training-orchestrator

name: training-orchestrator description: Orchestrate personal model training — SFT, DPO, and STT fine-tuning via mlx-tune on Apple Silicon. tags: - training - self-improvement metadata: author: fae version: "2.0"

Training Orchestrator

This skill manages the personal model training pipeline using mlx-tune — a native MLX fine-tuning library with Unsloth-compatible API.

Training Modes

Available Scripts

• export_data: Export conversation episodes to train.jsonl/valid.jsonl (80/20 split)
• train: SFT fine-tuning via mlx-tune SFTTrainer (Qwen3.5 models)
• train_dpo: DPO preference training via mlx-tune DPOTrainer (uses correction pairs)
• train_stt: STT fine-tuning via mlx-tune FastSTTModel (Qwen3-ASR)
• evaluate: Benchmark trained checkpoint — reads train_metrics.json or falls back to log parsing
• propose: Generate human-readable comparison report for the user
• check_status: Check if training is in progress
• deploy: Activate a trained adapter (requires user approval)
• rollback: Revert to previous adapter
• train_keyword: Train 5-class keyword classifier (interrupt/wake/speech/silence/noise)
• train_speech_verifier: Train 3-class speech verifier (speech/music/noise)

Model Map

Auto-selection matches Fae's production LLM model tier.

SFT Workflow

1. Run export_data to prepare training dataset from recent conversations
2. Run train with preset (smoke/light/standard/deep)
3. Run evaluate to benchmark the new checkpoint
4. Run propose to generate upgrade proposal
5. If user approves, run deploy to switch models

DPO Workflow

1. Run training-data-bridge extract_corrections to build DPO pairs
2. Run train_dpo with preset (smoke/light/standard)
3. Run evaluate to benchmark
4. Run propose → deploy if approved

STT Workflow

1. Fae automatically captures ASR corrections ("my name is X not Y")
2. Run train_stt to fine-tune Qwen3-ASR with correction pairs
3. Run evaluate → propose → deploy

Training Presets (SFT)

Safety

• Training data never leaves the Mac — 100% local via MLX
• New models must score ≥ current on evaluation
• User must explicitly approve model switches
• Previous checkpoints always preserved for rollback
• mlx-tune runs in uv sandbox with PEP 723 inline dependencies