executing-plans
executing-plans
Use when executing an implementation plan in the current session as the implementer yourself — your human partner chose inline execution, or no subagent tool is available
nemoRouter for trlX's optional NVIDIA NeMo/Megatron backend: when to choose it, how configs and checkpoints fit together, and what remains unverified without NeMo/Apex.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill only when the task explicitly depends on the trlX NeMo / Megatron backend, .nemo checkpoints, or the trlX NeMo wrappers.
NeMoPPOTrainer, NeMoILQLTrainer, or NeMoSFTTrainerPPOGPT, ILQLGPT, or SFTGPTdefault_nemo_20b_config, default_nemo_2b_config, or default_nemo_1_3b_configpretrained_model / megatron_cfg wiringtrlx.train PPO / ILQL / SFT / RFT on Accelerate, reward functions, samples/rewards, PEFT, data shapes, or Ray sweeps → ../training/SKILL.mdreferences/workflows.md for setup, conversion, training, inference, and default selection.references/configuration.md for NeMo YAML shape and trlx-to-NeMo config mapping.references/troubleshooting.md for import, Apex/CUDA, parallelism, checkpoint, tokenizer, and memory failures.Source evidence was read from the NeMo-related trainer/model files, NeMo defaults, and docs/examples in the repo, including docs/source/installation.rst, docs/source/api.rst, trlx/models/README.md, examples/llama_nemo/README.md, trlx/data/default_configs.py, trlx/trainer/nemo_*.py, trlx/models/modeling_nemo_*.py, and configs/nemo_configs/*.yaml.