soup/examples/data/README.md

Sample data

These are format examples and test fixtures — not training data. Each file is a handful of rows, there to show what a format looks like and to give the parser and the test suite something to chew on. Nothing useful will train on this volume. Treat them as a shape to copy, not a starting corpus.

File	Format	Rows	Demonstrates
alpaca_tiny.jsonl	alpaca	10	instruction / input / output instruction pairs
chat_preferences.jsonl	dpo	5	prompt / chosen / rejected, messages as chat turns
dpo_sample.jsonl	dpo	8	Same shape, longer answers — used by the DPO example
reasoning_math.jsonl	alpaca	5	Step-by-step worked solutions for GRPO/reasoning

soup data inspect <file> prints the detected format and stats for any of them.

Getting real data

soup data search "instruction tuning"      # find datasets on the HF Hub
soup data preview <dataset-id>             # splits + features, before downloading
soup data download <dataset-id> -o out.jsonl
soup data demo                             # list bundled demo fixtures (same files as these)

How much do you actually need

• A format or a style (JSON output, a house tone, a response shape): hundreds of rows.
• A task the model already half-knows (your flavour of summarisation, classification): thousands.
• New facts the model has never seen: fine-tuning is usually the wrong tool — retrieval (RAG) puts the facts in the prompt instead, and stays correct when they change.

soup advise <data> --goal "..." reads your data and says which of these you're in.

More on formats, conversion, and the data pipeline: docs/data.md.