Overview

Break free from expensive API dependencies and take complete control of your AI infrastructure. ""Open Source LLMs: From Zero to Production"" is the comprehensive, hands-on guide that transforms developers, ML engineers, and technical leaders into experts capable of deploying production-grade language models on their own hardware. While companies spend thousands monthly on proprietary API calls, you'll learn to build private, cost-effective AI solutions that respect data privacy, eliminate vendor lock-in, and scale on your terms. This book delivers battle-tested strategies for fine-tuning cutting-edge open source models-LLaMA, Mistral, and GPT4All-without the theory-heavy fluff found in academic texts. What You'll Master: Starting from fundamental transformer architecture, you'll progress through every stage of the ML lifecycle. Set up professional development environments with Docker, CUDA, and essential Python libraries. Understand the nuanced differences between model families and select the right architecture for your specific use case-whether that's a 7B parameter model running on consumer hardware or a 70B model distributed across GPU clusters. Dive deep into parameter-efficient fine-tuning with LoRA and QLoRA, training custom models with a fraction of the memory requirements of traditional approaches. Learn advanced techniques like RLHF, Direct Preference Optimization, and Constitutional AI to align models with your exact specifications. Master distributed training strategies using DeepSpeed and FSDP to handle models that won't fit on a single GPU. But training is only half the battle. You'll discover production inference optimization with vLLM, TensorRT, and quantization techniques that dramatically reduce latency and hardware costs. Build robust REST APIs with FastAPI, implement OpenAI-compatible endpoints, and architect microservices that handle thousands of concurrent requests. Deploy with confidence using Kubernetes, Terraform, and CI/CD pipelines that automate everything from model versioning to multi-region failover. Security and compliance receive dedicated coverage-from prompt injection prevention to GDPR compliance, from federated learning to red team testing. You'll also explore advanced use cases including Retrieval-Augmented Generation (RAG), multi-modal models, autonomous agent systems, and domain-specific applications in healthcare, legal, and technical fields. Every chapter includes practical code examples, architecture diagrams, and real-world troubleshooting scenarios. Comprehensive appendices provide hardware specifications, cost analyses, command-line references, and curated community resources. Who This Book Is For: Software engineers implementing AI features, ML engineers transitioning to LLMs, DevOps professionals managing AI infrastructure, startups building privacy-first products, and enterprises seeking independence from cloud AI providers. Stop renting AI. Start owning it. Transform from API consumer to AI infrastructure architect-get your copy today and deploy your first production LLM within weeks.

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