See How Intelligence Flows Between Models
   
World's first model knowledge transfer & distillation visualization — holographic view of AI model intelligence
Part of the MYND AI Ecosystem — A 7-layer architecture for next-generation AI agent systems.
MYND Model Holography creates a holographic visualization of knowledge transfer between AI models. When knowledge distills from a large model to a small one, when fine-tuning transfers capabilities, when models ensembles combine — the flow of intelligence is not linear but holographic: every fragment contains patterns of the whole.
This platform visualizes these flows in 3D using Three.js, maps knowledge distillation paths, quantifies information transfer fidelity, and provides the model infrastructure layer for the entire MYND ecosystem.
- 🔮 3D Holographic Visualization — Three.js-powered 3D visualizations of model knowledge spaces and transfer paths
- 📊 Knowledge Transfer Mapping — Visualize how intelligence flows from teacher models to student models during distillation
- 🧬 Distillation Fidelity Metrics — Quantify how much knowledge survives transfer using NumPy, SciPy, and scikit-learn
- 🤖 PyTorch + Transformers Integration — Deep integration with Hugging Face Transformers for model analysis
- 🧠 Vector Holography Engine — Python workers using ChromaDB and sentence-transformers to create holographic knowledge embeddings
- 📈 Plotly Analytics — Server-side plot generation with Kaleido for knowledge distribution charts
- 🎨 SvelteKit + Three.js Frontend — Modern reactive frontend with Tailwind CSS and 3D graphics
- 🗄️ PostgreSQL + Redis — Persistent storage with high-performance caching for model metadata
- 🐍 FastAPI Python Workers — Dedicated Python analysis workers with Numba JIT compilation for performance
- 🐳 Nginx + Docker Production — Production-ready deployment with Nginx reverse proxy and Docker Compose
- 🔐 OAuth2 + JWT Security — Enterprise authentication with @fastify/oauth2
MYND Model Holography operates at Layer 2 — Model Infrastructure:
Layer 7: Application → mynd-platform
Layer 6: Skills → mynd-skill-forge
Layer 5: Decisions → mynd-decision-paleontology, decisionpaleo
Layer 4: Knowledge → mynd-knowledge-paleontology
Layer 3: Cognition → mynd-prompt-phylogeny, phylogeny
Layer 2: Models ██→ THIS PROJECT: Model Holography (model knowledge visualization)
Layer 1: Synchronization→ mynd-synchron
Layer 0: Foundation → mynd-agent-replay
Role: The Model Infrastructure layer provides visibility into how intelligence is distributed, transferred, and preserved across the models that power the MYND ecosystem. All higher layers depend on models — this is how we understand them.
| Component | Technology |
|---|---|
| Frontend | SvelteKit, TypeScript, Three.js, Tailwind CSS, Chart.js |
| Backend (Node) | Fastify, TypeScript, PostgreSQL, Redis, Multipart file upload |
| Workers (Python) | FastAPI, Uvicorn, PyTorch, Transformers, NumPy, SciPy, pandas |
| ML/Analysis | scikit-learn, sentence-transformers, ChromaDB, huggingface-hub |
| Visualization | Three.js (@types/three), Plotly, Kaleido, Chart.js, D3.js |
| Database | PostgreSQL (pg), Redis (ioredis), ChromaDB |
| Auth | OAuth2, JWT, bcrypt/passlib |
| Performance | Numba JIT compilation, joblib parallelization |
| DevOps | Docker, Nginx reverse proxy, Prettier formatting |
- Node.js 18+ and npm
- Python 3.11+ with pip
- Docker and Docker Compose
- GPU recommended for PyTorch analysis (CPU supported)
git clone https://github.com/yethikrishna/mynd-model-holography.git
cd mynd-model-holography
# Install Node dependencies
npm install
# Set up Python workers
cd workers
python -m venv venv
source venv/bin/activate # Windows: venv\Scripts�ctivate
pip install -r requirements.txt
cd ..
# Environment configuration
cp .env.example .env
# Configure DATABASE_URL, REDIS_URL, CHROMADB_URL, HF_TOKEN
# Start services
docker-compose up -d
# Run database migrations
cd backend && npx prisma migrate dev && cd ..
# Start development servers
npm run dev
# Node API: http://localhost:8080
# Python Workers: http://localhost:8000
# Frontend: http://localhost:5173cd workers
source venv/bin/activate
uvicorn src.main:app --reload --host 0.0.0.0 --port 8000
# API docs: http://localhost:8000/docsmynd-model-holography/
├── backend/ # Fastify Node.js API
│ └── src/
│ ├── routes/ # Model registration, analysis, transfer APIs
│ └── services/ # Model metadata, holography orchestration
├── frontend/ # SvelteKit 3D visualization app
│ ├── src/
│ │ ├── lib/
│ │ │ ├── components/ # Three.js holographic viewers
│ │ │ └── three/ # 3D scene management
│ │ └── routes/
│ ├── svelte.config.js
│ ├── tailwind.config.js
│ └── vite.config.js
├── workers/ # Python FastAPI analysis workers
│ ├── src/
│ │ ├── holography/ # Knowledge transfer analysis
│ │ ├── distillation/ # Model distillation metrics
│ │ └── embeddings/ # Sentence transformer embeddings
│ ├── requirements.txt
│ └── Dockerfile
├── database/ # SQL schemas
├── nginx/ # Nginx proxy config
├── docs/ # Documentation
└── docker-compose.yml
| Analysis | Description |
|---|---|
| Knowledge Transfer Visualization | 3D hologram showing which knowledge pathways transfer between models |
| Distillation Fidelity | Measure how accurately knowledge is preserved during distillation |
| Model Similarity Mapping | Spatial visualization of model knowledge overlap and divergence |
| Fine-tuning Impact | Track how fine-tuning reshapes model knowledge holographically |
| Ensemble Holography | Visualize how ensemble models combine holographic knowledge |
| Attention Flow | Map attention patterns as holographic interference patterns |
This project is part of the larger MYND AI Ecosystem. Explore related projects:
| Project | Description |
|---|---|
| mynd-platform | The core MYND AI orchestration platform |
| mynd-plan | Hierarchical planning and task decomposition |
| mynd-agent-replay | Agent execution replay and debugging |
| mynd-model-arena | Model comparison and benchmarking arena |
| mynd-synchron | Real-time multi-agent synchronization |
| mynd-skill-forge | AI skill creation and management platform |
| mynd-decision-paleontology | Decision forensics for AI agents |
| mynd-knowledge-paleontology | Knowledge lineage and origin tracking |
Help us map the holographic structure of AI intelligence!
- Fork the repository
- Create a feature branch
- Add tests for new analysis methods
- Ensure both Node and Python tests pass
- Submit a PR
MIT License — see LICENSE for details.