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Fine-Tuning Large Language Models: From Custom Datasets to High-Performance AI Models Using Modern Toolchains
Paperback
Publisher: Independently Published
Published: Jun 19 2026
Pages: 266
Weight: 0.79
Height: 0.56 Width: 6.00 Depth: 9.00
Language: English
custom-trained large language model using real code, modern tools,
and production-ready techniques used by AI engineers today. Whether you want to build a medical assistant, legal document analyzer,
pipeline to make it happen. What you'll learn: - How fine-tuning differs from prompt engineering and RAG and when
to use each
- LoRA and QLoRA: fine-tune powerful models on a single GPU
- How to collect, clean, and format high-quality instruction datasets
- Supervised Fine-Tuning (SFT), RLHF, and Direct Preference Optimization
- Advanced techniques: mixed precision, gradient checkpointing,
multi-GPU training
- How to evaluate, quantize, and deploy your model to production
- Safety, bias mitigation, and responsible AI practices
- 14 chapters of step-by-step projects including a chatbot,
evaluation dashboard, and automated training pipeline This is not a theory book. Every chapter includes working Python code
using Hugging Face Transformers, PEFT, TRL, and modern open-source
models like Llama and Mistral. By the end, you won't just understand fine-tuning you'll have built
and shipped a model of your own. Perfect for: ML engineers, Python developers, data scientists, and
anyone ready to move beyond ChatGPT and take control of their AI stack.
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