How to Run Local LLMs with Llama.cpp: Complete Guide скачать в хорошем качестве

How to Run Local LLMs with Llama.cpp: Complete Guide

In this guide, you'll learn how to run local llm models using llama.cpp. In this llamacpp guide you will learn everything from model preparation such as what a gguf is, how to convert an llm into a gguf, how to quantize an llm and also everything in regards to local llm inference. This is a complete llama.cpp tutorial so we even cover how to run LoRA's, how to benchmark your models and how you should use llama.cpp bindings to include llm inference in the applications you build. We also compare it against popular alternatives such as ollama and vllm. After watching this video you will know everything you need to know about llama.cpp. Github to llama.cpp: https://github.com/ggml-org/llama.cpp Timestamps: 00:00:00 - Why run llms locally? 00:01:00 - What is llama.cpp? 00:02:10 - llama.cpp vs ollama vs vllm vs lmstudio 00:05:30 - Tour of the llama.cpp repo 00:08:40 - How to build / install llama.cpp 00:19:20 - How to run llms locally with llama.cpp 00:32:10 - How to benchmark llms 00:35:14 - Structured outputs with grammars and json-schema 00:37:20 - Memory mapping (no-mmap, mlock) 00:41:10 - How to create a gguf model with llama.cpp 00:45:33 - How to quantize an llm 00:49:30 - How to use a lora adapter 00:57:00 - How to merge lora with base model 01:01:00 - How to use llama.cpp bindings to build applications 01:06:50 - Outro