Machine-Learning

Modern large language models (LLMs) like GPT-4, Llama, and Claude look magical—but they are built on concepts that have matured over decades: neural networks, gradient descent, and clever architectural choices. This tutorial walks you step by step from classic neural networks all the way to LLMs. You’ll see how each idea builds on the previous one, and you’ll get practical code examples along the way. Table of Contents Foundations: What Is a Neural Network? 1.1 The Perceptron 1.2 From Perceptron to Multi-Layer Networks 1.3 Activation Functions ...

Table of Contents Introduction Probability Fundamentals Conditional Probability and the Chain Rule Probability Distributions How LLMs Use Probability From Theory to Practice Common Misconceptions Conclusion Resources Introduction If you’ve ever wondered how ChatGPT, Claude, or other large language models generate coherent text that seems almost human-like, the answer lies in mathematics—specifically, probability theory. While the internal mechanics of these models involve complex neural networks and billions of parameters, at their core, they operate on a surprisingly elegant principle: predicting the next word by calculating probabilities. ...

Large language models (LLMs) are transformative AI systems trained on massive text datasets to understand, generate, and predict human-like language. They power tools like chatbots, translators, and code generators by leveraging transformer architectures, self-supervised learning, and intricate mechanisms like attention.[1][2][4] This comprehensive guide breaks down LLMs from fundamentals to advanced operations, drawing on established research and explanations. Whether you’re a developer, researcher, or curious learner, you’ll gain a detailed understanding of their inner workings. ...

Table of contents Introduction What is quantization (simple explanation) Why quantize LLMs? Costs, memory, and latency Quantization primitives and concepts Precision (bit widths) Range, scale and zero-point Uniform vs non-uniform quantization Blockwise and per-channel scaling Main quantization workflows Post-Training Quantization (PTQ) Quantization-Aware Training (QAT) Hybrid and mixed-precision approaches Practical algorithms and techniques Linear (symmetric) quantization Affine (zero-point) quantization Blockwise / groupwise quantization K-means and non-uniform quantization Persistent or learned scales, GPTQ-style (second-order aware) methods Quantizing KV caches and activations Tools, libraries and ecosystem (how to get started) Bitsandbytes, GGML, Hugging Face & Quanto, PyTorch, GPTQ implementations End-to-end example: quantize a transformer weight matrix (code) Best practices and debugging tips Limitations and failure modes Future directions Conclusion Resources Introduction Quantization reduces the numeric precision of a model’s parameters (and sometimes activations) so that a trained Large Language Model (LLM) needs fewer bits to store and compute with its values. The result: much smaller models, lower memory use, faster inference, and often reduced cost with only modest accuracy loss when done well[2][5]. ...

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. ...