2nd Report Of March 2025

March 21, 2025

Mainnet Performance

  • Validator Nodes: 105

  • Miner Nodes: 1,182

  • New Wallets: +44


Project Updates

Large Model Initialization

  • Optimization of Morpheus as the Base Large Model for AI Agents: 50%

    • Optimized computational efficiency to reduce latency in training and inference while enhancing large-scale task processing capabilities.

    • Introduced distributed training mechanisms to ensure Morpheus operates efficiently across multiple GPU/TPU nodes, increasing computational throughput.

    • Completed model compression and quantization to reduce memory usage, ensuring efficient performance on resource-constrained devices.

    • Conducted performance testing and fine-tuning to ensure stability across diverse hardware platforms.

  • Language Understanding, Reasoning, and Communication Abilities: 50%

  • Enhanced Features: Introduction of foundational personality modules (logical, emotional, creative) for personalized agent development: 5%

  • Integration of DeepSeek V3's MOE Model Framework: 50%

    • Completed large-scale distributed training of the MOE model to handle vast datasets.

    • Implemented MOE model applications in multi-task learning, supporting expert selection for different tasks.

    • Conducted large-scale dataset performance evaluation, balancing training speed and accuracy.

Support for Distributed Privacy Computing (Paused) Agent Model Center (MVP)

  • Initial Version Deployment: 39%

    • Developed AI Agent training tools, including data preprocessing, training monitoring, and optimization strategies.

    • Provided custom hyperparameter tuning to help users optimize training and model performance.

    • Integrated automated training and model evaluation features for streamlined debugging and validation.

    • Implemented parallel training mechanisms to support large-scale training tasks efficiently.

  • Extension of Compatible Baseline Models: 23%

    • Enhanced training workflows for DeepSeek V3 and other baseline models, optimizing data preprocessing, training configurations, and model tuning.

    • Developed automated model deployment features to simplify the production launch process.

    • Introduced real-time monitoring and feedback mechanisms to track model performance during training.

    • Improved inference efficiency for deployed models, ensuring high-performance operation in production environments.

AI Agent MVP

  • Web 3.0 Mentor MVP: First version completed

  • Second MVP: Under research

Launch of Brainwave Distributed Database

  • Deployment of NeuraMATRIX’s first ecosystem application, initiating data collection and anonymization processes: 17%

Others: Submitted listing application to Bitfinex

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