Case study: DeepSeek
Définition
DeepSeek is a famille de LLMs from DeepSeek AI. The models are connus pour leurs performances solides en raisonnement et code, 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, ajustement d'instructions, alignment) as ChatGPT and Claude, with an emphasis on open release and efficiency. Use case: chat, code generation, raisonnement tasks, and RAG or agents when self-hosted or cost control matters.
Comment ça fonctionne
Base models sont pré-entraînés sur large text and code corpora; ajustement d'instructions and preference optimization (par ex. 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 (par 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.
Cas d'utilisation
DeepSeek fits when you want strong raisonnement 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 confidentialité or cost
Documentation externe
- DeepSeek – Official site
- DeepSeek – Models on Hugging Face — Weights and cards