deepseek
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ChineseDeepSeek
DeepSeek
DeepSeek (from China) disrupted the market in late 2024/2025 by releasing DeepSeek-V3 and R1 (Reasoning) with performance matching Claude/GPT-4 at 1/10th the cost.
来自中国的DeepSeek在2024年末/2025年初发布了DeepSeek-V3和R1（推理模型），其性能可与Claude/GPT-4媲美，但成本仅为后者的1/10，颠覆了市场格局。
When to Use
适用场景
- Cost Efficiency: The API is incredibly cheap.
- Reasoning: DeepSeek-R1 uses Chain-of-Thought reinforcement learning (like OpenAI o1) but is open weights.
- Coding: DeepSeek-Coder-V2 is a top-tier coding model.
- 成本效益：API价格极低。
- 推理能力：DeepSeek-R1采用思维链强化学习（类似OpenAI o1），且为开源权重模型。
- 编码能力：DeepSeek-Coder-V2是顶级的编码模型。
Core Concepts
核心概念
MLA (Multi-Head Latent Attention)
MLA (Multi-Head Latent Attention)
Architectural innovation that drastically reduces KV cache memory usage (allowing huge context).
这是一种架构创新，可大幅减少KV缓存的内存占用（支持超大上下文窗口）。
DeepSeek-R1
DeepSeek-R1
A reasoning model that outputs its "thought process" before the final answer.
一款在输出最终答案前会先输出自身“思考过程”的推理模型。
Best Practices (2025)
2025年最佳实践
Do:
- Use R1 for Math/Logic: It rivals o1-preview in math benchmarks.
- Local Distillations: Run locally for private reasoning.
DeepSeek-R1-Distill-Llama-70B
Don't:
- Don't suppress thoughts: When using R1, the "thought" trace is valuable for debugging the model's logic.
建议做法：
- 用R1处理数学/逻辑问题：在数学基准测试中，其性能可与o1-preview匹敌。
- 本地蒸馏部署：可本地运行以实现私有推理。
DeepSeek-R1-Distill-Llama-70B
不建议做法：
- 不要抑制思考过程：使用R1时，“思考”轨迹对调试模型逻辑非常有价值。