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name: data-model-customization description: "Customize ms-swift datasets, registries, plugins, models, and templates safely." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

Data and Model Customization

Use this sub-skill when the task is to prepare or validate custom data, map columns, inspect ms-swift registries, register datasets/models/templates through an external plugin, or reason about model/template selection before training or inference.

Read First

• Data formats: standard messages rows, --columns, SFT/RLHF/multimodal/agent fields, and preprocessor selection.
• Registry and plugins: --custom_dataset_info, --external_plugins, register_dataset, register_model, register_template, and safe plugin structure.
• Template reference: get_processor, get_template, template/model selection, agent templates, loss scaling, and offline skeleton checks.
• Troubleshooting: row drops, malformed messages, media problems, template mismatch, plugin side effects, and local/offline loading.

Bundled Checks

• scripts/validate_dataset_rows.py: validate JSONL/JSON/CSV rows for common ms-swift keys before passing them to swift sft, swift pt, or swift rlhf.
• scripts/inspect_registries.py: list installed DATASET_MAPPING, MODEL_MAPPING, and TEMPLATE_MAPPING entries without model downloads.
• scripts/inspect_template_encoding.py: print a safe template-encoding plan by default; optionally attempt an installed ms-swift processor/template encode without downloading weights.

Routing Boundaries

• Stay here for --dataset, --columns, --custom_dataset_info, --external_plugins, preprocessors, registry inspection, and model/template registration plans.
• Route full swift sft, swift pt, and dataset packing/training execution to the training sub-skill.
• Route inference engine behavior, deployment servers, and client requests to inference/deployment coverage.
• Route RLHF/GRPO algorithm execution, reward models, rollout workers, Ray, and Megatron execution to advanced distributed/RLHF coverage.
• Route export and evaluation execution to export/evaluation coverage.

Quick Patterns

• Prefer direct data files first: swift sft --dataset train.jsonl --columns prompt=query answer=response ....
• Use --custom_dataset_info dataset_info.json when stable aliases, subsets, splits, or reusable column mappings are needed.
• Use --external_plugins plugin.py only when code must call register_dataset, register_model, or register_template.
• Inspect support with python scripts/inspect_registries.py --models --templates --contains qwen2_5 before overriding --model_type or --template.
• Validate rows with python scripts/validate_dataset_rows.py data.jsonl --columns raw_prompt=query raw_answer=response --check-media exists before long runs.