[claude] Implement AutoLoRA continuous improvement loop (#1105) #1118

claude · 2026-03-23T18:18:13Z

claude commented

2026-03-23 18:18:13 +00:00

What this does

Implements the AutoLoRA sovereignty retrain cycle — the weekly pipeline that lets Timmy improve himself without cloud AI in the decision path.

Work → Record trajectories → Export weekly → Filter quality
→ LoRA fine-tune → Load adapter → Model improves → Repeat forever

Components

trajectory_exporter.py — reads session JSONL logs, segments by time gap and turn boundaries into discrete Trajectory objects
quality_filter.py — scores trajectories (multi-step tasks, successful tool calls, decision points, confidence); keeps HIGH/MEDIUM quality only
training_dataset.py — appends filtered trajectories to a chat-format JSONL training file with idempotent deduplication
lora_trainer.py — runs mlx_lm.lora fine-tune and registers the adapter in Ollama; degrades gracefully when mlx-lm unavailable
training_log.py — records per-cycle metrics (iteration, train loss, skill accuracy) to .loop/retrain/training_log.jsonl + .md
retrain.py — orchestrator; CLI with --dry-run, --weeks-ago, --json flags

Automation

Registered in automations.json as:

id: retrain
schedule: weekly_sunday
base model: hermes4-14b

Tests

49 new unit tests covering all components. All pass. Pre-existing failures in test_calm.py/test_round4_fixes.py are unrelated (unhashable dict key bug in calm routes).

Done when

A retrain automation exists
It runs end-to-end without intervention (dry-run verified)
Training log shows iteration count and metrics per cycle

Fixes #1105 ## What this does Implements the AutoLoRA sovereignty retrain cycle — the weekly pipeline that lets Timmy improve himself without cloud AI in the decision path. ``` Work → Record trajectories → Export weekly → Filter quality → LoRA fine-tune → Load adapter → Model improves → Repeat forever ``` ## Components - **`trajectory_exporter.py`** — reads session JSONL logs, segments by time gap and turn boundaries into discrete `Trajectory` objects - **`quality_filter.py`** — scores trajectories (multi-step tasks, successful tool calls, decision points, confidence); keeps HIGH/MEDIUM quality only - **`training_dataset.py`** — appends filtered trajectories to a chat-format JSONL training file with idempotent deduplication - **`lora_trainer.py`** — runs `mlx_lm.lora` fine-tune and registers the adapter in Ollama; degrades gracefully when mlx-lm unavailable - **`training_log.py`** — records per-cycle metrics (iteration, train loss, skill accuracy) to `.loop/retrain/training_log.jsonl` + `.md` - **`retrain.py`** — orchestrator; CLI with `--dry-run`, `--weeks-ago`, `--json` flags ## Automation Registered in `automations.json` as: - id: `retrain` - schedule: `weekly_sunday` - base model: `hermes4-14b` ## Tests 49 new unit tests covering all components. All pass. Pre-existing failures in `test_calm.py`/`test_round4_fixes.py` are unrelated (unhashable dict key bug in calm routes). ## Done when - [x] A retrain automation exists - [x] It runs end-to-end without intervention (dry-run verified) - [x] Training log shows iteration count and metrics per cycle

claude added 1 commit 2026-03-23 18:18:14 +00:00

feat: implement AutoLoRA continuous improvement loop (#1105 )

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Details

283ec330c8

Adds the weekly sovereignty retrain cycle — Timmy improves himself
without cloud AI in the decision path.

Components:
- `timmy_automations/retrain/trajectory_exporter.py` — reads session
  JSONL logs and segments them into discrete conversation trajectories
- `timmy_automations/retrain/quality_filter.py` — scores trajectories
  (multi-step tasks, successful tool calls, decision points) and keeps
  only HIGH/MEDIUM quality examples
- `timmy_automations/retrain/training_dataset.py` — appends filtered
  trajectories to a chat-format JSONL training file with deduplication
- `timmy_automations/retrain/lora_trainer.py` — triggers mlx_lm.lora
  fine-tune and registers the resulting adapter in Ollama; degrades
  gracefully when mlx-lm is unavailable
- `timmy_automations/retrain/training_log.py` — records per-cycle
  metrics (iteration count, train loss, skill accuracy) to JSONL + MD
- `timmy_automations/retrain/retrain.py` — main orchestrator; runs the
  full pipeline and is registered as a `weekly_sunday` automation

Automation registered in `automations.json` as `retrain` with
`schedule: weekly_sunday` pointing to `hermes4-14b` as the base model.

49 new unit tests covering all components.

Fixes #1105

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

claude referenced this pull request

2026-03-23 18:18:22 +00:00

[AutoLoRA P2] Set Up Continuous Improvement Loop #1105

claude merged commit 1be1324a0d into main

2026-03-23 18:18:33 +00:00

claude deleted branch claude/issue-1105

2026-03-23 18:18:33 +00:00

claude referenced this issue from a commit

2026-03-23 18:18:34 +00:00

[claude] Implement AutoLoRA continuous improvement loop (#1105) (#1118)

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