MindMap Gallery DeepSeek core architecture and key technologies innovation
DeepSeek core architecture and key technologies have been innovated. DeepSeek has achieved improved processing speed and reduced computing complexity through a series of key technological innovations, providing strong support for applications in related fields.
Edited at 2025-02-05 21:36:44In order to help everyone use DeepSeek more efficiently, a collection of DeepSeek guide mind map was specially compiled! This mind map summarizes the main contents: Yitu related links, DS profile analysis, comparison of DeepSeek and ChatGPT technology routes, DeepSeek and Qwen model deployment guide, how to make more money with DeepSeek, how to play DeepSeek, DeepSeek scientific research Application, how to import text from DeepSeek into MindMaster, the official recommendation of DeepSeek Wait, allowing you to quickly grasp the essence of AI interaction. Whether it is content creation, plan planning, code generation, or learning improvement, DeepSeek can help you achieve twice the result with half the effort!
This is a mind map about DeepSeek's 30 feeding-level instructions. The main contents include: professional field enhancement instructions, interaction enhancement instructions, content production instructions, decision support instructions, information processing instructions, and basic instructions.
This is a mind map about a commercial solution for task speech recognition. The main content includes: text file content format:, providing text files according to the same file name as the voice file.
In order to help everyone use DeepSeek more efficiently, a collection of DeepSeek guide mind map was specially compiled! This mind map summarizes the main contents: Yitu related links, DS profile analysis, comparison of DeepSeek and ChatGPT technology routes, DeepSeek and Qwen model deployment guide, how to make more money with DeepSeek, how to play DeepSeek, DeepSeek scientific research Application, how to import text from DeepSeek into MindMaster, the official recommendation of DeepSeek Wait, allowing you to quickly grasp the essence of AI interaction. Whether it is content creation, plan planning, code generation, or learning improvement, DeepSeek can help you achieve twice the result with half the effort!
This is a mind map about DeepSeek's 30 feeding-level instructions. The main contents include: professional field enhancement instructions, interaction enhancement instructions, content production instructions, decision support instructions, information processing instructions, and basic instructions.
This is a mind map about a commercial solution for task speech recognition. The main content includes: text file content format:, providing text files according to the same file name as the voice file.
DeepSeek Core architecture and key technologies innovation
Key technologies innovation
Efficient reasoning engine
FlashAttention optimization
Take advantage of the GPU memory bandwidth advantage to accelerate attention calculation and achieve delay reduction of more than 30%.
Dynamic batch processing technology
Flexible adjustment of batch size according to request complexity and optimize throughput.
Multimodal expansion
Unified representation space
Through CLIP-style comparison learning, accurate alignment of text, image and video embedded vectors is achieved, and cross-modal retrieval and generation are supported.
Multimodal Reasoning Engine
Integrate visual Transformer (ViT) and language models to empower cutting-edge applications such as graphic and text Q&A and video description generation.
Resource efficiency promote
Parameter efficient fine-tuning (PEFT)
Using LoRA technology, you can quickly adapt to new tasks by training only 1% parameters, and save up to 90% on video memory.
Quantification and distillation technology
Supports INT8 quantization and model distillation, so that the 10B-level model can run smoothly on edge devices (such as mobile phones).
Core architecture
Model cornerstone
Deeply optimize the Transformer architecture, integrate the sparse attention mechanism, and greatly reduce the computational complexity.
Introduce a dynamic routing network, intelligently allocate computing resources based on the input content, significantly improving the processing speed of long text and complex logical tasks.
Hierarchical strategy optimization
Hybrid expert system (MoE)
Built-in multiple expert subnets, activated on demand through a fine gating mechanism, enhance model capacity while maintaining controllable computing costs.
Phase training Essence
Pre-training stage
Immerse yourself in a trillion-level multilingual corpus (covering Chinese, English and code), and integrate knowledge graphs to deepen entity understanding.
Align stage
Combining human feedback reinforcement learning (RLHF) with constitutional AI concepts, ensure that the output is both safe and in line with value orientation.
Field fine adjustment stage
Inject professional data in specific fields such as finance and medical care to improve the performance of the model in professional tasks.