Case study: DeepSeek
Definición
DeepSeek es una familia de LLMs de DeepSeek AI. Los modelos son conocidos por su fuerte rendimiento en razonamiento y código, released as open weights so they can be run locally or fine-tuned. Variants include dense and mixture-of-experts (MoE) architectures for different scale and cost trade-offs.
They illustrate the same core stack (pretraining, ajuste de instrucciones, alignment) as ChatGPT and Claude, with an emphasis on open release and efficiency. Use case: chat, code generation, razonamiento tasks, and RAG or agents when self-hosted or cost control matters.
Cómo funciona
Base models se preentrenan en large text and code corpora; ajuste de instrucciones and preference optimization (por ej. 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 (por ej. SafeTensors); teams run them with quantization on consumer GPUs or deploy via local inference runtimes (vLLM, Ollama, etc.). Prompt engineering and fine-tuning extend use for specific domains.
Casos de uso
DeepSeek fits when you want strong razonamiento 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 privacidad or cost
Documentación externa
- DeepSeek – Official site
- DeepSeek – Models on Hugging Face — Weights and cards