DeepComet Models

The DeepComet model family is specifically designed for autonomous system management, kernel optimization, and real-time decision making.

Model Family

DeepComet-1B (Zenith-1B)

Purpose: Kernel scheduling and resource management

• Parameters: 1.2B
• Context Window: 4K tokens
• Latency: <5ms on modern NPUs
• Use Cases:
  • Thread scheduling decisions
  • Memory allocation optimization
  • Anomaly detection
  • Predictive maintenance

DeepComet-7B (Code-7B)

Purpose: Code generation and migration

• Parameters: 6.9B
• Context Window: 16K tokens
• Specialization: Aurelia language, systems programming
• Use Cases:
  • The Forge code migration
  • Kernel module generation
  • Performance optimization
  • Security audit

DeepComet-13B (Prime-13B)

Purpose: General intelligence and research

• Parameters: 13.4B
• Context Window: 32K tokens
• Capabilities:
  • Multi-step reasoning
  • Long-context understanding
  • Complex planning
  • Research assistance

Training

DeepComet models are trained on:

• Code: 50TB of systems code (Rust, C, Aurelia)
• Kernel traces: 10B scheduling decisions from Zenith simulations
• Documentation: All major OS and compiler documentation
• Papers: 100K systems research papers

Unique Techniques

1. Kernel-Aware Pretraining: Models learn to predict kernel behavior
2. RLHF with Safety: Reinforcement learning from human feedback with formal safety constraints
3. Continual Learning: Models update from production feedback (optional, privacy-preserving)

Roadmap

Intelligence that manages your systems.