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name: checkpoints-and-model-ops description: "Operate verl checkpoints, model merging/export, LoRA checkpoint handling, and profiling diagnostics without changing training setup or data schema." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

checkpoints-and-model-ops

Use this sub-skill when a task involves an existing verl run or checkpoint tree: identifying whether a checkpoint is FSDP, Megatron, or already HuggingFace-format; planning verl.model_merger usage; diagnosing save/load contents; handling LoRA export implications; or enabling profiling/performance diagnostics after training is already configured.

Do not use this for initial installation, environment setup, training command assembly, reward/data schemas, or choosing datasets. Route those to the root skill or the training/data sub-skills.

Fast path

1. Inspect the checkpoint directory without loading tensors:

   python sub-skills/checkpoints-and-model-ops/scripts/inspect_checkpoint_path.py CHECKPOINT_DIR --pretty
   

2. If the path is a run root, choose a global_step_* directory and then the role directory, usually actor.
3. If the role directory is FSDP, merge with python -m verl.model_merger merge --backend fsdp --local_dir ROLE_DIR --target_dir TARGET_DIR.
4. If the role directory is Megatron, inspect checkpoint_contents_metadata.json first; merge with --backend megatron only when model shards or HF config artifacts are present as expected.
5. If the role directory already contains a complete HuggingFace tree, prefer copying/validating that tree over re-merging large shards.

References

• Checkpoint layouts, save contents, and model merging
• Performance tuning and profiling operations
• Checkpoint/model-ops troubleshooting

Bundled script

• Safe checkpoint path inspector: directory-only argparse helper that detects likely run roots, step roots, FSDP role directories, Megatron role directories, HuggingFace exports, and next-action suggestions. It does not import verl, torch, or load checkpoint tensors.

Native verification anchors

Relevant upstream CPU or focused tests for future maintainers include checkpoint-engine global-step propagation, checkpoint cleanup retention, Megatron checkpoint manager behavior, and FSDP LoRA merge semantics. Treat GPU-heavy LoRA/FSDP tests as native candidates only when the requested environment has the needed accelerators.