Case study: DeepSeek
Definition
DeepSeek ist eine Familie von LLMs von DeepSeek AI. Die Modelle sind bekannt für starke Schlussfolgerungs- und Code-Leistung, released as open weights sodass sie can be run locally or feinabgestimmt. Variants include dense and mixture-of-experts (MoE) architectures for different scale and cost trade-offs.
They illustrate the same core Stack (pretraining, Instruktions-Tuning, alignment) as ChatGPT and Claude, with an emphasis on open release and efficiency. Use case: chat, code generation, Schlussfolgern tasks, and RAG or agents when self-hosted or cost control matters.
Funktionsweise
Base models are vortrainiert auf großen Text- und Code-Korpora; Instruktions-Tuning and Präferenzoptimierung (z. B. DPO) align them for chat and tool use. MoE variants activate a subset of parameters per token to scale capacity without proportionally increasing compute. Weights are published in standard formats (z. B. SafeTensors); teams run them with quantization on consumer GPUs or deploy via local inference runtimes (vLLM, Ollama, etc.). Prompt engineering and Feinabstimmung extend use for specific domains.
Anwendungsfälle
DeepSeek passt, wenn you want strong Schlussfolgern and code capability with open weights and local or cost-effective deployment.
- Code generation and code-assisted workflows (IDE, agents)
- Reasoning and math with open, self-hostable models
- Fine-tuning and local inference for Datenschutz or cost
Externe Dokumentation
- DeepSeek – Official site
- DeepSeek – Models on Hugging Face — Weights and cards