overview of fine-tuning DeepSeek Large Language Models. It explores the architectural evolution of DeepSeek models, from traditional transformers to efficient Mixture-of-Experts (MoE) designs, and categorizes the various DeepSeek models for different applications. The guide details essential fine-tuning techniques, particularly focusing on Parameter-Efficient Fine-Tuning (PEFT) methods like LoRA and QLoRA, which significantly reduce computational demands. It also emphasizes the critical role of high-quality dataset preparation, outlines the necessary software tools and frameworks, and offers practical advice on hardware infrastructure and hyperparameter tuning for optimal performance, culminating in strategies for model evaluation and seamless deployment.