Fine-tuning has become the secret weapon for building specialized AI applications. While general-purpose models like GPT-4 and Claude excel at broad tasks, fine-tuning transforms them into laser-focused experts for your specific domain. This guide walks you through everything you need to know—from understanding when to fine-tune to deploying your custom model.
Serving dozens of fine-tuned large language models used to mean provisioning one GPU per model. LoRAX (LoRA eXchange) flips that math on its head: keep a single base model in memory and hot-swap lightweight LoRA adapters per request.
This guide shows you how LoRAX achieves near-constant cost per token regardless of how many fine-tunes you're serving. We'll cover:
You've probably seen model names like Llama-3.1-8B-Instruct.Q4_K_M.gguf or Mistral-7B-v0.3-A3B.awq and wondered what all those suffixes mean. It looks like a secret code, but the short answer is: they tell you two critical things.
Open-source LLMs vary along two independent dimensions:
-Instruct, -Distill, -A3B, etc.) describes how the model was trained and what it's optimized for..gguf, .gptq, .awq, etc.) describes how the weights are stored and where they run best (CPU, GPU, mobile, etc.).Think of it like this: the model variant is the recipe, and the file format is the container. You can put the same soup (recipe) into a thermos, a bowl, or a takeout box (container) depending on where you plan to eat it.
Understanding both dimensions helps you avoid downloading 20 GB of the wrong model at midnight and then spending hours debugging CUDA errors.
Running Large Language Models (LLMs) locally on your Mac is a game-changer. It means faster responses, complete privacy, and zero API bills. But with so many tools popping up every week, which one should you choose?
This guide breaks down the top options—from dead-simple menu bar apps to full-control command-line tools. We'll cover what makes each special, their trade-offs, and how to get started.
The race to build bigger, better language models continues at breakneck speed. Today's state-of-the-art models require massive computing resources that no single GPU can handle. Whether you're training a custom LLM or deploying one for inference, understanding how to distribute this workload is essential.
This guide walks through practical strategies for scaling LLMs across multiple GPUs and nodes, incorporating insights from Hugging Face's Ultra-Scale Playbook.