Capabilities
Production-grade
AI infrastructure
Not a chatbot. An intelligence engine with domain adapters, neural compression, autonomous invention, and self-healing training.
Multi-Domain LoRA
5 specialized adapters — programming, cybersecurity, quantum, fintech, general. 5.2M trainable parameters per domain. Switch with a single API parameter.
Neural Compression
VQ-VAE hierarchical autoencoders compress hidden states at 2x/4x/8x ratios. Process longer contexts with the same memory footprint.
RAG + Document Grounding
FAISS + sentence-transformers retrieve and inject relevant chunks from your documents. Every answer is grounded. Zero hallucination on known material.
Self-Verification Loop
Adaptive router estimates difficulty, routes queries, and self-verifies outputs. Every response is checked before delivery. 100% verification pass rate.
Hardware Agnostic
Runs on Apple Silicon (MPS), NVIDIA (CUDA), or CPU. Auto-detects hardware acceleration. No cloud dependency for inference. Privacy-first.
Distributed Training
Bee Hive enables anyone to contribute compute. Train on MacBook, Linux, Colab, Kaggle. Validated adapters auto-push to HuggingFace Hub.
Autonomous Invention
Evolutionary search discovers novel algorithms — attention mechanisms, compression schemes, state-space models. Bee writes its own improvements.
Self-Healing Training
Monitors gradient health, detects training anomalies, auto-adjusts learning rate, rolls back to stable checkpoints. Training never crashes.
Architecture
Request → Response Pipeline
# Inference Pipeline
User Request
↓
1. RAG Retrieval — FAISS top-k document chunks
2. Context Injection — Inject into system prompt
3. Template Format — SmolLM2-Instruct chat template
4. Domain Adapter — Activate LoRA weights
5. Generate — MPS/CUDA/CPU inference
↓
Log interaction → Feedback → Training mix
↓
Cloud GPU → Train LoRA → Eval vs baseline → Deploy