The Craft of Post-Training : A Practical Guide for AI Engineers and Developers
Language: English
Published by No Starch Press Sep 2026, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
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Add to basketItem description from seller
Neuware - Capable by default. Reliable by design.A pre-trained model has read most of the internet—and can be trusted with almost none of it. Post-training is the work that changes that: where you take a raw, general model and shape it into something that behaves, follows instructions, refuses what it shouldn’t do, and handles the specific job you need. It’s the human hand on the machine, and the part almost no one explains.Chris von Csefalvay has spent his career building production ML systems in industry, from clinical language to legal text. In The Craft of Post-Training, he shows you the decisions behind every technique: when to fine-tune and when not to, why a model quietly gets worse, and which method fits the constraint you’re actually under. The math is here, because knowing why a technique works is what lets you debug it when it breaks.You’ll know how to:- Choose among the main post-training methods, from SFT and RLHF to DPO, KTO, and GRPO, well enough to fix failures instead of guessing- Adapt a model to your domain without catastrophic forgetting—the tendency of a network to abruptly overwrite what it already knew when you train it on something new- Run larger models with the memory you have by using new quantization- Train agentic systems to act reliably under adversarial pressure- Measure what matters in your deployment, beyond standard benchmarksWhen you’ve used LLMs long enough, you start to wonder what was done to make them behave. The secret is in the post-training that shaped them. The Craft of Post-Training shows you how that’s done.…
Seller Inventory # 9781718505209
- Title
- The Craft of Post-Training : A Practical Guide for AI Engineers and Developers
- Author
- Chris Von Csefalvay
- Publisher
- No Starch Press Sep 2026
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1718505205
- ISBN 13
- 9781718505209
- Item weight
- 816 grams
- Dimensions
- 234x178x25 mm
If you're a practitioner who has watched a promising AI demo fail to survive contact with production, where prompting hits its ceiling, retrieval isn't enough, and the model still can't be trusted with your domain, post-training is what you've been missing.
Post-Training is a practical guide to turning foundation models into production-ready systems — reshaping behavior, aligning to your values, and deploying with confidence. Each technique is taught concept-first, then implementation-through-code, so you understand not just what to run, but what you're actually changing inside the model.
You'll leave with the skills to:
- Fine-tune models on curated datasets using supervised fine-tuning, LoRA, and QLoRA without destroying the base model's general capabilities
- Apply reinforcement learning from human feedback and modern preference optimization methods, including GRPO, ORPO, and beyond, to shape model behavior
- Evaluate models rigorously: design benchmarks, detect regression, and measure quality claims that survive scrutiny
- Adapt models to specialized domains, from clinical language to legal text, turning general capability into a defensible competitive advantage
- Train agentic models that take sequences of actions reliably, not just models that talk about taking actions
- Quantize and compress fine-tuned models for deployment without sacrificing the gains you trained for
Post-training is where models stop being impressive and start being useful. This book teaches you to do it right.
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