Synthetic Data Flywheel: End-to-End Pipeline for LLM Fine-Tune Dataset Generation

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Pipeline Architecture

Synthetic data pipelines usually fail in one of two ways: they generate too much low-quality data, or they become manually curated and lose scale advantages. This project is designed to keep both quality and automation in the loop.

It structures the lifecycle into generation, validation, judgment, labeling, calibration, visualization, and export so each stage is auditable and repeatable.

Why the Flywheel Pattern Works

Rejected outputs are not wasted. They become signal for the next iteration, which gradually improves pass rates and dataset quality over multiple rounds.

The result is a more reliable path from seed prompts to training-ready data.

Run the Project

git clone https://github.com/dakshjain-1616/synthetic-data-flywheel
cd synthetic-data-flywheel
pip install -e .
ollama pull gemma4

flywheel init
flywheel ingest -i demo.jsonl -n demo --tag demo1
flywheel validate -d demo --checks schema,length,dedup,pii --write-clean data/user/demo.clean.jsonl
flywheel judge -d demo --backend ollama --model gemma4:latest --tag v1
flywheel label -d demo --mode auto-from-judge --judgments data/judgments/demo.v1.jsonl
flywheel calibrate -d demo --tag v1
flywheel visualize -d demo
flywheel dataset export demo --to data/exports/demo.jsonl --judgments data/judgments/demo.v1.jsonl --filter "scores['overall'] >= 7" --split train=0.8,val=0.2

The README documents the flywheel CLI as the canonical interface, and this run block mirrors that end-to-end flow.

Architecture Walkthrough

The synthetic data flywheel repository is organized around a clear pipeline, so you can trace the full flow from input handling to final output without guesswork. This makes onboarding easier for new contributors and helps teams debug faster when behavior changes after updates.

Practical Use Cases

If you are evaluating synthetic data flywheel for production, start with a small real-world dataset, run the included commands end to end, and compare output quality, latency, and operational complexity. This gives a practical signal that is stronger than a toy demo.

Implementation Notes

The project is useful as both a standalone tool and a reference implementation. You can copy patterns from this codebase into your own stack, especially around evaluation discipline, reproducibility, and operator visibility.

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