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name: dataset-and-prompts description: "Guides Stanford Alpaca dataset schema, prompt formatting, label masking, validation, intended use, and license checks." disable-model-invocation: true metadata: disco-role: operating license: NOASSERTION

Dataset and Prompts

Use this sub-skill when the task is about the released Alpaca instruction dataset, Alpaca-style data files, prompt rendering, source/target construction, label masking, or pre-flight validation before training or generation.

Route here for

• Explaining the released alpaca_data.json record schema and the verified 52,002-row release count.
• Checking whether a JSON or JSONL file can be consumed as Alpaca-style supervised fine-tuning data.
• Rendering the exact prompt form used for records with a non-empty input and records with an empty input.
• Explaining how train.py constructs sources, appends the tokenizer EOS token to targets, and masks prompt labels with IGNORE_INDEX = -100.
• Answering dataset datasheet, model-card, intended-use, and license questions before using Alpaca data or generated derivatives.

Reroute instead

• OpenAI/Self-Instruct data generation, seed tasks, completion parsing, cost, network, or API keys: route to instruction-generation at ../instruction-generation/SKILL.md.
• Trainer/FSDP/DeepSpeed commands, model downloads, checkpoint directories, or full supervised fine-tuning execution: route to fine-tuning at ../fine-tuning/SKILL.md.
• Alpaca/LLaMA weight diff creation, recovery, checksums, or weight artifact licenses: route to weight-diff-recovery at ../weight-diff-recovery/SKILL.md.
• Repository-wide install or route selection issues: return to the root router at ../../SKILL.md.

Bundled references and scripts

• Read data formats for the schema, prompt templates, training source/target construction, label masking, and validator semantics.
• Read intended use and licenses before distributing, modifying, training on, or generating derivatives of Alpaca data.
• Read troubleshooting when validation fails, prompt text differs from expected Alpaca format, tokenizer padding/EOS behavior is confusing, or license constraints are unclear.
• Run validate_alpaca_data.py to validate JSON/JSONL records and preview the exact Alpaca prompt branch selected for each row.

Safe validation workflow

1. Start with the offline validator; it never calls OpenAI, downloads models, imports Torch, or reads the original repository.

   python scripts/validate_alpaca_data.py path/to/alpaca_or_generated.json --preview 2
   

2. For the public release, expect a JSON array with exactly 52,002 objects and the keys instruction, input, and output on every row:

   python scripts/validate_alpaca_data.py alpaca_data.json --expect-count 52002
   

3. For generated data handoff, validate JSONL or JSON first, then send schema-clean examples to instruction-generation if the question is about producing more examples or to fine-tuning if the question is about training with the file.

4. Treat empty input as valid: it selects the no-input prompt template. Treat missing input as invalid because the released dataset uses the key on every row.

5. Treat blank output as a warning by default because the released dataset contains a small number of blank outputs; use --require-nonempty-output when building a stricter training corpus.

Core facts to remember

• The verified train.PROMPT_DICT keys are prompt_input and prompt_no_input.
• The verified train.preprocess signature is preprocess(sources, targets, tokenizer) -> Dict.
• SupervisedDataset loads a JSON list, selects the prompt template with example.get("input", "") != "", and sets each target to output + tokenizer.eos_token.
• preprocess tokenizes source + target and the source alone, then masks prompt-token labels with -100 so loss is only taken on the response portion.
• DataCollatorForSupervisedDataset pads input_ids with tokenizer.pad_token_id, pads labels with -100, and returns attention_mask = input_ids.ne(tokenizer.pad_token_id).

Validation before downstream work

Before training or generation, confirm:

• The file parses as JSON array or JSONL object rows.
• Every row is an object with string instruction, input, and output fields.
• instruction is non-empty after trimming whitespace.
• Any blank output rows are intentional, filtered, or accepted by the downstream owner.
• Prompt previews match the exact Alpaca templates in data formats.
• The intended use is research/non-commercial where the data license or generated-data restrictions apply; see intended use and licenses.