LLM ApplicationsAdvancedUpdated 2026-06-01💎 Deep-dive special · Cohort 4

LLM Fine-tuning & Alignment

20 lessons 10 hours video / illustrated text

LLM fine-tuning and alignment is the core technique for adapting general-purpose models to specific businesses — and a guaranteed topic in big-tech algorithm-role interviews. This course starts from the LoRA / QLoRA principles, extends into SFT data preparation and the RLHF / DPO alignment paradigms, then into DeepSpeed ZeRO distributed training — taking you from calling APIs to actually training models, along the whole chain.

By the end of this course you will be able to:

deeply understand the LoRA / QLoRA / AdaLoRA principles and engineering practice
master the full SFT pipeline: data preparation → training config → effect evaluation
understand the core logic of RLHF's three stages (SFT / RM / PPO)
master the principles and selection of new alignment paradigms like DPO / KTO
independently run one LoRA fine-tuning practice, from data to training to evaluation
standard answers and principle derivations for 30 high-frequency fine-tuning interview questions

LoRA / QLoRA / AdaLoRA principles and engineering practice
SFT data preparation and the training pipeline
RLHF / DPO / KTO paradigm comparison
Key points of reward-model design
DeepSpeed ZeRO / FSDP distributed training
30 high-frequency fine-tuning questions explained in depth

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The course is organized into four modules totaling 20 lessons.

01 PEFT Efficient Fine-tuning

6 lessons · 3 hours

full-parameter vs parameter-efficient fine-tuning28 min
LoRA internals dissected34 min
QLoRA: 4-bit quantization + LoRA32 min
variants: AdaLoRA / DoRA and more28 min
hands-on engineering with the PEFT library30 min
PEFT module quiz: 12 high-frequency interview questions30 min

02 SFT Supervised Fine-tuning

4 lessons · 2 hours

instruction data preparation and quality control30 min
Chat Template and data formats26 min
fine-tuning practice with the Transformers Trainer36 min
SFT module quiz: 8 high-frequency interview questions24 min

03 Alignment paradigms (RLHF / DPO / KTO)

6 lessons · 3 hours

the three stages of RLHF: SFT → RM → PPO32 min
Key points of reward-model design28 min
DPO: direct preference optimization principles30 min
variants: KTO / IPO / SimPO and more30 min
alignment-paradigm quiz: 15 high-frequency interview questions32 min

04 Distributed Training Practice

4 lessons · 2 hours

DeepSpeed ZeRO-1/2/3 internals30 min
FSDP practice and memory optimization32 min
lessons learned from multi-node multi-GPU training28 min
distributed-training quiz: 10 high-frequency interview questions26 min

🎯 Who it’s for

job seekers for big-tech algorithm roles (a must)
engineers who want to go deep on LLM training
those who already know LLM fundamentals and want to specialize in training
grad students doing LLM-related research

🚀 What you’ll gain

a systematic body of fine-tuning & alignment knowledge
the ability to compare and select among mainstream alignment paradigms
engineering experience with distributed training
standard answers for 30 high-frequency fine-tuning questions
the core edge for big-tech algorithm roles

📋 Prerequisites

comfortable with PyTorch and deep-learning fundamentals
know the Transformer architecture (ideally after Full-Stack Program Module 1)
GPU access is a plus

⏰ Pace

10 hours total; recommended pace: complete in 2–3 weeks
6–8 lessons per week + hands-on drills
Access starts immediately on enrollment; review anytime, forever

The Instructor

CAS PhD · senior algorithm engineer · sits on real hiring loops · author behind the WeChat account 「兔老板工作室」

View the full instructor profile →

It's for big-tech algorithm-role job seekers (fine-tuning is a required big-tech algorithm topic), engineers deepening their LLM-training skills, and grad students doing LLM research. Prerequisites: comfort with PyTorch and deep-learning fundamentals plus knowledge of the Transformer architecture.

Starting from the LoRA/QLoRA/AdaLoRA principles, it covers the full SFT pipeline (data preparation → training config → effect evaluation), the core logic of RLHF's three stages (SFT/RM/PPO), new alignment paradigms like DPO/KTO, plus hands-on DeepSpeed ZeRO distributed training.

It's 10 hours total, ideally completed over 2–3 weeks at 6–8 lessons plus hands-on drills per week. Access opens immediately on enrollment with permanent viewing, plus standard answers to 30 high-frequency fine-tuning questions.

LoRA compresses trainable parameters to a tiny scale with low-rank decomposition, updating only low-rank matrices; QLoRA stacks LoRA on top of a 4-bit quantized base, cutting memory far further so consumer GPUs can fine-tune 7B/13B models. Module 1 (PEFT) dissects the principles, rank selection, and memory estimation in depth.

It depends on parameter count, method, and sequence length: full-parameter fine-tuning of a 7B needs roughly 50GB+; LoRA around 16–24GB; QLoRA can run a 7B in about 6–8GB. The course teaches concrete memory-estimation methods so you can gauge feasibility on the GPUs you actually have.

High-frequency topics: LoRA/QLoRA internals and rank selection, instruction-data quality and construction, overfitting and catastrophic forgetting, SFT vs RLHF, and choosing between fine-tuning, RAG, and prompt engineering. Every module pairs with high-frequency question deep-dives, from principles to engineering decisions.

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