Files

perplexity 6507cffc15 feat: migrate autolora pipeline into training/

Per direction shift (the-nexus#542).

Replaces the autolora repo (1,500 lines of custom pipeline code)
with config files for existing tools:

- axolotl.yaml: replaces train_modal.py (239 lines)
- mlx-lora.yaml: replaces MLX training scripts
- eval-tasks.yaml: replaces run_eval.py (300 lines)
- Makefile: replaces run_vibes.py, compare.py, convert_to_mlx.py

Data migrated as-is:
- curated_dataset.jsonl (26 gold-standard conversations)
- preference_pairs.jsonl (DPO pairs)
- prompts_vibes.yaml, prompts_nexus_vibes.yaml
- v0-baseline eval results (historical record)

Thin glue kept:
- build_curated.py (data authoring, not infrastructure)
- ingest_trajectories.py (domain-specific quality filter)

Dependencies: pip install axolotl mlx-lm lm-evaluation-harness

2026-03-25 23:05:50 +00:00

2.5 KiB

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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

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

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.

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