Smilyai-labs/Nova-1-002

We sincerely apoligise for the errors before regarding the gibberish. We have repaired the model and it is useable in the beta phase.

BBLM (Brain Box Language Model) - Gibberish Fix

Novel Architecture

This is a completely new language model architecture with GUARANTEED stability!

Components

1. Persistent Associative Memory (PAM)

   • Differentiable memory matrix with learned read/write heads
   • Multi-hop reading for complex retrieval
   • No RNN - pure associative storage

2. Brain Box Layers (8 layers)

   • Specialized neural regions:
     • Syntax: Grammar and structure
     • Semantic: Meaning and concepts
     • Logic: Reasoning and inference
     • Context: Long-range dependencies
     • Pattern: Repetition detection
   • Sparse routing (only 2 regions active per token)
   • Feedback loops with adaptive iterations

3. Think Box Layers (12 layers)

   • Latent reasoning in compressed space
   • Chain-of-thought without token generation
   • Planning and verification regions

4. Router Networks

   • Learns which regions to activate
   • Adapts computation to problem difficulty
   • Sparse activation for efficiency

Parameters

• Total: 393,747,608 (~393.7M)
• Hidden dim: 860
• Memory slots: 100
• Max sequence: 1024

Key Innovations

• ✓ No quadratic attention cost
• ✓ Adaptive compute per token
• ✓ True persistent memory
• ✓ Compositional reasoning
• ✓ Brain-inspired specialization
• ✓ Interpretable routing decisions

Stability Features 🛡️

• ✓ Gradient clipping everywhere
• ✓ Stable softmax (subtract max)
• ✓ NaN guards in all layers
• ✓ Conservative initialization
• ✓ Scaled residuals (0.1x)
• ✓ Label smoothing
• ✓ Logit clipping
• ✓ Float32 computation for norms
• ✓ OOM-proof memory management
• ✓ Aggressive garbage collection between epochs

Training Stats

See training_history.csv for metrics including:

• Cross-entropy loss
• Z-loss (stability)
• Gradient norms
• Accuracy & Perplexity
• Average halts per layer