Fine-Tune DeepSeek Models for Custom Use Cases

DeepSeek's open-source models can be customized for specific tasks using LoRA fine-tuning with just 500-5,000 labeled examples on consumer GPUs with 16GB VRAM. The process involves loading a distilled 7B parameter model with 4-bit quantization, training adapters on attention layers, and deploying through Docker containers with Node.js APIs, typically achieving 10-30 percentage point accuracy improvements for specialized use cases.