Case study: DeepSeek
Definição
DeepSeek é uma família de LLMs da DeepSeek AI. Os modelos são conhecidos pelo forte desempenho em raciocínio e 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 instruções, alignment) as ChatGPT and Claude, with an emphasis on open release and efficiency. Use case: chat, code generation, raciocínio tasks, and RAG or agents when self-hosted or cost control matters.
Como funciona
Modelos base são pré-treinados em grandes corpus de texto e código; ajuste de instruções e otimização de preferências (por ex. DPO) os alinham para 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 ex. 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 raciocínio 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 privacy or cost
Documentação externa
- DeepSeek – Official site
- DeepSeek – Models on Hugging Face — Weights and cards