prep

prep is an agent-friendly CLI for converting LLM/ML datasets into common standard training and evaluation schemas backed by Hugging Face datasets.

It currently supports four CLI target modes:

• sft: two-turn OpenAI-style chat samples with images.
• verl: VERL-compatible prompt/reward records with images.
• eval: evaluation records with question, options, answer, and images.
• show: diagnostic load-only mode that forces dataset decoding to surface bad samples and image warnings.

Installation

uv tool install prep-cli
# or use pip
pip install prep-cli

Install from source:

uv sync --dev
pip install -e .

CLI

Convert a dataset:

prep TARGET_FORMAT PIPELINE_ID [SPLIT] [OPTIONS]

Examples:

# `vqa` is the generic path for datasets that already resemble common VQA formats.
# It accepts a local path or a Hugging Face dataset source and maps fields using configurable column lists and templates.
prep sft vqa train --src path/to/data.jsonl --save
prep eval vqa val --src my-org/my-dataset --q-cols question problem --a-cols answer label

# or you may use a dataset-specific formatter pipeline if one is registered:
prep verl geo3k test --save

# show a dataset, to diagnose lazy image decoding or other issues:
prep show - train --src path/to/local_dataset --no-save --no-hf

List registered pipelines and local output status:

ppls
ppls out --as-json
ppls out --filter-format 's*'
ppls out --list-format

Source Loading

Loaders resolve sources in this order:

• Nonexistent local path: treat as a Hugging Face dataset ID. Use repo or repo@subset syntax.
• Existing directory: try datasets.load_from_disk(...), then fall back to load_dataset(...) for local datasets when a split is provided.
• Existing file: infer a loader from the extension. Supported suffixes are .parquet, .pq, .json, .jsonl, .ndjson, .csv, .tsv, .arrow, .txt, and .text.

If a dataset has multiple splits, a split must be provided. val is automatically translated to Hugging Face validation when needed.

Generic Conversion Controls

The generic vqa pipelines expose these important knobs:

• --q-cols: candidate question columns. Defaults to question, Question, problem.
• --a-cols: candidate answer columns. Defaults to answer, Answer, solution, label, caption, correct_answer, reports.
• --op-cols: option columns or choice fields. Defaults to options, choices, and choice_a through choice_j.
• --q-template: formats the prompt text. Defaults to {im_tags}{question}{options}.
• --a-template: formats the final answer text. Defaults to {answer}.
• --verl-ability and --verl-style: fill VERL metadata fields.
• --max-samples: truncate without shuffling.

Shared extraction logic supports these input patterns:

• image, image_1..image_n, image_01..image_99, or images.
• OpenAI-style messages.
• ShareGPT-style conversations.
• VERL-style prompt plus reward_model.
• Flat question/answer/option columns.

When options exist and the answer is an integer label, the mapper converts it to A, B, C, and so on.

Output Schemas

sft produces records shaped like:

{
	"images": list[Image],
	"messages": [
		{"role": "user", "content": str},
		{"role": "assistant", "content": str},
	],
	"id": str,
	"extra_info": str,
}

verl produces records shaped like:

{
	"images": list[Image],
	"data_source": str,
	"prompt": [{"role": "user", "content": str}],
	"ability": str,
	"reward_model": {"style": str, "ground_truth": str},
	"extra_info": {
		"split": str,
		"index": str,
		"explanation": str,
		"misc": str,
	},
}

eval produces records shaped like:

{
	"id": str,
	"images": list[Image],
	"question": str,
	"options": list[str],
	"answer": str,
}

During loading, the pipeline attempts to cast to the corresponding Hugging Face Features object and validates a small sample window.

Save And Upload Flow

By default, prep previews a few converted samples and then enters an interactive prompt before writing outputs.

• --save forces saving without prompting.
• --no-save skips writing altogether.
• --save-root changes the output root. The default is out.
• --save-parq writes a single parquet file per split instead of save_to_disk(...) directories.

Default local output paths are:

out/<target_format>/<pipeline_id>/<split>
out/<target_format>/<pipeline_id>/<split>.parquet

The CLI also exposes Hugging Face upload options such as --hf, --hf-repo, --hf-subset, and --hf-private. At the moment, the upload path is wired through a dry-run call in the current implementation, so it previews the target and prompt flow but does not actually push data.

Diagnostics

prep show ... is useful when a dataset fails during lazy image decoding or contains problematic files. In normal mode it runs a no-op filter(...) pass to force reads. If the environment variable SLOW_PASS=1 is set, it iterates in chunks and prints the exact failing indices with warnings or errors.

The pipeline layer also validates:

• OpenAI chat structure for sft and verl samples.
• <image> tag counts against the number of loaded images.
• The first few converted samples through ProcArgs.peek(...) logging.

Extending The Project

Add a new formatter by creating a module under src/prep/formatter/ and registering one or more loader functions with @formatter(...).

Minimal pattern:

from prep.api import ProcArgs, adaptive_load_dataset, formatter


@formatter("my-dataset", "sft", "train", default_src="org/my-dataset")
def load(path: str, split: str, args: ProcArgs):
	d = adaptive_load_dataset(path, split=split, nproc=args.num_proc)
	return d.map(...)

Notes:

• Formatter pipeline IDs (my-dataset) must not contain /.
• src/prep/formatter/__init__.py auto-imports all formatter modules under src/prep/formatter/*.py, so registration happens on package import.
• If your dataset already follows common VQA conventions, prefer vqa with CLI overrides before adding a dataset-specific loader.