timmy-config/training

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)
├── build_crisis_manipulation_edge_cases.py ← Deterministic 500-pair crisis edge-case builder
├── ingest_trajectories.py                 ← Quality filter for heartbeat cycle data
└── data/
    ├── curated_dataset.jsonl                     ← 26 gold-standard conversations (proprietary)
    ├── crisis_manipulation_edge_cases_500.jsonl ← 500 prompt→response pairs for manipulation/boundary cases
    ├── 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.