2 commits

Author	SHA1	Message	Date
rUv	385eb17d08	feat(training): ADR-129 RuvLTRA training pipeline — calibration, SFT, benchmarks, HF publishing ... * docs(adr): update ADR-129 — all phases executing, Phase 4 publishing complete - Phase 1 Calibration: Complete (all 4 models, benchmarks uploaded to HF) - Phase 2 SFT: Executing on L4 GPU (rank-16, 2 epochs) - Phase 3 Benchmarks: Executing (release gates + L4 benchmark job) - Phase 4 Publishing: Complete (TQ configs + benchmarks + README updates on HF) Benchmark results (L4 GPU): - ruvltra-small: 75.4 tok/s - ruvltra-medium: 62.6 tok/s - ruvltra-claude-code: 67.1 tok/s Co-Authored-By: claude-flow <ruv@ruv.net> * docs: add training pipeline and release gates to root README Add Continuous Training & Optimization section (ADR-129) to the capabilities table: nightly training, 7-gate release checks, TurboQuant profiling, training corpus. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(training): include training corpus in Docker build context The SFT job failed because merged_corpus.jsonl was not in the Docker image. Copy it to scripts/training/data/training/ so it's included in the COPY . /app/ step. Co-Authored-By: claude-flow <ruv@ruv.net> * fix(training): handle raw text corpus format in SFT pipeline The training corpus uses a flat 'text' field (brain memories, ADRs) rather than chat messages or Alpaca instruction format. Add handler that converts raw text to completion-style messages for SFT. Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-30 07:58:07 -04:00
rUv	f12e6c1584	feat: implement ADR-129 training pipeline and TurboQuant sidecar infra ... Training tooling: - release_gate.py: Automated 7-gate ship/no-ship checker (G1-G7) - export_training_data.py: Dataset export with governance (schema, dedup, quality scoring, contamination check) - contamination_check.py: 13-gram eval contamination detection - run_calibration.py: Phase 1 imatrix + TurboQuant profiling - run_sft.py: Phase 2 LoRA SFT + DPO training - deploy_training.sh: Cloud Run job creation + Vertex AI setup - Dockerfile: GPU training image (transformers + peft + trl) Rust infrastructure: - turboquant_profile.rs: .turboquant.json sidecar config loading, per-layer TQ config discovery, default profiles Ref: ADR-129, #310 Co-Authored-By: claude-flow <ruv@ruv.net>	2026-03-28 02:27:32 +00:00