DeepSeek R1 Enhanced Version Delivers Major Efficiency Gains

German technology consulting firm TNG has unveiled the DeepSeek-TNG-R1T2-Chimera, an enhanced version of the DeepSeek model that marks a significant leap in deep learning performance. The new version demonstrates 200% higher inference efficiency while reducing operational costs through its innovative Adaptive Expert (AoE) architecture.

Hybrid Model Architecture

The Chimera version combines three DeepSeek models (R1-0528, R1, and V3-0324) using a novel AoE architecture that refines the traditional mixture-of-experts (MoE) approach. This optimization allows for more efficient parameter usage, enhancing performance while conserving token output.

Benchmark tests including MTBench and AIME-2024 show the Chimera version outperforming standard R1 models in both reasoning capability and cost-efficiency.

MoE Architecture Advantages

The AoE architecture builds upon MoE principles, where Transformer feed-forward layers are divided into specialized "experts." Each input token routes to only a subset of these experts, dramatically improving model efficiency. For example, Mistral's Mixtral-8x7B model demonstrates this principle by matching the performance of larger models while activating far fewer parameters.

The AoE approach takes this further by enabling researchers to:

• Create specialized sub-models from existing MoE frameworks
• Interpolate and selectively merge parent model weights
• Adjust performance characteristics dynamically

Technical Implementation

Researchers developed the new model through careful weight tensor manipulation:

1. Prepared parent model weight tensors through direct file parsing
2. Defined weight coefficients for smooth feature interpolation
3. Implemented threshold controls and difference filtering to reduce complexity
4. Optimized routing expert tensors to enhance sub-model reasoning

The team used PyTorch to implement the merging process, saving optimized weights to create the final high-efficiency sub-model.

The enhanced DeepSeek model is now available as open source at Hugging Face.

Key Points:

• 200% inference efficiency improvement over previous versions
• Significant cost reduction through AoE architecture
• Outperforms standard models in MTBench and AIME-2024 benchmarks
• Builds upon MoE principles with enhanced weight merging techniques
• Open source availability promotes wider adoption and research