👑 DeepNarra Paham-Experimental: 3-Tier Awake-Sleep Continuum

The Experimental Continuous Lifelong Learning Engine for Sovereign Edge Models
Recurrent Fast-Weights • Sidecar-QLoRA • MoE Router Gating • 75/25 Replay Consolidation

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🏛️ Executive Summary

Paham-Experimental is the research variant of DeepNarra/Paham specifically designed for decoupled 3-Tier Awake/Sleep continual learning.

Instead of mutating discrete quantized integer weights during inference (which destroys sub-second generation throughput and causes catastrophic forgetting), Paham-Experimental strictly decouples active user interaction from consolidation training across two operational phases and three architectural tiers:

[ ☀️ AWAKE SESSION (Day / Edge Inference) ]
  • Base GGUF runs frozen at peak speed (1,000+ tok/s).
  • Tier 1: Recurrent fast-weights update in forward-pass buffers (S_t, <5 MB).
  • High-value interactions & user corrections queue into the Non-Blocking Buffer.
         │
         ▼ (When system goes idle / plugs into AC power overnight)
[ 🌙 SLEEP SESSION (Night / Background Consolidation) ]
  • Hippocampal Replay: 75% Golden Pre-Training Anchors : 25% Live Interactions.
  • Tier 2: Sidecar-QLoRA (r=8) trains low-rank adapters with Delta = 0.0e+00 init.
  • Tier 3: MoE Router Gating adapts expert routing priors across the 92 sub-experts.
  • Consolidation Merge: Low-rank deltas fold directly into primary representations.
  • Re-Quantization: Compiles a fresh native 1.58-bit Ternary GGUF (model-TQ1_0.gguf).
         │
         ▼
[ ☀️ NEXT AWAKE BOOT ]
  • Model boots directly into the new `model-TQ1_0.gguf` with fresh consolidated knowledge!

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📊 The 3-Tier Continual Learning Stack

Tier	Operational Phase	Mathematical Mechanism	Target Parameters	Memory Cost
Tier 1	☀️ Awake	Recurrent Fast-Weights ($S_t$)	Narra GLA recurrence forward-pass state ($S_{t+1} = \text{decay} \cdot S_t + k_t v_t$) with zero backprop overhead.	< 5 MB
Tier 2	🌙 Sleep	Sidecar-QLoRA ($r=8, \alpha=16$)	Zero-drift floating-point low-rank adaptation ($y = W_{\text{base}}x + \frac{\alpha}{r}BAx$). Starts at $B = 0$.	< 0.5% params
Tier 3	🌙 Sleep	MoE Router Gating	Updates gating softmax prior distributions ($W_{\text{gate}}$) across the 92 sub-experts without altering expert neural bodies.	< 0.1% params
Consolidation	🌙 Sleep	75/25 Replay & Re-Quantization	Replays 75% Golden Anchors : 25% Live Replay Buffer, folds QLoRA deltas, and re-quantizes to native TQ1_0 GGUF.	Zero Drift

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🚀 Autonomous Continual GGUF Trainer (awake and sleep training/)

For autonomous edge continual training without human intervention or external rate limits, use the 3-cell pipeline located in awake and sleep training/Paham_Experimental_GGUF_Trainer.ipynb. Model weights and compiled ternary GGUFs auto-commit directly to this repository (DeepNarra/Paham-Experimental).

📐 Streamlined 3-Cell Architecture:

• Cell 1 (📖 Info & Blueprints): Architectural documentation of the 3-Tier Awake-Sleep Continual Learning engine, 7 cognitive pillars, and zero-degradation laws.
• Cell 2 (⚡ All Code): The complete unified training loop containing setup, SoftRescueSentinel, 7-pillar self-play gym, continuous execution loop, post-task atomic checkpointing, and native ternary GGUF exporter.
• Cell 3 (🚨 Emergency Save to Paham-Experimental): Standalone emergency rescue cell. Can be executed at any moment (mid-loop or manual stop) to safely snapshot weights, compile fresh GGUF, and auto-commit artifacts directly to DeepNarra/Paham-Experimental.

Key Systems in the GGUF Trainer:

1. 7-Pillar Omniversal Cognitive Gym (PahamOmniversalSelfPlayGym):
   • AST Code Sandbox: Generates synthetic Python programs, parses AST grammar trees, executes sandboxed code, and validates runtime stdout.
   • SymPy Formal Math CAS: Solves nonlinear ODEs, multivariable calculus integrals, and linear systems with symbolic verification.
   • 768-D Symplectic Hamiltonian Phase Space: Verifies energy preservation ($\Delta E < 10^{-13}$) under Störmer-Verlet dynamics.
   • Cognitive Psychology & Empathetic De-escalation: Reframes CBT cognitive distortions (catastrophizing, mind-reading, black-and-white thinking) into balanced cognitive appraisals.
   • Epistemic Metacognition: Calibrates confidence thresholds to eliminate hallucinations and flag uncertain outputs.
   • Strategic Planning DAGs: Generates multi-step prerequisite graphs and validates topological execution orders.
2. Zero-Rate-Limit Autonomous Loop:
   • Runs 100% offline and in-memory with native procedural generation.
   • 0 external commercial API dependencies (no OpenAI, no Anthropic, no Google rate limits, no HTTP 429 throttling).
3. 🛟 Non-Destructive Soft Rescue Checkpointer (SoftRescueSentinel):
   • Atomic Two-Phase Commit: Writes state to an ephemeral .tmp file, verifies file integrity and non-zero byte size, and atomically moves (shutil.move) to target .pt.
   • Decoupled Detached CPU Tensors: .detach().cpu().clone().contiguous() avoids GPU memory exhaustion, locks, or partial-write model corruption.
   • Post-Task Auto-Rescue: Automatically snapshots progress after every Awake exploration and after every Sleep consolidation cycle.
4. Zero-Degradation Sentinel (ZeroDegradationSentinel):
   • Pre-sleep baseline anchor loss is benchmarked against golden validation sets.
   • Post-sleep loss is re-evaluated. If any regression occurs, sidecar weights are instantly rolled back to baseline ($\Delta = 0.00\text{e}+00$).

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⚖️ Open-Source Governance & Integrity

• Zero Fake Scores Policy: Paham-Experimental adheres strictly to DeepNarra's governance forbidding synthetic or estimated benchmark percentages.
• Non-Destructive Blending: Guaranteed $\Delta = 0.00\text{e}+00$ numerical drift on initialization.
• License: Permissive Apache License 2.0.

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_{DeepNarra AI Research Ecosystem • Built with Open-Source Integrity}