# DataArcTech/DataArc-SynData-Toolkit

Synthetic Data Generation Platform By DataArcTech

Repository: https://github.com/DataArcTech/DataArc-SynData-Toolkit
Canonical: https://ross.abutalabs.com/products/dataarc-syndata-toolkit
Language: Python
License Family: other
Last push: 2026-06-30T10:33:18+00:00

## Health v2 (maintenance only)
Score: 57/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 90, release rhythm 35, longevity 20
- inputs: {"age_days": 285, "days_push": 64, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1777, forks 72 (observed 2026-08-28T04:05:34.737379+00:00)

## What it is
DataArc SynData Toolkit is a Python-based synthetic data generation platform for creating customized LLM training data from local corpora, Huggingface sources, or model distillation via simple configuration files. It offers both CLI and GUI interfaces and integrates end-to-end post-training workflows (SFT/GRPO via verl) with model evaluation using DeepEval.

## Use cases
- generate synthetic training data for llm fine-tuning
- distill data from a teacher model to train a smaller model
- create training datasets for low-resource languages
- crawl and filter datasets from huggingface for synthesis
- run sft or grpo post-training on generated data
- evaluate fine-tuned models automatically
- generate synthetic data without writing code

## When to choose
- you need an end-to-end pipeline from data synthesis to training and evaluation
- you want zero-code or config-driven synthetic data generation
- you need multilingual or low-resource-language training data
- you want flexible model provider support including local and OpenAI APIs

## When to avoid
- you need a permissively licensed tool - the repo has no license, so usage rights are unclear
- you only need simple tabular synthetic data rather than LLM training corpora
- you require a mature, long-established project with extensive community validation

## Facets
- artifact type: framework
- maturity: active
- function: data-generation, llm-training, machine-learning, cli, gui, etl
- domain: machine-learning, large-language-models, artificial-intelligence
- platform: python, cli, cross-platform
- tags: synthetic-data, model-distillation, sft, grpo, post-training, deepeval, huggingface, low-resource-languages, zero-code, data-engineering

## Member repositories
- DataArcTech/DataArc-SynData-Toolkit (main) score 57

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:34.737379+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T03:25:11.677462+00:00, confidence not recorded.
  - readme: https://github.com/DataArcTech/DataArc-SynData-Toolkit (fetched 2026-08-28T04:05:34.737379+00:00, sha 4c58609bb82c)
- Data as of 2026-08-30T08:39:29.467469+00:00.