5 Lessons
Advanced AI Engineer
★ 4.98
(3,100+ Engineers Enrolled)
Model Fine-Tuning, Quantization & RLHF (PEFT, LoRA, vLLM)
Instruction Tuning, LoRA/QLoRA (NF4), GGUF/AWQ Quantization, DPO & vLLM Serving
⏱️ 35 Hours
📖 5 Modules
⚡ 16 Interactive Lessons
🏆 Free Verified Certificate
Complete Track Curriculum & Modules
Step-by-step interactive lessons with live browser compilers & coding assignments.
16 Lessons
1
3 Lessons
Module 1: Pre-training vs Fine-Tuning & Dataset Engineering
01. LLM Adaptation Paradigms: Pretraining, Continued Pretraining, Full Fine-Tuning vs PEFT
⏱️ 45 mins
•
⚡ Interactive Sandbox
02. Fine-Tuning Dataset Engineering: Instruction Datasets (Alpaca/ShareGPT formats), Quality Filtering & Deduplication
⏱️ 45 mins
•
⚡ Interactive Sandbox
03. Tokenization Traps: Prompt Formatting, Chat Templates (ChatML / Llama-3 format) & Loss Masking on Prompts
⏱️ 45 mins
•
⚡ Interactive Sandbox
2
3 Lessons
Module 2: Parameter-Efficient Fine-Tuning (PEFT) & LoRA / QLoRA Internals
04. Low-Rank Adaptation (LoRA) Mathematics: Intrinsic Rank Hypothesis, Matrix Factorization (W + B*A) & Alpha Scaling
⏱️ 50 mins
•
⚡ Interactive Sandbox
05. QLoRA Deep Dive: 4-Bit NormalFloat (NF4), Double Quantization & Paged Optimizers for VRAM Efficiency
⏱️ 50 mins
•
⚡ Interactive Sandbox
06. Target Modules Selection (q_proj, k_proj, v_proj, o_proj, gate_proj, up_proj, down_proj) & Rank Tradeoffs
⏱️ 45 mins
•
⚡ Interactive Sandbox
3
3 Lessons
Module 3: Supervised Fine-Tuning (SFT) with HuggingFace TRL & Unsloth
07. Hugging Face TRL (Transformer Reinforcement Learning) & SFTTrainer Architecture
⏱️ 50 mins
•
⚡ Interactive Sandbox
08. Ultra-Fast Fine-Tuning with Unsloth (Manual Triton Kernels & 5x Speedup on Single GPUs)
⏱️ 50 mins
•
⚡ Interactive Sandbox
09. Monitoring Loss Curves: Overfitting Detection, Validation Perplexity & Catastrophic Forgetting Mitigation
⏱️ 45 mins
•
⚡ Interactive Sandbox
4
3 Lessons
Module 4: Alignment Algorithms: DPO (Direct Preference Optimization) & RLHF
10. RLHF (Reinforcement Learning from Human Feedback) Architecture: Reward Models & PPO (Proximal Policy Optimization)
⏱️ 50 mins
•
⚡ Interactive Sandbox
11. Direct Preference Optimization (DPO): Eliminating Reward Models via Implicit Policy Optimization Math
⏱️ 50 mins
•
⚡ Interactive Sandbox
12. ORPO (Odds Ratio Preference Optimization) & KTO (Kahneman-Tversky Optimization) Alignment Techniques
⏱️ 45 mins
•
⚡ Interactive Sandbox
5
4 Lessons
Module 5: Model Quantization (GGUF, AWQ, GPTQ) & High-Throughput vLLM Serving
13. Post-Training Quantization (PTQ): 4-Bit & 8-Bit Quantization Mathematics (Zero Point & Scale Factors)
⏱️ 45 mins
•
⚡ Interactive Sandbox
14. Quantization Formats: GGUF (llama.cpp on CPU/Mac), AWQ (Activation-aware Weight Quantization) & GPTQ
⏱️ 50 mins
•
⚡ Interactive Sandbox
15. Merging LoRA Adapters back into Base Model Weights for Zero-Inference Latency Overhead
⏱️ 40 mins
•
⚡ Interactive Sandbox
16. High-Throughput Enterprise Model Serving with vLLM (PagedAttention, Continuous Batching & Tensor Parallelism)
⏱️ 55 mins
•
⚡ Interactive Sandbox
Target Big Tech Roles
Google / Meta SDE-2
Senior Full-Stack Architect
Lead Backend Engineer
Skills You Will Master
- Production-Grade Clean Architecture
- V8 Internals, Memory Profiling & Leaks
- Time & Space Complexity Optimization
- Big Tech Behavioral & System Design Edge
Verified Digital Certificate
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⚡
Live Interactive Sandbox
Test real-time code without installing Node, Python or C++ locally. Instant output in browser!
Launch Code Sandbox (8-in-1)