01 - LLM Models

What You Will Learn

• What large language models are, how they predict tokens, and how sampling works
• Transformer architecture internals: embeddings, positional encoding (RoPE/ALiBi), residuals, FFN
• Attention mechanisms: scaled dot-product, multi-head, Flash Attention, GQA
• Model architecture types: encoder-only (BERT), decoder-only (GPT/LLaMA), encoder-decoder (T5)
• KV caching, paged attention, speculative decoding, and inference optimization
• How LLMs are pretrained and how scaling laws shape model design decisions
• Fine-tuning: SFT, RLHF, DPO, LoRA, QLoRA, and multi-head fine-tuning
• GPU/hardware considerations: VRAM estimation, quantization, parallelism, ZeRO
• Failure modes: catastrophic forgetting, lost in the middle, hallucination, sycophancy
• Production deployment: serving frameworks, latency optimization, context window workarounds
• Interview-ready answers on all LLM topics with 68+ Q&A pairs

Chapter Map

#	File	Topic	Difficulty
1	LLM Fundamentals	Tokens, sampling parameters, context window, model types	Beginner
2	Transformer Architecture	Embeddings, positional encoding, Pre-LN, residuals, SwiGLU FFN	Intermediate
3	Attention Mechanisms	Q/K/V math, multi-head, Flash Attention, GQA, causal masking	Intermediate
4	Model Architecture Types	Encoder-only, Decoder-only, Encoder-Decoder, MoE, model comparison table	Intermediate
5	KV Cache & Inference Optimization	KV cache math, MQA/GQA, paged attention, speculative decoding, continuous batching	Advanced
6	Training & Pretraining	Data curation, BPE, CLM/MLM objectives, scaling laws, distributed training	Intermediate
7	Fine-Tuning	SFT, RLHF, DPO, LoRA math, QLoRA, multi-head fine-tuning	Advanced
8	GPU & Hardware	VRAM estimation, quantization (INT8/INT4/AWQ/NF4), tensor/pipeline/ZeRO parallelism	Advanced
9	Failure Modes & Tricky Issues	Catastrophic forgetting, lost in the middle, hallucination, sycophancy, repetition	Advanced
10	Production Deployment	vLLM/TGI, latency budgets, prefix caching, token window workarounds, cost optimization	Advanced
11	Prompting Strategies	Chat templates, CoT mechanics, system prompts, structured output, prompt injection	Intermediate
12	Q&A Review Bank	68+ Q&A pairs tagged Easy/Medium/Hard across all topics	All levels

Recommended Learning Paths

Path A: Beginner → Conceptual Understanding

1. LLM Fundamentals - understand what LLMs are and how they generate text
2. Transformer Architecture - understand the building blocks
3. Attention Mechanisms - understand the core operation
4. Model Architecture Types - understand the landscape
5. Prompting Strategies - understand how to interact with models

Path B: Interview Preparation (Accelerated)

1. LLM Fundamentals + Transformer Architecture in parallel
2. Attention Mechanisms - very common in technical interviews
3. KV Cache & Inference - increasingly asked in production roles
4. Fine-Tuning - LoRA math, RLHF vs DPO
5. GPU & Hardware - VRAM estimation questions are common
6. Q&A Review Bank - drill all 68 questions

Path C: Production Engineering (Advanced)

1. KV Cache & Inference Optimization
2. GPU & Hardware
3. Production Deployment
4. Failure Modes & Tricky Issues

Resources

• Gemma Handbook - Google's Gemma open model reference
• Q&A Review Bank - 68+ Q&A pairs in this module
• Cross-topic Interview Questions

Key Cross-References

• KV cache and attention variants → Attention Mechanisms + KV Cache
• Catastrophic forgetting → Failure Modes + Fine-Tuning (LoRA)
• Token window limits → Failure Modes + Production Deployment
• VRAM and parallelism → GPU & Hardware
• RAG as a complement to LLM capabilities → 03-RAGs module

Next Topic

02 - Prompt Engineering