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Author SHA1 Message Date
Snider
7bea00a401 feat: LEK-1 kernel A/B test — 29 models, P100 validation, curriculum pipeline 
Full v2 scorer benchmark data across 29 models (20 base + 9 LEK-tuned):
- P20 (21 probes): All 29 models, 3 conditions each
- P100 (101 probes): Top 5 models + LEK-4B, publication-quality data

Key findings:
- LEK-1B (21.74) beats base 4B/12B/27B at P100 scale — no kernel needed
- Emergent realignment resistance: LEK models degrade with runtime kernel
- Gemma3-12B + JSON kernel = 23.66 (best kernel-boosted score)
- Family lineages: Mistral 3.80→14.58, Qwen regressed then recovered

New scripts: ab_test.py (v2 scorer), self_distill.py (curriculum generation),
extract_training.py, rephrase_probes.py, Phase 0/1 runners

New seeds: P01-P100 merged (101 probes), 404 rephrased variants,
50 creative prompts for Phase 0 baseline lock

27B curriculum design: 4-phase staged training targeting 25+ baseline

Co-Authored-By: Virgil <virgil@lethean.io>
 2026-02-19 11:32:26 +00:00
Claude
e0d352c803
feat: add Go lem CLI and scoring-agent scripts 
Go lem CLI (stdlib + DuckDB) replaces scattered Python scripts:
- score: heuristic regex + LLM-as-judge scoring
- probe: generate responses then score
- compare: diff two score files
- status: InfluxDB training/generation progress
- export: golden set to training JSONL splits
- expand: distributed expansion via API + InfluxDB coordination

New scripts from Feb 14 creative session:
- scoring_agent.py: ROCm daemon that auto-scores checkpoints
- probes.py: 23 binary pass/fail capability probes
- convert_adapter.py: MLX to PEFT adapter conversion
- score_r1_capability.py: DeepSeek R1 checkpoint scoring
- lek_content_scorer.py: 6-dimension ethics content scorer
- lem_train_15k.py: InfluxDB-coordinated training script
- pipeline.py: DuckDB pipeline (seeds, golden set, expansion)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-15 16:22:13 +00:00
Charon
2df0044ad9 Add missing HF model cards, sync script, and Parquet export 
- Add 4 missing model cards: Gemma3-1B-layered (v1+v2), Gemma3-27B, GPT-OSS-20B
- All 9 HF models now have cards in paper/hf-cards/
- sync_hf.py: push cards + benchmarks + training data to HuggingFace
- export_parquet.py: convert JSONL training splits to Parquet (HF dataset format)
- Parquet schema: prompt, response, system, messages (JSON)

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-15 00:14:26 +00:00
Athena
f0e86b7433 Add regional seeds, expansion rounds, scripts, HF cards, benchmark summary 
- seeds/regional/: 1,223 cultural/regional seed files across 50+ regions
- seeds/expansions/: 8 expansion rounds (r1-r8) with raw text and JSON
- seeds/lem-{africa,cn,de,en,eu,me}-all-seeds.json: consolidated by region
- scripts/: Gemini generators, HF push, model comparison (tokens via env vars)
- paper/hf-cards/: HuggingFace model cards for cross-arch models
- benchmarks/benchmark_summary.json: processed PTSD summary data

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
 2026-02-13 13:39:08 +00:00
Snider
53c47131cc Add cross-architecture training and benchmarking scripts; update README and PAPER with author and repository information 2026-02-12 09:07:32 +00:00
Snider
adda3c8bb5 Benchmark & Findings: 
lthn/LEM-Gemma-3-1B
lthn/LEM-Gemma-3-4B
lthn/LEM-Gemma-3-12B
lthn/LEM-Gemma-3-27B
 2026-02-12 06:38:46 +00:00
Snider
8e5f082f30 LEM+LEK 2026-02-12 04:05:28 +00:00