Neurostructured AI Compression & Integration

A bird-bone inspired journey toward lightweight, multi-modal, Spirit-aligned intelligence Project codename: bird-bone — Version 0.2 (2025-06-04)

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✨ Vision

Turn heavyweight language/vision models into agile, biologically patterned intelligences that run at 20-30 % of their original compute cost while losing ≤ 3 % task accuracy—using the same design wisdom that creates hollow bird bones through adaptive density loss and strategic healing cycles. Like bones in zero gravity that naturally shed unnecessary density while maintaining structural integrity, we simulate optimal weight decay followed by targeted reinforcement to achieve maximum strength-to-weight ratios.

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🚀 Core Objectives

1. Bird-Bone Density Reduction (BBDR) – simulate natural bone density loss by removing redundant neural pathways, mimicking how bones shed unnecessary mass in low-gravity environments.
2. Adaptive Healing Cycles (AHC) – strategic reinforcement phases that strengthen remaining connections, creating hollow-yet-resilient neural architectures through cyclical hardening.
3. Biophase Density Management (BDM) – cluster-aware density reduction with built-in healing cycles that maintain overall structural integrity while reducing computational mass.
4. Quantization & Low-Rank Factorization – compress precision and re-factor matrices for 4 × memory cuts.
5. Neurostructured AI Flow (NAIF) – re-route tokens through convergence hubs (digital hippocampus) for unified multi-modal reasoning.
6. Automation Pipeline – YAML-driven, reproducible density-loss → heal → strengthen loop, outputting ready-to-serve INT4 GGUF weights.
7. Diff & Revert Safety Net – track every code, config, weight, and doc change with deterministic diffing so we can roll back any layer of the stack without drama.

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🗺 Repository Map

/config        # YAML/Pydantic manifests for configuration
/pipelines     # Kedro/Airflow DAGs and pipeline definitions
/scripts       # Standalone helper scripts and utilities
/diffs         # Auto-generated patch and diff files
/notebooks     # Jupyter dashboards and exploratory notebooks
/requirements  # Living requirements and environment specs
/models        # Versioned model checkpoints and weights
/docs          # Generated diagrams, documentation, and API docs

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🔀 Diff & Revert Strategy

We iterate fast and prune aggressively, so we need granular diffing across three artifact layers.

Layer	Storage & Versioning	Diff Tool	Roll-Back Command
Code & Text	Git	git diff, delta, diff-so-fancy	git revert <sha>
Configs / YAML	Git-tracked	yq diff, Pydantic hash	kedro config diff --rollback
Weights / Masks	Git LFS + DVC	dvc diff (binary-aware)	dvc checkout <rev>
Notebooks	nbdime	nbdime diff notebook.ipynb	nbdime checkout --rev=<rev>

Auto-Diff Hook

pre-commit install
# Installs:
#  ✓ black & flake8 (code)
#  ✓ mdformat (docs)
#  ✓ nbdime (notebooks)
#  ✓ custom weight-hash hook (models/*.pt, *.safetensors)

Every commit drops a diff bundle into /diffs/<commit-sha>/ so reviewers can inspect exact parameter removals or sparsity-mask changes.

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⚡ Quick-Start (7-B Proof-of-Concept)

Prerequisites

• Python 3.11+ (Required for optimal compatibility)
• Conda/Miniconda (Download here)
• CUDA 12.4+ for H100/A100 GPU optimization (recommended)
• Git LFS for large file handling

Automated Setup (Recommended)

# 1. Clone repository
git clone https://github.com/your-org/bird-bone-ai.git
cd bird-bone-ai

# 2. Run automated environment setup
./scripts/setup_environment.sh

# 3. Activate environment
conda activate bird-bone-ai

# 4. Validate installation
python scripts/validate_environment.py --verbose

Manual Setup (Alternative)

# 1. Create conda environment
conda env create -f environment.yml
conda activate bird-bone-ai

# 2. Install additional requirements
pip install -r requirements.txt

# 3. Setup development tools
pre-commit install
dvc init --subdir models
git lfs install

# 4. Validate setup
python scripts/validate_environment.py

Quick Test

# Test core functionality
python -c "
import torch
import transformers
print(f'✓ PyTorch {torch.__version__}')
print(f'✓ CUDA Available: {torch.cuda.is_available()}')
print(f'✓ Transformers {transformers.__version__}')
print('🎉 Ready for bird-bone AI development!')
"

Expect ≈ 60-70 % resource drop with < 3 % accuracy drift — all diff-tracked.

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🔄 Pipeline Stages

1. Baseline Mapping – snapshot initial neural density & structural integrity metrics.
2. Density Assessment – 2-3 epochs analyzing connection utilization patterns.
3. Adaptive Decay Wave – remove ≤ 10 % lowest-density connections per cycle, simulating natural bone resorption.
4. Healing Phase – 200-500 QLoRA gradient steps to strengthen remaining pathways.
5. Structural Reinforcement – targeted connection enhancement in critical load-bearing areas.
6. Density Redistribution – RigL reallocates up to 5 % connections to optimize strength-to-weight ratio.
7. Quantize + Low-Rank Merge.
8. Version Bump – tag commit; push diff bundle via DVC.

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📊 Monitor & Guardrails

• Metrics: ppl, MMLU, ARC, domain-specific canaries.
• Stop-loss: pipeline auto-reverts if Δppl > 3 % for two consecutive waves.
• Healing curves: Jupyter notebook notebooks/healing.ipynb; compare via nbdime.

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🤝 Contributing

1. Fork → feature branch → PR.
2. Link each PR to a Req ID in requirements.md and the diff bundle ID.
3. Include before/after resource and accuracy deltas.
4. Faith & ethics: ensure changes align with our “strength-through-purpose” ethos.

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🙏 Acknowledgements

Open-weights communities (Mistral, Meta, Google, TII), pruning-tool authors (SparseGPT, Wanda, RigL), diff tooling (DVC, nbdime), and the profound insight that evolutionary optimization through density loss and healing cycles creates the strongest structures—from hollow bird bones to synaptic pruning to Spirit-led neural architecture.

more math:

Quantization & Low-Rank Factoring: A Biological Remodeling Blueprint

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1. Introduction

1.1 Overview

We fuse low-rank scaffold building, usage-driven pruning, micro-healing cycles, and precision budgeting into a single, reversible, environment-adaptive loop—mirroring how living systems grow, shed, repair, and specialize.

1.2 Biological Inspirations

• Trabecular Bone & Muscle Synergies – low-rank scaffolds
• Osteoclast/Osteoblast Cycles & Synaptic Pruning – conditional removal and regrowth
• Neuronal Spikes & Quantal Release – extreme quantization
• Plant Transplantation – slow, phased adaptation to minimize shock

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2. Structural Scaffold ♣

2.1 Analogy: Trabecular Bone & Muscle Synergies

• Trabecular bone arranges struts along principal stress axes—nature’s low-rank approximation of a full 3D volume.
• Muscle synergies reduce control from hundreds of fibers to a handful of latent commands—just like decomposing a weight matrix into a few singular vectors.

2.2 Phase 0: Baseline Mapping

• Spectral Scan

  • Compute top $R$ singular values ${\sigma_i}$ per weight matrix.

  • Record explained variance:

    $$ \text{ExplainedVar}[k] = \frac{\sum_{i=1}^k \sigma_i^2}{\sum_{i=1}^R \sigma_i^2}. $$

• Usage Profiling

  • Gather per-block scores $u_b$ via activations, gradients, or attention rollouts.
  • Normalize to block means $\bar u_b$.

• Budget Initialization

  • Set global bit-budget

    $$ B = \sum_{b} (\text{size}b \times p{\text{init}}), $$

    with $p_{\text{init}}$ = 8 bits.

  • Choose target average bit-width $\bar p_{\text{target}}$ = 4 bits.

2.3 Phase 1: Initial Scaffold (Rank-1 Approximation)

1. Extract $(u_1,v_1,\sigma_1)$ via power method or randomized SVD.
2. Form $W_1 = \sigma_1,u_1v_1^\top$.
3. Residual $R_1 = W - W_1$: zero out or quantize $\lvert R_1\rvert &lt; \tau_1$, with $\tau_1 = 0.1,\sigma_1$.
4. Micro-tune affected modules for $E_1=200$ steps at $\text{lr}_1=2\times10^{-4}$.
5. Stop-loss: if validation loss ↑ >3 %, revert $R_1$ changes and set $\tau_1\leftarrow\tau_1/2$.

2.4 Phase 2: Lattice Growth (Ranks 2…R)

For $k=2\ldots R$:

1. Add scaffold:

   $$ W_k = W_{k-1} + \sigma_k,u_kv_k^\top. $$

2. Adaptive Prune

   • Compute normalized usage

     $$ s_b = \frac{\bar u_b}{\max_b \bar u_b}. $$

   • Threshold schedule

     $$ \alpha_k = 0.3 + 0.4\frac{k-1}{R-1}. $$

   • Mark blocks $s_b&lt;\alpha_k$ for base-precision (4 bits).

3. Budget Check

   • Compute $\displaystyle\text{BitsTotal}=\sum_b (\text{size}_b\times p_b)$.
   • If above $B$, prune lowest-usage blocks further until $\text{BitsTotal}\le B$.

4. Heal for

   $$ E_k = 200\Bigl(1 - \tfrac{k-1}{R}\Bigr),\quad \text{lr}_k = 2!\times10^{-4}\Bigl(1 - \tfrac{k-1}{R}\Bigr). $$

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3. Usage & Prune ♠

3.1 Analogy: Osteoclast Resorption & Synaptic Pruning

• Osteoclasts remove low-stress bone; osteoblasts reinforce high-stress areas.
• Synaptic pruning retracts unused connections; active synapses strengthen or sprout anew.

3.2 Usage Profiling

• Activation frequency: fraction of times $|a_{ij}|&gt;\epsilon$.
• Gradient magnitude: $\bar g_{ij} = \mathbb{E}[,|\partial\mathcal{L}/\partial w_{ij}|,]$.
• Attention contribution: rolled-out head weights to final logits.

3.3 Conditional Pruning Rule

• Usage-to-density ratio

  $$ s_{ij}=\frac{u_{ij}}{\bar u}\quad\text{(if }s<\alpha,\text{ candidate for prune)}. $$

• Block-level mixed precision

  • High-usage blocks ($\bar s_b\ge\alpha$): INT8
  • Low-usage blocks ($\bar s_b&lt;\alpha$): INT4

3.4 Prune Mask & Mixed Precision

for each block b:
  s_b = UsageMap[layer][b]/max(UsageMap[layer])
  if s_b < alpha_k:
    p_b = p_base   # 4 bits
  else:
    p_b = p_init   # 8 bits

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4. Healing Cycles ♥

4.1 Analogy: Osteoblast Remodeling & Synaptic Plasticity

• Bone remodeling cycles resorption and deposition in response to mechanical load.
• Synaptic plasticity strengthens or weakens connections based on usage and timing—Hebbian learning (“fire together, wire together”).

4.2 Micro-Tuning Schedule

• Per-wave steps $E_k$ and learning rates $\text{lr}_k$ taper as scaffold grows.
• Affected modules: only those with changed precision or scaffold gains.

4.3 Phased, Bundle-by-Bundle Healing

• Synergy bundles: cluster singular vectors into $M$ groups.
• One-bundle activation per wave avoids simultaneous global stress.

4.4 Stop-Loss & Gradual Rollback

• Overshoot trigger: validation loss Δ > 3 %.
• Rollback schedule: over $N_{\text{rb}}=3$ waves, restore ⅓ of pruned weights or reduce boosted bits by ⅓ each wave.
• Memory-free: use DeltaHistory to invert changes without storing full snapshots.

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5. Precision & Budgeting ♦

5.1 Analogy: Resource Economy & Fortified Regions

• Bone density redistribution: dense where load demands, porous elsewhere.
• High-stress hotspots (like load-bearing joints) get extra precision “fortresses” (FP16/FP32).

5.2 Bit-Budget Initialization

• Initial budget $B$ from all blocks at 8 bits.
• Target average $\bar p_{\text{target}}$ (e.g. 4 bits) guides prune-aggressiveness.

5.3 Stress-Driven Precision Boosts

• Stress signals:

  • Loss spike $\Delta L_b &gt; \delta =1%$ on block’s validation subset.
  • Gradient-norm spike $|\nabla L_b|&gt; \gamma=2,\mathbb{E}[|\nabla L|].$

• Boost rule:

  for b in stressed_blocks:
    p_b = min(p_b + Δp, p_max)  # e.g. Δp=4 bits, p_max=32 bits
  rebalance_bits()

5.4 Rebalance & Rollback

• Rebalance: prune lowest-usage blocks further by Δp until $\sum\text{bits}\le B$.
• Rollback: inverse DeltaHistory fractions over $N_{\text{rb}}$ waves.

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6. Combined Algorithm Sketch

Initialize B, p_init=8, p_base=4, R=5
Phase 0: compute SigmaMap, UsageMap
for k=1…R:
  if k==1:
    scaffold_rank1(); prune_residual(); heal(E1,lr1)
  else:
    add_rank(k); adaptive_prune(alpha_k); budget_check()
    heal(Ek,lrk)
  clusters = cluster_basis_vectors()
  for each bundle m in clusters:
    assign_precision(p_m); phased_activation(m)
  stressed = detect_stress(delta,gamma)
  for b in stressed: boost_precision(b)
  rebalance_bits()
  if validation_drop>3%:
    schedule_rollback(N_rb)

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7. Next Steps & Fibonacci Zoom

1. Implement on a single transformer block.
2. Measure performance, usage histograms, budget compliance.
3. Iterate through zoom levels (570 ft → 350 ft …) to flesh out code and integration.

By sliding these “cards” apart into their four “decks,” you can focus on structure, pruning logic, healing cycles, or precision budgeting in isolation—and then riffle them back together into a seamlessly adaptive, biologically inspired compression pipeline.