初始化项目，由ModelHub XC社区提供模型

Model: ayh015/myLightningOPD
Source: Original Platform

slime/utils/debug_utils/display_debug_rollout_data.py

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+# SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+
+import json
+from pathlib import Path
+from types import SimpleNamespace
+from typing import Annotated
+
+import torch
+import typer
+
+from slime.ray.rollout import compute_metrics_from_samples
+from slime.utils.types import Sample
+
+_WHITELIST_KEYS = [
+    "group_index",
+    "index",
+    "prompt",
+    "response",
+    "response_length",
+    "label",
+    "reward",
+    "status",
+    "metadata",
+]
+
+
+def main(
+    # Deliberately make this name consistent with main training arguments
+    load_debug_rollout_data: Annotated[str, typer.Option()],
+    show_metrics: bool = True,
+    show_samples: bool = True,
+    category: list[str] = None,
+):
+    if category is None:
+        category = ["train", "eval"]
+    for rollout_id, path in _get_rollout_dump_paths(load_debug_rollout_data, category):
+        print("-" * 80)
+        print(f"{rollout_id=} {path=}")
+        print("-" * 80)
+
+        pack = torch.load(path)
+        sample_dicts = pack["samples"]
+
+        if show_metrics:
+            # TODO read these configs from dumps
+            args = SimpleNamespace(
+                advantage_estimator="grpo",
+                reward_key=None,
+                log_reward_category=None,
+            )
+            sample_objects = [Sample.from_dict(s) for s in sample_dicts]
+            metrics = compute_metrics_from_samples(args, sample_objects)
+            print("metrics", metrics)
+
+        if show_samples:
+            for sample in sample_dicts:
+                print(json.dumps({k: v for k, v in sample.items() if k in _WHITELIST_KEYS}))
+
+
+def _get_rollout_dump_paths(load_debug_rollout_data: str, categories: list[str]):
+    # may improve later
+    for rollout_id in range(1000):
+        for category in categories:
+            prefix = {
+                "train": "",
+                "eval": "eval_",
+            }[category]
+            path = Path(load_debug_rollout_data.format(rollout_id=f"{prefix}{rollout_id}"))
+            if path.exists():
+                yield rollout_id, path
+
+
+if __name__ == "__main__":
+    """python -m slime.utils.debug_utils.display_debug_rollout_data --load-debug-rollout-data ..."""
+    typer.run(main)