LLM Engineer's Handbook
FreeProduction LLMOps: fine-tuning, quantization, serving (Labonne, Iusztin).
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About LLM Engineer's Handbook
The LLM Engineer's Handbook is a practical guide to production-level LLMOps, authored by Paul Iusztin and Maxime Labonne. It covers essential topics such as fine-tuning large language models, quantization techniques, and efficient model serving, providing hands-on insights for deploying LLMs in real-world environments.
Key Features
Fine-tuning large language models
Quantization techniques for efficient inference
Model serving and deployment strategies
Hands-on examples and best practices
Pros & Cons
Pros
- Authors are experienced ML engineers (Labonne, Iusztin)
- Practical, hands-on approach
- Covers the full LLMOps pipeline from fine-tuning to serving
Cons
- Assumes prior knowledge of machine learning and basic LLM concepts
Best For
Building production-ready LLM systemsOptimizing LLM performance through quantizationDeveloping custom fine-tuned models for domain-specific tasks