohibitively expensive for most practitioners. Two techniques—LoRA and QLoRA—have emerged as leading approaches for parameter-efficient fine‑tuning (PEFT), enabling high‑quality adaptation on modest hardware. They are related but distinct: LoRA (Low-Rank Adaptation) introduces small trainable matrices on top of a frozen full‑precision model. QLoRA combines 4‑bit quantization of the base model with LoRA adapters, making it possible to fine‑tune huge models (e.g., 65B) on a single 24–48 GB GPU. This article walks through: ...

The OpenAI Cookbook is an official, open-source repository of examples and guides for building real-world applications with the OpenAI API.[1][2] It provides production-ready code snippets, advanced techniques, and step-by-step walkthroughs covering everything from basic API calls to complex agent workflows, making it the ultimate resource for developers transitioning from LLM theory to practical deployment.[4] Whether you’re new to OpenAI or scaling AI features in production, this tutorial takes you from setup to mastery with the Cookbook’s most valuable examples. ...

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. ...