Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
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Updated
Jul 20, 2026 - Python
Distilabel is a framework for synthetic data and AI feedback for engineers who need fast, reliable and scalable pipelines based on verified research papers.
A framework for prompt tuning using Intent-based Prompt Calibration
Synthetic data curation for post-training and structured data extraction
DataDreamer: Prompt. Generate Synthetic Data. Train & Align Models. 🤖💤
Perception toolkit for sim2real training and validation in Unity
[ICLR 2025] Alignment Data Synthesis from Scratch by Prompting Aligned LLMs with Nothing. Your efficient and high-quality synthetic data generation pipeline!
A lightweight library for generating synthetic instruction tuning datasets for your data without GPT.
Configurable Generation of Synthetic Schemas and Knowledge Graphs at Your Fingertips
NVIDIA Deep learning Dataset Synthesizer (NDDS)
Compose multimodal datasets 🎹
A curated list of awesome projects which use Machine Learning to generate synthetic content.
Augmentation pipeline for rendering synthetic paper printing, faxing, scanning and copy machine processes
SynthDet - An end-to-end object detection pipeline using synthetic data
A novel approach for synthesizing tabular data using pretrained large language models
[NeurIPS D&B Track 2024] Official implementation of HumanVid
awesome synthetic (text) datasets
Unity's privacy-preserving human-centric synthetic data generator
✨ A synthetic dataset generation framework that produces diverse coding questions and verifiable solutions - all in one framwork
Random dataframe and database table generator
[IMC 2020 (Best Paper Finalist)] Using GANs for Sharing Networked Time Series Data: Challenges, Initial Promise, and Open Questions
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