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Pretraining, Fine-Tuning and Distillation

Understand how a base model becomes a useful application model. Separate training objectives from LoRA and quantization, compare distillation with retrieval, and choose the smallest intervention supported by your evaluation.

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RELATED CONCEPTS

Forward Pass, Backpropagation and Optimizer Steps Open-Weights Models of 2026 Model Onboarding: From Hugging Face to Production Multi-LoRA Serving Quantization for Inference Evaluation and Data Pipeline Infrastructure

LESSONS THAT TEACH THIS

Training at ScaleA training step follows a critical path through compute, exchange and update13 min→

PRACTICE THIS IN REAL QUESTIONS

Open-Weights Models & Serving EnginesFifty teams have fine-tuned the same base model. How do you serve all of them?→Napkin Math, Cost & CapacityHow much memory does it take to fine-tune a 70B model, full fine-tuning versus LoRA?→Kubernetes, Slurm & GPU SchedulingDesign the scheduling and isolation for a multi-tenant fine-tuning service: hundreds of customers, a few base models, shared GPUs.→Distributed Training & ParallelismFine-tune a 70B on one 80 GB card. What do NF4 and double quantization actually buy, and where does DoRA change the arithmetic?→Open-Weights Models & Serving EnginesThe model ships in FP8. Should you requantize to four bits to fit more of it on fewer GPUs?→LLM Inference & ServingHow would you serve hundreds of LoRA adapters on one base model, and what does it cost in throughput?→