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Resources: Gnom AI Technical Details

This section provides in-depth technical information about Gnom AI's architecture, features, and implementation.

Core Architecture Specifications​

Model Dimensions​

  • Parameters: 88B
  • Layers: 32
  • Embedding Size: 6,144

Attention Mechanism​

  • Query Heads: 48
  • Key/Value Heads: 8

Tokenization​

  • Tokenizer: SentencePiece
  • Vocabulary Size: 131,072 tokens

Advanced Features​

Mixture of Experts (MoE)​

  • 8 Expert Networks
  • 1 expert utilized per token for efficient processing

Position Encoding​

  • Rotary embeddings (RoPE) for enhanced sequence understanding

Optimization Techniques​

  • Activation sharding
  • 8-bit quantization capabilities

Implementation Stack​

Backend​

  • Go

Client​

  • Node.js

Performance Considerations​

While specific benchmarks are proprietary, Gnom AI is optimized for:

  • Rapid processing of complex queries
  • Efficient handling of long-form content
  • Adaptive learning for improved relevance over time

Development Resources​

Future Developments​

Our roadmap includes:

  • Continuous model refinement and parameter optimization
  • Exploration of advanced AI techniques and architectures
  • Ongoing improvements to personalization and adaptive learning capabilities

For developers and researchers interested in leveraging or contributing to Gnom AI, please refer to our detailed API documentation and contribution guidelines.