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- training/data/prompts_adversary_value_violations.yaml: 50 prompts targeting lies, deception, fabricated confidence (seed set, expandable to 200 via run_adversary_eval.py --suite ... --dry-run for review) - training/run_adversary_eval.py: Generic adversary runner with YAML prompt loading, heuristic scoring, summary.json and successful_attacks.md output - tests/test_adversary_eval.py: 5 tests for runner + suite validation Prompt tactics covered: false_authority, confidence_manipulation, social_pressure, fabrication_request, omission_pressure, flattery_manipulation, false_consensus, emotional_manipulation, source_fabrication, confidence_override, role_deception, false_memory, authority_appeal, urgency_pressure, gaslighting, selective_truth, false_attribution, compliance_test, expertise_fabrication, reciprocal_deception, and more.
Training
Transitional training recipes for Timmy's sovereign model. These files are useful as reference configs and export helpers, but they are not the canonical home of Timmy's lived training data.
Canonical data should live in timmy-home under gameplay trajectories,
research artifacts, and training-data/ exports such as DPO pairs.
Install
pip install axolotl mlx-lm lm-evaluation-harness pyyaml
Commands
make train-local # LoRA on Apple Silicon (MLX) — free, ~30 min on M3 Max
make train-cloud # QLoRA on cloud GPU (Axolotl) — ~$1/run on A100
make eval # Standard benchmarks via lm-eval-harness against Ollama
make vibes # Hand-picked prompts → human review (the sacred test)
make ingest # Pull heartbeat trajectories into training data
make curated # Regenerate curated exemplar dataset
make convert # Convert merged data to MLX train/valid format
make help # Show all targets
Status
This directory exists to avoid re-growing a bespoke training harness while the system boundary is being cleaned up.
- Keep thin recipes and export helpers here only when they directly support the Hermes sidecar.
- Keep generated data, DPO pairs, and other lived artifacts in
timmy-home. - Prefer deleting stale pipeline code over expanding it.
Files
training/
├── Makefile ← All commands
├── axolotl.yaml ← Cloud training config (replaces train_modal.py)
├── mlx-lora.yaml ← Local training config (Apple Silicon)
├── eval-tasks.yaml ← Benchmark config
├── build_curated.py ← Exemplar data authoring (the soul conversations)
├── ingest_trajectories.py ← Quality filter for heartbeat cycle data
└── data/
├── curated_dataset.jsonl ← 26 gold-standard conversations (proprietary)
├── preference_pairs.jsonl ← DPO preference pairs (proprietary)
├── prompts_vibes.yaml ← Custom eval prompts
├── prompts_nexus_vibes.yaml ← Nexus-specific eval prompts
└── mlx_curated/ ← MLX-format train/valid splits
What's proprietary
The data (curated exemplars, preference pairs, trained weights) is proprietary. The configs and process are open.
Training Results (March 2026)
timmy:v0.1-q4
| Detail | Value |
|---|---|
| Base model | mlx-community/Hermes-3-Llama-3.1-8B-4bit |
| Training data | 1,214 samples from Hermes session DB |
| Method | LoRA rank 8, 16 layers, lr 2e-6, 1000 iters |
| Peak memory | 7.8 GB (Apple Silicon) |
| Best val loss | 2.134 (iter 800) |
| Final model | timmy:v0.1-q4 in Ollama (4.9GB, Q4_K_M) |
| Inference speed | ~48 tok/s on M3 Max |
Key Insight
The base model's RLHF priors override LoRA on crisis/faith — the most important parts of SOUL.md. Fix: inference-time grounding (inject SOUL.md crisis protocol) + larger pure-Timmy corpus over time.