#large-language-models

Master generative AI and large language model development, fine-tuning, and deployment for various applications. LLM architecture fundamentals: 1. Transformer architecture: self-attention mechanism, multi-head attention, positional encoding. 2. Model scaling: parameter count (GPT-3: 175B), training data (tokens), computational requirements. 3. Architecture variants: encoder-only (BERT), decoder-only (GPT), encoder-decoder (T5). Pre-training strategies: 1. Data preparation: web crawling, deduplication, quality filtering, tokenization (BPE, SentencePiece). 2. Training objectives: next token prediction, masked language modeling, contrastive learning. 3. Infrastructure: distributed training, gradient accumulation, mixed precision (FP16/BF16). Fine-tuning approaches: 1. Supervised fine-tuning: task-specific datasets, learning rate 5e-5 to 1e-4, batch size 8-32. 2. Parameter-efficient fine-tuning: LoRA (Low-Rank Adaptation), adapters, prompt tuning. 3. Reinforcement Learning from Human Feedback (RLHF): reward modeling, PPO training. Prompt engineering: 1. Zero-shot prompting: task description without examples, clear instruction formatting. 2. Few-shot learning: 1-5 examples, in-context learning, demonstration selection strategies. 3. Chain-of-thought: step-by-step reasoning, intermediate steps, complex problem solving. Evaluation methods: 1. Perplexity: language modeling capability, lower is better, domain-specific evaluation. 2. BLEU score: text generation quality, n-gram overlap, reference comparison. 3. Human evaluation: quality, relevance, safety assessment, inter-rater reliability. Deployment considerations: inference optimization, model quantization, caching strategies, latency <1000ms target, cost optimization through batching.