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  • Format: ePub

Are you ready to fine-tune your own LLMs?
This book is a practical guide to fine-tuning Large Language Models (LLMs), combining high-level concepts with step-by-step instructions to train these powerful models for your specific use cases.
Who Is This Book For?
This is an intermediate-level resource positioned between building a large language model from scratch and deploying an LLM in productiondesigned for practitioners with some prior experience in deep learning .
If terms like Transformers, attention mechanisms, Adam optimizer, tokens, embeddings, or GPUs sound familiar,
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Produktbeschreibung
Are you ready to fine-tune your own LLMs?

This book is a practical guide to fine-tuning Large Language Models (LLMs), combining high-level concepts with step-by-step instructions to train these powerful models for your specific use cases.

Who Is This Book For?

This is an intermediate-level resourcepositioned between building a large language model from scratch and deploying an LLM in productiondesigned for practitioners with some prior experience in deep learning.

If terms like Transformers, attention mechanisms, Adam optimizer, tokens, embeddings, or GPUs sound familiar, you're in the right place. Familiarity with Hugging Face and PyTorch is assumed. If you're new to these concepts, consider starting with a beginner-friendly introduction to deep learning with PyTorch before diving in.

What You'll Learn:

  • Load quantized models using BitsAndBytes.
  • Configure Low-Rank Adapters (LoRA) using Hugging Face's PEFT.
  • Format datasets effectively using chat templates and formatting functions.
  • Fine-tune LLMs on consumer-grade GPUs using techniques such as gradient checkpointing and accumulation.
  • Deploy LLMs locally in the GGUF format using Llama.cpp and Ollama.
  • Troubleshoot common error messages and exceptions to keep your fine-tuning process on track.


This book doesn't just skim the surface; it zooms in on the critical adjustments and configurationthose all-important "knobs"that make or break the fine-tuning process.

By the end, you'll have the skills and confidence to fine-tune LLMs for your own real-world applications. Whether you're looking to enhance existing models or tailor them to niche tasks, this book is your essential companion.


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Autorenporträt
Daniel Voigt Godoy is a husband, a brother, and a son. In the last 25 years, he had many jobs developer, data scientist, teacher, writer but he's none of them. He is an avid learner and he has a curious and restless mind.

At age 46, he was finally able to switch gears. It took him several years and lots and lots of questions to figure out what was the right path for him. Now, he's finally at peace and happy with who he is while living his life the best he can.