so8t-moonshot-pipeline

name: so8t_moonshot_pipeline description: Advanced multimodal 'thinking' model pipeline with SO8T Grand Design, stability-constrained evolutionary training (EvoFreeze), and specialized OSINT/Military/Bio data collection.

SO8T Moonshot Pipeline: Technical Authority & Implementation Guide

This skill encapsulates the collective knowledge, architectural standards, and implementation roadmap for the SO8T project. It serves as the primary reference for building, maintaining, and training the SO8ViT multimodal thinking model.

1. Requirements Definition

1.1 Core Objectives

• Quadrality Inference: Enable the model to perform 4-pass thinking cycles (Observation -> Deduction -> Abduction -> Integration).
• Multimodal Reasoning: Seamlessly integrate Vision (ViT) and Audio (Codec-LM) with text-based reasoning chains.
• Stability-First Training: Maintain base model integrity (no catastrophic forgetting) while unfreezing parameters dynamically.
• Specialized Domain Expertise: Master Military, Aerospace, Intelligence, Biology, and Pharmacology through targeted data collection (Moonshot v4).

1.2 Functional Requirements

• SO8T Adapters: Gated residual adapters in the top 1/3 of Transformer layers.
• EvoFreeze-CEM: Evolutionary selection of trainable submodule masks using Cross-Entropy Method.
• Rollback Engine: Automatic reversion to "Stable Anchors" if KL/Drift metrics diverge.
• V4 Fetchers: Automated scrapers for Wikipedia (Specialized), YouTube metadata, and Government White Papers.

1.3 Non-Functional Requirements

• Performance: Sub-1.0s inference impact for adapters.
• Efficiency: Optimized for RTX 4090/3060 environments using Unsloth and explicit VRAM management.
• Safety: Mandatory NSFW/Toxic/Drug detection dataset integration for policy alignment.

2. Technical Specifications

2.1 Model Architecture (Grand Design)

• Top-Third Patching: Adapters only applied to the upper 1/3 of the model's depth to preserve core linguistic features.
• AdapterBank: $L \times 4$ (Layers x Passes) gated residual paths.
• PET Regularization: $\mathcal{L}{PET} = \lambda_p \Delta^2 \alpha{pass} + \lambda_d \Delta^2 \alpha_{depth}$.
• SO8ViT Projector: Linear/MLP bridge with pass-specific conditioning gates.

2.2 Training & Optimization

• GRPO (Group Relative Policy Optimization): Advantage-based RL without a value head, with dual trust-region constraints.
• mHC (Manifold Constraint): $tanh$ or L2-projection of adapter outputs to ensure identity mapping at $t=0$.
• Stability Metrics:
  • Anchor KL: Divergence relative to the base model on "Capability Anchors".
  • Rep-Drift: Cosine distance shift in hidden states for fixed prompts.

2.3 Data Schema

• Think Tags: <think-task>, <think-safety>, <think-policy>, <think-analysis>.
• VSSI Template: Vector State -> Spinor Logic (+/-) -> Quadrality Integration.

3. Implementation Roadmap

Phase 1: D-Refactoring & Base Adapters (Completed)

• Codebase reorganization.
• Implementation of SO8TAdapterBank and basic pet_loss.

Phase 2: EvoFreeze-TRM & Stability (Completed)

• CEM evolutionary mask selection.
• Rollback Engine and Stability Monitor integration.

Phase 3: Improved Moonshot Pipeline v4 (In-Progress)

• Deployment of specialized fetchers (Military, Space, Bio).
• YouTube transcript and Gov Paper aggregation.

Phase 4: SO8ViT Multimodal Integration

• Thinking-aware vision projector.
• Joint SFT on interleaved multimodal-thinking datasets.

Phase 5: Verification & Deployment

• Comprehensive evaluation on Reasoning, Science, and Safety benchmarks.
• Imatrix-protected GGUF/fp16 export.

4. Operational Best Practices

• Safety First: Never skip NSFW/Drug alignment data during SFT.
• Checkpoints: Use 5-minute rolling checkpoints (3 generations) for long runs.
• Logging: Mandatory tqdm and logging in all fetchers/trainers.
• Refactoring: Regularly perform soft-refactoring to maintain PEP8 and type safety.

Created by Antigravity for the SO8T Project.