examples/train_tiny_lm.py

import argparse
import json
import torch
from celestis_rl.training import TrainConfig, run_tiny_lm


def main():
    p = argparse.ArgumentParser(description="Offline scratch causal Transformer smoke training")
    p.add_argument("--steps", type=int, default=100); p.add_argument("--seed", type=int, default=7)
    p.add_argument("--dense", action="store_true"); p.add_argument("--output", default="runs/tiny_lm")
    p.add_argument("--mode",default="stratified_safe",choices=["iid","safe","trace","full","stratified_safe"])
    p.add_argument("--head-backend",default="recomputed",choices=["recomputed","legacy","exact_streaming"])
    args = p.parse_args(); torch.set_num_threads(1)
    cfg = TrainConfig(seed=args.seed, steps=args.steps, batch_size=16, learning_rate=0.002,
                      budget=8, replay_capacity=256, mode=args.mode)
    r = run_tiny_lm(cfg, selected_backward=not args.dense, head_backend=args.head_backend, output=args.output)
    print(json.dumps({k: v for k, v in r.items() if k != "history"}, indent=2))


if __name__ == "__main__": main()