LATENTROUTE

A from-scratch LLM research prototype with the following implemented pipeline:

• 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

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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

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1) Environment Setup

From project root:

python3 -m venv .venv
source .venv/bin/activate
python -m pip install --upgrade pip
python -m pip install -r requirements.txt

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2) Wikipedia Training Pipeline

New scripts have been added to support full-scale training:

• 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.

Example training:

source .venv/bin/activate
python scripts/train_on_wiki.py --total_steps 5000

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

source .venv/bin/activate
python scripts/run_smoke_test.py

Expected output includes:

• Smoke test passed
• overall_ready: True

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3) Run Unit Tests

source .venv/bin/activate
PYTHONPATH=. pytest -q

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4) Quick Local Hyperparameter Tuning

source .venv/bin/activate
PYTHONPATH=. python - <<'PY'
from src.training.ray_tune_train import run_quick_tune_local

best = run_quick_tune_local(num_samples=2)
print('Best config:', best.config)
print('Best loss:', best.metrics.get('loss'))
print('Best perplexity:', best.metrics.get('perplexity'))
PY

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5) Start Ray Dashboard

source .venv/bin/activate
ray stop --force
ray start --head --dashboard-host=127.0.0.1 --dashboard-port=8265 --disable-usage-stats

Open:

• http://127.0.0.1:8265

Check health:

python - <<'PY'
import urllib.request
for u in ["http://127.0.0.1:8265", "http://127.0.0.1:8265/api/version"]:
    with urllib.request.urlopen(u, timeout=5) as r:
        print(u, r.status)
PY

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6) Build Full Innovation Model + Recheck Connections

source .venv/bin/activate
PYTHONPATH=. python - <<'PY'
from src.training.pipeline import build_full_innovation_model, recheck_pipeline_connections

model = build_full_innovation_model(
    vocab_size=512,
    d_model=256,
    n_layers=2,
    n_heads=8,
    max_seq_len=256,
    n_experts=8,
    d_c=64,
    d_rope=16,
)
report = recheck_pipeline_connections(model)
print(report)
PY

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7) Current Status

Implemented and validated:

• 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

This repository is a research/training core, not yet a full production chat platform (no serving API, chat UI, auth, moderation, persistence, or deployment stack yet).

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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

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9) Cost Reduction Summary (Target)

Component	Standard	With innovations
Embedding memory (50K vocab)	~800 MB	~55 MB (up to -93%)
KV-cache (large-model scenario)	very high	strongly reduced via MLA/HLCR
MoE routing compute	$O(E)$	hierarchical approx. $O(\sqrt{E})$
Effective vocabulary	baseline	can reduce via entropy-weighted merges
Total inference cost	baseline	substantial reduction depending on scale

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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

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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