Deepseek in 6 points: what to know about its operation

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Deepseekdeveloped by High-Flyer Capital, arouses many questions about its development model. Here is a detailed explanation in 6 points to better understand Deepseek:

1. Optimized modular architecture

Deepseek is based on a modular architecture, where different submodels specialize in specific tasks. When a request is formulated:

  • Only the necessary parts of the model are activated.
  • This approach reduces the consumption of resources and increases the speed of execution.
  • It also allows better scalability, because each module can be optimized independently.

2. Distillation training

Deepseek uses knowledge distillation for learning. This method consists of:

  • Use existing high-performance model responses (such as GPT-4 or LLAMA) to form Deepseek.
  • Reduce calculation and data needs while achieving comparable performance.
  • Optimizing the training process, making it faster and economical.

3. Effective management of resources with the test-time compute

The model integrates the Compute testa method to dynamically adjust the computing power according to the complexity of the tasks.

  • This provides optimal performance without overconsumption.
  • This approach reduces operating costs while maintaining a high quality of response.

4. Open Weight: Transparency and collaboration

Deepseek is published in Open Weightwhich means that its parameters are publicly accessible. This transparency offers several advantages:

  • Developers can personalize the model as needed.
  • The improvements made by the community can be integrated into future versions.
  • This open-source strategy promotes collaborative innovation and widens the ecosystem around Deepseek.

5. Economic accessibility and flexibility

Deepseek is distinguished by its drastically reduced cost of use:

  • Until 27 times cheaper that competing models like GPT-4 for uses via API Cloud.
  • It can also be downloaded and executed locally, an ideal solution for companies wishing to guarantee the confidentiality of their data.

6. Modular and specialized applications

Deepseek incorporates separate models for various use cases:

  • Text analysis, content generation, conversational assistance, etc.
  • This specialization increases the accuracy of the results