Guglielmo Iozzia

LLM Fine-Tuning for Engineers

Building a specialized model requires a variety of decisions. This free ebook helps you make sense of them, from choosing training examples and fine-tuning your model to planning experiments that make good use of your testing budget. Across five complementary chapters from Manning books, you’ll explore Bayesian optimization through a compiler example, examine the strengths and limitations of LLM judges, and investigate runtime performance with ONNX profiling. You’ll come away with practical methods you can apply as you develop, evaluate, and refine your own models.

Inference Engineering

Find out what’s slowing your LLM down—and what you can do about it. This eBook brings together five chapters from Manning books to help you make informed choices about inference, model efficiency, and production deployment. Explore ways to ease memory pressure, balance quantization savings with output quality, and reduce data movement with fused Triton kernels. Then learn how batching, streaming, and rate limiting help you handle requests in a running service. Whether you’re refining a prototype or preparing for deployment, you’ll gain practical techniques to try on your own workloads and a stronger foundation for further study of LLM optimization and GPU programming.

Local AI and Small Language Models

Choose a model that suits your task, adapt it to your needs, and build software that runs locally. This free eBook features four chapters from four Manning books, covering small language models, specialization with LoRA and DoRA, CPU inference with GGUF and llama.cpp, and application development through Ollama’s Python library. Get practical guidance on producing structured outputs, making informed quantization choices, and handling streaming responses and chatbot conversation history. Get a firmer grasp of the choices behind local AI as well as concrete techniques to try in your next project, without depending on a hosted inference service.