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name: data-and-templates description: "Use this sub-skill when working on LlamaFactory dataset_info entries, Alpaca/ShareGPT/OpenAI data rows, multimodal media columns, prompt templates, tool-calling fields, preprocessing validation, or data processor behavior. It covers v0 data loading/conversion/template/preprocessing issues and excludes launch, optimizer, export, API serving, and web UI operations." disable-model-invocation: true metadata: disco-role: operating license: Apache 2.0

LlamaFactory Data And Templates

Use this sub-skill for LlamaFactory 0.9.6.dev0 v0 data preparation and prompt-formatting work. The v0 CLI entry points are llamafactory-cli and lmf; v0 is the default unless USE_V1=1 is set.

Route The Task

• For dataset_info.json entries, local/remote dataset selection, column mappings, ranking flags, and media columns, read references/data-formats.md.
• For template:, tool_format, thinking flags, prompt construction, processor selection, packing, and tokenization behavior, read references/templates-and-processing.md.
• For common failure modes such as undefined datasets, missing registry files, wrong columns/tags, ranking mismatch, media-token mismatch, and tokenized_path confusion, read references/troubleshooting.md.
• To statically check a registry entry and optional tiny row file without importing LlamaFactory, run python scripts/validate_dataset_entry.py --help.

Boundaries

This sub-skill owns data registry/schema/template/preprocessing diagnosis. Hand off training launch, optimizer, LoRA, distributed, and YAML orchestration decisions to training-and-configs; model loading, quantization, adapters, and export to model-loading-and-export; and service/API/web UI operations to inference-and-serving or webui-and-ops.

Fast Workflow

1. Identify the dataset name listed in dataset: or eval_dataset: and verify a matching top-level key exists in dataset_info.json under dataset_dir.
2. Determine load source precedence: hub URL keys first, then script, cloud file, then local file_name.
3. Confirm formatting is alpaca, sharegpt, or code-backed openai, and verify the declared columns and tags match representative rows.
4. Check task/stage compatibility: reward-modeling expects ranking: true; most non-RM stages reject ranking datasets.
5. Check template: supports the model family and any multimodal placeholders present in the data.
6. Run the bundled validator for early static issues before trying a full train command.