Fine-Tuning and Adapting Open LLMs
LoRA, quantization and instruction tuning on your own data
By Houssam Kodad
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About this book
What's inside
When prompting hits its limits, fine-tuning an open model on your own data is the next move — but it's full of expensive ways to waste a week. This book gives you a grounded process for adapting open LLMs with LoRA and QLoRA, from building a clean instruction dataset to evaluating and serving the result. You'll learn what fine-tuning can and can't fix, and how to do it on a realistic GPU budget.
What you'll learn
Skills you'll walk away with
- Decide when to fine-tune versus prompt or use RAG
- Build and clean an instruction-tuning dataset
- Apply LoRA and QLoRA for parameter-efficient tuning
- Use quantization to train and serve on modest GPUs
- Set hyperparameters that converge without overfitting
- Evaluate a tuned model against honest baselines
- Serve adapters efficiently in production
Table of contents
9 chapters-
01
When Fine-Tuning Is the Right Tool
- · Prompting vs RAG vs tuning
- · What tuning can and cannot fix
- · Cost and effort reality check
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02
Datasets Make or Break It
- · Instruction data design
- · Cleaning and deduplication
- · Synthetic data with care
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03
Parameter-Efficient Fine-Tuning
- · Full vs LoRA tuning
- · How LoRA adapters work
- · Targeting the right layers
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04
Quantization for Modest GPUs
- · 8-bit and 4-bit basics
- · QLoRA end to end
- · Memory and throughput trade-offs
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05
Running a Training Job
- · Hyperparameters that matter
- · Monitoring loss and stability
- · Checkpointing and resuming
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06
Alignment and Preference Tuning
- · Supervised fine-tuning
- · DPO and preference data
- · Avoiding capability regressions
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07
Evaluating a Tuned Model
- · Task-specific benchmarks
- · Regression against the base model
- · Human evaluation
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08
Serving Fine-Tuned Models
- · Merging vs serving adapters
- · Multi-adapter serving
- · Latency and batching
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09
Maintaining Adapted Models
- · Versioning data and weights
- · Re-tuning on new data
- · Governance and licensing
This is the full chapter list — exactly what you'll receive in the PDF.
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