计划执行与检查节点
executing-plans
按既定实施计划完成工作,保持过程检查和反馈。
v1-experimental【效率与协作】Use for LlamaFactory experimental v1 flows selected with USE_V1=1, including v1 trainer architecture, v1 config parsing, v1 core engines, samplers, plugin configs, and migration caveats from v0 configs.
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Use this sub-skill only when the user explicitly asks for LlamaFactory v1, USE_V1=1, v1 examples/tests, v1 plugin configs, or the experimental trainer/core architecture.
USE_V1=1 is required before llamafactory-cli or lmf.sft, train, rm, chat, and merge under the v1 launcher.model, model_class, train_dataset, micro_batch_size, global_batch_size, peft_config, dist_config, kernel_config, quant_config, init_config, sample_backend, and v1 batching keys.DataEngine, ModelEngine, BaseTrainer, SFTTrainer, RMTrainer, BaseSampler, plugin registry behavior, FSDP2/DeepSpeed plugin configs, and rendering plugins.USE_V1=1: use training-and-configs.dataset_info.json, data templates, packing, and multimodal schemas: use data-and-templates.model-loading-and-export.inference-and-serving.USE_V1=1 is intended if the request mixes v1 examples with v0 fields.references/v1-architecture.md to identify the launcher route, supported commands, and core engine ownership.references/v1-configs.md before editing YAML/JSON because v1 uses a smaller plugin-oriented config surface than v0.scripts/check_v1_config_keys.py CONFIG.yaml for static mixed-key warnings before proposing a v1 run.references/troubleshooting.md for v1-specific parser, plugin, kernel, distributed, and dependency failures.scripts/check_v1_config_keys.py statically inspects YAML or JSON configs without importing LlamaFactory. It reports likely v0-only keys, v1-only keys, missing plugin name fields, unsupported enum-like values, and command hints.