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synthetic-data-gen

Generate 1k-1M labeled training examples via local Qwen. ML teams + Datarade marketplace.

Get startedSource on GitHub

Launch kit

synthetic-data-gen — launch kit

1-liner

Generate 1k-1M labeled training examples via local Qwen. ML teams + Datarade marketplace.

Tweet hook

ML teams hire Surge/Scale to label 100K examples for $50-150K. Or burn 6 months of internal labeler time.

Synthetic data via Qwen + a small human holdout: $0 marginal, 70% quality match.

Spec format + diversity-seed strategy 🧵

Reddit

  • r/MachineLearning: "Synthetic vs real labeled data — research"
  • r/datasets: "Open synthetic-data framework on Datarade"

Cold-email ICP

  • ML teams at startups training their own models
  • Subcontractors for Surge / Scale needing volume
  • Datarade buyers wanting niche corpora

Cold-email template

Subject: 100K labeled examples for {their task}

Hi {first} — caught {their ML work / Hugging Face profile}.

For supervised classification + extraction tasks, synthetic data via
Qwen + a small human holdout works well in 2026 (research-supported).
Cost: $0.005/record vs $1.50 from Surge.

Free 1K examples on your task spec. Reply with the schema.

SEO content

  1. "Synthetic vs human-labeled data — when each wins"
  2. "Diversity-seed strategy for unbiased synthetic data"
  3. "Datarade vs direct sale for synthetic-data products"

Documentation

synthetic-data-gen

Synthetic training-data generator. Spec (YAML) → 1k–1M labeled examples via local Qwen. Round-robins across diversity seeds, dedupes by input string.

Pricing

  • Per-record $0.005-0.05 depending on complexity
  • Custom dataset commission $1,000-50,000 per project
  • Datarade marketplace listing $99-2,999 per pre-built dataset
  • DIY $0 — you generate locally

Why this matters

ML teams need labeled data. Hiring labelers (Surge, Scale AI, Labelbox) is expensive and slow. Public datasets are limited. Synthetic data via local Qwen is unlimited and free at point of use.

For supervised classification + extraction tasks, synthetic data combined with a small human-validated holdout set works well in practice (research-supported in 2026).

Run

cd C:\openclaw-products\synthetic-data-gen
python -m venv .venv
.\.venv\Scripts\activate
pip install -e .

syndata generate examples/intent-classification.yaml --out data/intent-1k.jsonl

Spec format

task: <string describing the task>
labels: [<allowed label strings>]
count: <total examples>
batch_size: 25
diversity_seeds:
  - <persona/scenario 1>
  - <persona/scenario 2>
  ...

Roadmap

  • Schema-driven (Pydantic model in spec → enforced output shape)
  • Active-diversity (semantic similarity check vs previous batch)
  • Image-task support (Qwen-VL for visual classification)
  • Auto-scoring against a small human-labeled holdout
  • Datarade upload helper