| .PHONY: setup smoke collect build-data distill train-s1 calibrate eval eval-v2 eval-25 eval-cpu fetch-games agent-local push-hf test clean | |
| PY ?= python3.13 | |
| setup: ## create venv and install deps (Python 3.12+ required) | |
| $(PY) -m venv .venv | |
| .venv/bin/pip install -U pip | |
| .venv/bin/pip install -r requirements.txt | |
| @echo ">> done. activate with: source .venv/bin/activate" | |
| smoke: ## FULL local bring-up on the built-in mock game (no GPU, no downloads) | |
| $(PY) data/collect_selfplay.py --config configs/collect.yaml | |
| $(PY) data/build_dataset.py --in data/traces --out data/dataset | |
| $(PY) train/distill_alignment.py --config configs/train.yaml | |
| $(PY) train/train_system1.py --config configs/train.yaml | |
| $(PY) train/calibrate_rlcd.py --config configs/train.yaml | |
| $(PY) train/evaluate_local.py --config configs/train.yaml --games mock-pushbox | |
| collect: ## self-play trace collection (mock game or public games) | |
| $(PY) data/collect_selfplay.py --config configs/collect.yaml | |
| build-data: ## traces -> decision-question training samples | |
| $(PY) data/build_dataset.py --in data/traces --out data/dataset | |
| distill: ## stage A: align student (image+text) states with the frozen teacher's vision tower | |
| $(PY) train/distill_alignment.py --config configs/train.yaml | |
| train-s1: ## stage B: train System-1 decision heads (choice CE, NanoJev recipe) | |
| $(PY) train/train_system1.py --config configs/train.yaml | |
| calibrate: ## stage C: RLCD calibration + temperature fitting (absorbed from Laya) | |
| $(PY) train/calibrate_rlcd.py --config configs/train.yaml | |
| eval: ## evaluate the hybrid agent (mock game by default) | |
| $(PY) train/evaluate_local.py --config configs/train.yaml --games mock-pushbox | |
| eval-v2: ## one-command eval of the released v2 checkpoint (auto backend; see ops/run_eval.py) | |
| $(PY) ops/run_eval.py --checkpoint hf:tchbcb/qwenjev@t4_assets_v2/checkpoints --games mock | |
| eval-25: ## evaluate on the 25 public games (needs arc-agi + environment_files, see train.md §4.1) | |
| $(PY) ops/run_eval.py --checkpoint hf:tchbcb/qwenjev@t4_assets_v2/checkpoints --games all25 | |
| eval-cpu: ## CPU harness test (tiny backend, no checkpoint, 60 actions) | |
| $(PY) ops/run_eval.py --backend tiny --no-checkpoint --games mock --max-actions 60 | |
| fetch-games: ## cache all 25 public games locally (anonymous arcade key, ~3MB, no Kaggle account) | |
| $(PY) ops/fetch_games.py | |
| agent-local: ## run the hybrid agent locally via the ARC-AGI-3-Agents runner | |
| $(PY) ../ARC-AGI-3-Agents/main.py --agent=qwenjevagent --game=ls20 | |
| push-hf: ## push this repo to HuggingFace (HF_TOKEN from env or .secrets) | |
| $(PY) scripts/push_hf.py | |
| test: ## unit tests (CPU-only, no downloads) | |
| $(PY) -m pytest tests -q | |
| clean: | |
| rm -rf __pycache__ .pytest_cache build data/traces data/dataset eval_report.json | |