DeepSeek LLM Model

A DeepSeek LLM Model is a large language model that provides natural language processing (for general-purpose AI tasks).

• Context:
  • It can perform Natural Language Understanding through transformer architecture, attention mechanisms, and context processing.
  • It can enable Multi-Task Processing through task embeddings and instruction following.
  • It can support Knowledge Application through model parameters and training optimization.
  • It can maintain Response Generation through token prediction and sequence completion.
  • It can handle Language Processing Tasks through neural computation and pattern recognition.
  • ...
  • It can range from being a Base Model to being a Fine-Tuned Model, depending on its training objective.
  • It can range from being a General Purpose Model to being a Domain Specific Model, depending on its specialization level.
  • ...
  • It can integrate with DeepSeek Infrastructure for computational resources and deployment management.
  • It can connect to Application Interfaces for user interaction and service delivery.
  • It can support Development Tools for integration workflows and customization options.
  • It can be accessed via:
    • LLM Cloud Platforms
      • DeepSeek Chat Website for web-based interaction.
      • DeepSeek API Platform for programmatic access.
    • LLM Local Platforms, such as:
      • DeepSeek Infer for lightweight deployment.
      • SGLang Implementation for optimized inference.
  • ...
• Examples:
  • Base Model Versions, such as:
    • DeepSeek-V3, during architecture advancement with 671B parameters and MoE architecture.
    • DeepSeek-R1-Zero, during reasoning model release with base capability.
    • DeepSeek-R1, during performance enhancement with cold-start data.
  • Distilled Model Versions, such as:
    • Llama Based Models, such as:
      • DeepSeek-R1-Distill-Llama-8B, during parameter reduction to 8B scale.
      • DeepSeek-R1-Distill-Llama-70B-Instruct, during instruction tuning at 70B scale.
    • Qwen Based Models, such as:
      • DeepSeek-R1-Distill-Qwen-14B, during knowledge distillation at 14B scale.
      • DeepSeek-R1-Distill-Qwen-32B, during capability preservation at 32B scale.
      • DeepSeek-R1-Distill-Qwen-Math-1.5B, during mathematical specialization at 1.5B scale.
      • DeepSeek-R1-Distill-Qwen-Math-7B, during reasoning enhancement at 7B scale.
  • ...
• Counter-Examples:
  • o1 Model.
• See: Language Model Architecture, Neural Network Model, AI Model Deployment, Model Infrastructure.

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