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Tuning Large Language Models for Real-World Applications
16 chapters and 104 canonical sections synced from the Cuantum content database.
Author
Cuantum Tech.
Chapters
16
Reading time
~ 22h
Level
Professional
Language
English
Edition
2025
Your progress0%
Chapters & sections
16 chapters - 104 sectionsChapter 1: Instruction Tuning and SFT (Supervised Fine-Tuning)
0/6Chapter 1 Practical Project: Fine-Tune an Open-Source Model with Instruction Tuning (SFT)
0/10Project prerequisites5mStep 1: Create a clean project folder3mStep 2: Install the tools3mStep 3: Build a small instruction dataset (starter set)4mStep 4: Clean + split → JSONL (training format)7mStep 5: Train with SFT (the clean, modern way)10mStep 6: Quick evaluation (does it actually follow your instructions?)7mStep 7: Make it "real" (a simple qualitative test set)5mStep 8: Common issues and how to fix them4mYour "graduation" checklist (if you can do these, you're doing real SFT)3m
Chapter 1 Quiz
0/2Chapter 2: Parameter-Efficient Fine-Tuning (PEFT)
0/5Chapter 2 Practical Project: Apply LoRA/QLoRA to fine-tune LLaMA or Mistral on a domain dataset
0/13Step 1: Choose a Domain Dataset6mStep 2: Install Dependencies1mStep 3: Load Mistral with QLoRA Configuration3mStep 4: Attach LoRA Adapters3mStep 5: Load Dataset1mStep 6: Configure Training2mStep 7: Train1mStep 8: Save Adapter1mStep 9: Reload and Evaluate2mStep 10: Compare LoRA vs QLoRA1mStep 11: Deploy Modular Adapters1mWhat You Just Accomplished4mReflection Questions3m
Chapter 2 Quiz
0/2Chapter 3: Reinforcement Learning with Human & AI Feedback
0/6Chapter 3 Practical Project: Implement DPO to align a chatbot’s responses using preference pairs.
0/19Project Setup6mStep 1: Define Your Alignment Goal4mStep 2: Create Prompts for Preference Data3mStep 3: Generate Candidate Responses7mStep 4: Turn Candidates into Preference Pairs10mStep 5: Prepare the Dataset for TRL DPOTrainer5mStep 6: Train with DPOTrainer7mStep 7: Evaluate Base vs Aligned Model9mStep 8: Improve the Model by Improving the Preferences6mStep 9: Save, Version, and Reuse Your Aligned Chatbot5mDeployment Considerations7mWhat You've Accomplished8mNext Steps and Extensions1mScaling to Larger Models2mParameter-Efficient Fine-Tuning2mImproving Preference Label Quality3mExploring Alternative Algorithms2mBuilding Production-Ready Alignment Systems3mTransferring Your Skills2m
Chapter 3 Quiz
0/2Chapter 4: Evaluation and Alignment
0/74.1 Benchmarks: HELM, MT-Bench, Arena Hard55m4.2 Task-Specific Evaluation (QA, Summarization, Code, Dialogue)117m4.3 Measuring Hallucinations, Truthfulness, and Factual Grounding25m4.4 Bias, Toxicity, Fairness & Responsible LLM Alignment86m4.5 What Could Go Wrong? Troubleshooting Evaluation and Alignment Failures6mPractical Exercises – Chapter 47mChapter 4 Summary4m
Chapter 4 Practical Project: Evaluate a Fine-Tuned Model Using MT-Bench and Build a Small Hallucination-Detection Pipeline
0/11Project Overview and Files3mStep 1: Pick Your Base and Fine-Tuned Models2mStep 2: Build an MT-Bench-Style Multi-Turn Test Set3mStep 3: Implement the MT-Bench Evaluation Harness6mStep 4: Add a Lightweight MT-Bench Judge (Optional but Recommended)9mStep 5: Build a Grounded Evaluation Set for Hallucination Detection4mStep 6: Force the Model to Answer Using Only Context6mStep 7: Implement Hallucination Detection (Small but Useful)6mStep 8: Interpret Results Like an Alignment Engineer21mStep 9: Improvements You Can Add Next5mStep 9: Improvements You Can Add Next8m
Chapter 4 Quiz
0/2Chapter 5: Deployment and Inference
0/6Chapter 5 Practical Project: Deploy a fine-tuned LoRA model with vLLM, monitor latency, and test quantization.
0/8Step 1: Define Your Model Artifacts5mStep 2: Install vLLM and Dependencies3mStep 3: Serve Base Model + LoRA with vLLM5mStep 4: Send Requests with a Simple Client5mStep 5: Add a Lightweight Monitoring Logger10mStep 6: Test Quantization (Compare Performance and Memory)11mStep 7: Compare Results and Make a Deployment Decision9mStep 8: Production Hardening Checklist5m