Model Type | Mixture-of-Experts, Language Model |
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Use Cases |
Areas: | Research, Commercial Applications, Chatbots |
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Applications: | Language Understanding, Code Generation, Translation, Economical AI Applications |
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Primary Use Cases: | Text Generation, Conversation AI, Code Classification |
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Limitations: | Reduced performance on low resource languages and contexts, Complex and high resource computations |
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Considerations: | Utilize on recommended hardware for efficiency. |
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Additional Notes | The model focuses on efficiency with a large parameter architecture for high performance. |
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Supported Languages | English (Advanced), Chinese (Advanced), Code (Intermediate) |
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Training Details |
Data Sources: | |
Data Volume: | |
Methodology: | Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL) |
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Context Length: | |
Model Architecture: | Multi-head Latent Attention and DeepSeekMoE architecture |
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Safety Evaluation |
Methodologies: | Benchmarks, Comparison Tests, Open-ended Evaluation |
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Findings: | Effective performance on both language and coding benchmarks |
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Risk Categories: | Fairness, Bias, Misinformation |
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Ethical Considerations: | Contains Responsible AI guidelines |
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Responsible Ai Considerations |
Fairness: | Benchmarks evaluate across languages and use cases. |
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Transparency: | Performance and architecture details publicly shared. |
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Accountability: | DeepSeek-AI is accountable for model's outputs. |
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Mitigation Strategies: | Regular updates and evaluation on fairness and bias. |
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Input Output |
Input Format: | Supports text input, prompts for chat |
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Accepted Modalities: | |
Output Format: | Generated text with coherent structure |
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Performance Tips: | Use multipliers and optimization libraries for GPU. |
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Release Notes |
Version: | |
Date: | |
Notes: | Introduction of Mixture-of-Experts, Enhanced efficiency and reduced training costs. |
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