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
post-training-recipesSelect and adapt torchtune post-training recipes without launching expensive jobs accidentally.
Independent GitHub snapshot · Automated classification · Original documentation governs
Use this sub-skill when an agent must choose, customize, or explain a torchtune training recipe for supervised finetuning, LoRA, QLoRA, DoRA, DPO, PPO, KD, QAT, distributed training, or multi-node launch planning.
Do not use it for generation, Eleuther evaluation, post-training quantization conversion, or general CLI/config syntax. Route those to sibling skills.
Training recipes can download gated artifacts, allocate GPUs, start network rendezvous, or consume long cluster jobs. Build and inspect commands first; only run them after the user explicitly approves the hardware, dataset, checkpoint, credential, and output-dir choices.
Use the bundled command builder to construct a safe dry command:
python sub-skills/post-training-recipes/scripts/build_tune_command.py \
lora_finetune_distributed llama3_2/3B_lora \
--nnodes 2 --nproc-per-node 8 \
--rdzv-endpoint '<head-node>:29500' \
--override checkpointer.checkpoint_dir='<checkpoint-dir>' \
--override output_dir='<output-dir>'
The script prints tune run ...; it never executes training.
torchrun, SLURM, rendezvous, and multi-node placement rules, read references/distributed-and-multinode.md.tune ls, tune cp, tune cat, tune validate, and tune run; do not import the recipes package directly, because it intentionally is not an importable API surface.--config.../cli-and-config/SKILL.md for general CLI/config mechanics and OmegaConf override syntax.../data-and-datasets/SKILL.md before changing dataset components or data schemas.../training-utilities-and-rlhf/SKILL.md for checkpointers, precision utilities, logging, profiling, and RLHF utility internals.../inference-evaluation-quantization/SKILL.md after a training checkpoint exists and the task moves to generation, evaluation, or quantization conversion.