Generative Neural Architectures-Design Patterns, Model Adaptation, and Production Operations
Large language models are easy to call and hard to run well. Generative Neural Architectures walks through the whole path in plain language: how the transformer predicts text, how to choose a model, how to build applications around it, when to adapt it, and how to keep it reliable and affordable once real users depend on it.
Every chapter explains an idea, turns it into working code and traces it back to the research it came from.
Inside you will learn:
- Foundations: tokens, embeddings, attention and how to read benchmarks with care
- Building: message roles, streaming, prompt design and retrieval-augmented generation
- Adapting: when to fine-tune, LoRA and QLoRA, and how to evaluate the result, with a real fine-tuning run reported step by step
- Operating: deployment choices, quantization, pruning, distillation, monitoring and guardrails
Written for software engineers entering the field and for practitioners who design, adapt and run these systems in production.
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