{ "item_title" : "Fine-Tuning Local LLMs with Unsloth", "item_author" : [" Kaelion Veyric "], "item_description" : "Want to fine-tune your own AI model but feel overwhelmed by LoRA, QLoRA, VRAM, quantization, datasets, and deployment?You do not need previous experience with LLM fine-tuning to begin.Fine-Tuning Local LLMs with Unsloth is a practical, step-by-step guide that helps you move from running open-source models to actually adapting, evaluating, optimizing, and deploying them yourself.Written for technically inclined beginners, developers, students, data scientists, and self-learners, this book explains complex ideas in clear language and introduces each concept when you need it. Basic familiarity with Python, package installation, and command-line tools is helpful, but no prior experience with Unsloth, LoRA, QLoRA, quantization, or local LLM deployment is required.Instead of overwhelming you with unnecessary theory, the book focuses on steady, practical progress. You will learn through manageable steps, realistic workflows, and measurable results. Training errors, weak outputs, and out-of-memory problems are treated as useful feedback-not failure-so every experiment helps build your confidence.Key FeaturesBeginner-friendly, hands-on approach to local LLM fine-tuningPractical workflows using Unsloth and the Hugging Face ecosystemClear coverage of LoRA, QLoRA, SFT, DPO, GRPO, and quantizationTechniques for working with limited GPU memory and consumer hardwareModel evaluation, checkpointing, reproducibility, and troubleshootingA complete capstone project from dataset preparation to local deploymentWhat You Will LearnChoose the right model for your task and hardwarePrepare, clean, validate, and structure training datasetsFine-tune models efficiently with LoRA and QLoRARun supervised fine-tuning with UnslothOptimize VRAM, batch size, sequence length, and training throughputEvaluate improvements and detect overfitting, hallucinations, and regressionsApply preference and reasoning fine-tuning with DPO and GRPOQuantize models for efficient local inferenceExport models to Hugging Face and GGUFRun and serve fine-tuned models locallyBuild a complete domain-specific local LLMWho This Book Is ForBeginners entering LLM fine-tuningPython developers exploring generative AIStudents and independent learnersData scientists working with open-source modelsAI enthusiasts moving beyond pretrained modelsProfessionals building private or specialized AI systemsTable of ContentsChapter 1: Understanding Unsloth and the Local LLM StackChapter 2: Setting Up a Reproducible Fine-Tuning EnvironmentChapter 3: Choosing the Right Model and Fine-Tuning StrategyChapter 4: Preparing High-Quality Training DataChapter 5: Fine-Tuning Your First Model with UnslothChapter 6: Building a Domain-Specific Local LLMChapter 7: Optimizing Training for Limited HardwareChapter 8: Evaluating Fine-Tuned ModelsChapter 9: Preference and Reasoning Fine-TuningChapter 10: Quantizing Models for Efficient Local InferenceChapter 11: Exporting and Running Fine-Tuned Models LocallyChapter 12: Capstone - Building and Deploying a Complete Local LLMYou do not need to master everything at once. Start with one model, one dataset, and one measurable improvement.Start today and build the practical skills to fine-tune, optimize, and deploy local AI models with confidence.", "item_img_path" : "https://covers3.booksamillion.com/covers/bam/9/79/819/270/9798192701942_b.jpg", "price_data" : { "retail_price" : "32.99", "online_price" : "32.99", "our_price" : "32.99", "club_price" : "32.99", "savings_pct" : "0", "savings_amt" : "0.00", "club_savings_pct" : "0", "club_savings_amt" : "0.00", "discount_pct" : "10", "store_price" : "" } }

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