synthetic-dataset-generation

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.   🤖💤

A novel approach for synthesizing tabular data using pretrained large language models

A suite of auto-regressive and Seq2Seq (sequence-to-sequence) transformer models for tabular and relational synthetic data generation.

Optimize Document Retrieval with Fine-Tuned KnowledgeBases

Build an Autonomous Web3 AI Trading Agent (BASE + Uniswap V4 example)

Synthetic test data that hits the numbers you declare, exactly. Multi-table with verified foreign-key integrity, deterministic, no model in the data path. Python + MCP server.

A data-centric AI package for ML/AI. Get the best high-quality data for the best results. Discord: https://discord.gg/t6ADqBKrdZ

SynthGenAI - Package for Generating Synthetic Datasets using LLMs.

Diagnose the performance of your RAG🩺

Using deep research workflow to generate datasets for finetuning LLMs.

[TACL, EMNLP 2025 Oral] Code, datasets, and checkpoints for the paper "CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation"

Synthetic data generation to fuel AI models

Madlab is an advanced AI development studio designed to streamline the process of dataset management, model training, and evaluation. It combines a robust Node.js/Express backend with a powerful Python-based training engine and a modern React frontend.

Open-source framework for turning expert knowledge into PII-free synthetic conversational data and production-ready LoRA adapters.

Synthetic data augmentation technique via LLM for Automatic Speech Recognition fine tuning.