model training

**LLM training** is the **multi-stage process that transforms a neural network from random parameters into a capable language model** — encompassing pretraining on massive text corpora, supervised fine-tuning on instruction-response pairs, and alignment through RLHF or DPO to produce models that are helpful, harmless, and honest. **What Is LLM Training?** - **Pretraining**: Self-supervised learning on trillions of tokens from internet text, books, and code. - **Supervised Fine-Tuning (SFT)**: Training on curated (instruction, response) pairs to teach format and helpfulness. - **Alignment (RLHF/DPO)**: Human preference optimization to make outputs safe and useful. - **Scale**: Modern models train on 1-15 trillion tokens with billions of parameters. **Training Phases** **Phase 1 — Pretraining**: - **Objective**: Next-token prediction (causal language modeling). - **Data**: Common Crawl, Wikipedia, GitHub, books, scientific papers. - **Compute**: 10,000+ GPUs running for weeks to months. - **Cost**: $10M–$100M+ for frontier models. - **Output**: Base model with broad knowledge but no instruction-following ability. **Phase 2 — Supervised Fine-Tuning (SFT)**: - **Data**: 10K–1M high-quality (prompt, response) examples. - **Effect**: Teaches the model to follow instructions and respond in desired format. - **Duration**: Hours to days on 8-64 GPUs. - **Techniques**: Full fine-tuning, LoRA, QLoRA for efficiency. **Phase 3 — Alignment**: - **RLHF**: Train reward model on human preferences, then optimize policy with PPO. - **DPO**: Direct preference optimization without separate reward model. - **Constitutional AI**: Self-critique and revision based on principles. - **Goal**: Helpful, harmless, honest responses. **Key Concepts** - **Tokenization**: BPE, WordPiece, or SentencePiece converts text to tokens. - **Scaling Laws**: Performance scales predictably with compute, data, and parameters. - **Distributed Training**: Data parallelism, tensor parallelism, pipeline parallelism across GPU clusters. - **Mixed Precision**: FP16/BF16 training with FP32 master weights for efficiency. - **Gradient Checkpointing**: Trade compute for memory to train larger models. **Training Infrastructure** - **Hardware**: NVIDIA H100/A100 clusters, Google TPU v5, AMD MI300X. - **Frameworks**: PyTorch + DeepSpeed, Megatron-LM, JAX + T5X. - **Orchestration**: Slurm, Kubernetes for cluster management. - **Storage**: High-throughput distributed filesystems (Lustre, GPFS). LLM training is **the foundation of modern AI capabilities** — the careful orchestration of pretraining, fine-tuning, and alignment determines whether a model becomes a useful assistant or generates harmful content.