LATENTROUTE

• Entropy-weighted BPE tokenizer (AMT-style)
• Factorized embeddings (FED / FED-Dk)
• RoPE + ARFS positional encoding
• MLA attention with HLCR-style latent compression/gating
• MoE FFN with hierarchical routing + entropy/load-balance loss
• RMSNorm pre-norm transformer blocks
• Training utilities (total loss, AdamW, cosine warmup, Ray Tune)
• Smoke tests and pipeline readiness checks

Repository Layout

• src/tokenizer — Standard BPE + entropy-weighted BPE
• src/embedding — FED, FED-Dk, RoPE, ARFS
• src/model — Transformer stack (MLA + MoE + RMSNorm)
• src/training — Objectives, optimizer/scheduler, Ray Tune, pipeline registry/recheck
• tests — Pytest smoke tests
• scripts — Standalone smoke-test runner

1) Environment Setup

2) Wikipedia Training Pipeline

• scripts/prepare_wiki.py: Extracts Wikipedia articles into JSONL format (streaming).
• scripts/train_on_wiki.py: Trains the LatentRoute model on Wikipedia with all innovations enabled.
• src/model/hf_utils.py: Uploads your trained model to the Hugging Face Hub.

2) Run Smoke Test (End-to-End)

• Smoke test passed
• overall_ready: True

3) Run Unit Tests

4) Quick Local Hyperparameter Tuning

5) Start Ray Dashboard

• http://127.0.0.1:8265

6) Build Full Innovation Model + Recheck Connections

7) Current Status

• Tokenization, embedding, positional, attention, FFN/MoE, model integration
• Total training loss components:
  • L_CE
  • lambda_1 * L_route
  • lambda_2 * L_aux
• Optimizer and scheduler utilities
• Ray Tune local quick tuning
• Ray dashboard operational

8) Innovation Registry

Innovation #1 — Adaptive Morphological Tokenizer (AMT)

• Phase: Tokenization (Phase 2)
• Formula:
  • $P_{merge}(a,b)=freq(a,b)\cdot \exp\left(-H\left(\frac{p(c|ab)}{p(c|a)p(c|b)}\right)\right)$
• Impact: ~18% vocabulary reduction, better rare-word handling
• Patent basis: entropy-weighted BPE merge criterion
• Code location: src/tokenizer/bpe_entropy_weighted.py

Innovation #2 — Factorized Embedding Decomposition with Dynamic k (FED-Dk)

• Phase: Embedding (Phase 3)
• Formula:
  • $k_i = k_{min} + (k_{max}-k_{min})\cdot \sigma(\alpha\log(freq_i))$
• Impact: up to ~93% embedding memory reduction
• Patent basis: per-token adaptive bottleneck dimension
• Code location: src/embedding/fed_dk.py

Innovation #3 — Adaptive RoPE with Learned Frequency Scaling (ARFS)

• Phase: Positional Encoding (Phase 4)
• Formula:
  • $\theta_j^{(domain)} = \theta_j\cdot \exp(\gamma_j z_{domain})$
• Impact: better cross-domain context extension without full retraining
• Patent basis: domain-conditioned rotary scaling
• Code location: src/embedding/rope.py

Innovation #4 — Hierarchical Latent Compression with Residual Gating (HLCR)

• Phase: Attention / MLA (Phase 6)
• Formula:
  • $g=\sigma(W_g[c_1;c_2;h])$
  • $c_{final}=g\cdot c_1 + (1-g)\cdot proj(c_2)$
• Impact: major KV-cache reduction with adaptive per-token compression
• Patent basis: two-level latent KV compression with learned gating
• Code location: src/model/init.py

Innovation #5 — Hierarchical MoE Routing with Entropy Regularization

• Phase: MoE (Phase 7)
• Formula:
  • $L_{entropy}=-\lambda\sum_e \bar{p}_e\log(\bar{p}_e)$
• Impact: reduced routing compute and improved expert utilization
• Patent basis: hierarchical $O(\sqrt{E})$ routing + entropy regularization
• Code location: src/model/init.py

9) Cost Reduction Summary (Target)

10) Recommended Implementation Roadmap

1. Month 1-2: 1B-scale prototype validation (quality + stability)
2. Month 3-4: 7B tuning with Ray Tune (quality/cost Pareto)
3. Month 5-6: long-context + preference alignment
4. Month 7-8: scale-up, optimization hardening, and IP/legal packaging

11) Useful Entry Points

• src/model/__init__.py:
  • LLM, TransformerLM, MLAAttention, MoELayer
• src/training/objectives.py:
  • compute_language_model_loss
• src/training/optim.py:
  • create_adamw, CosineWithWarmup
• src/training/ray_tune_train.py:
  • run_quick_tune_local, build_tuner, train_llm_ray
• src/training/pipeline.py:
  • innovation registry, cost summary, connection recheck