# Training

LoRA fine-tuning pipeline for Timmy's sovereign model. No custom harness — just config files for existing tools.

Replaces the `autolora` repo (1,500 lines of custom code → config + `make`).

## Install

```bash
pip install axolotl mlx-lm lm-evaluation-harness pyyaml
```

## Commands

```bash
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
```

## 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.