DataDreamer is an open-source Python library designed to simplify the implementation of complex large language model (LLM) workflows, including synthetic data generation, fine-tuning, instruction-tuning, and model alignment. It addresses challenges associated with the use of LLMs, such as their scale, closed-source nature, and lack of standardized tooling, by providing a standardized interface and best practices for reproducibility and open science. The library supports a wide range of LLM workflows, including synthetic data generation, task evaluation, fine-tuning, and model distillation. It also includes features for caching, resumability, and multi-GPU training, making it easier to implement and reproduce LLM workflows. DataDreamer integrates with other open-source and commercial LLM libraries and APIs, and it automatically implements best practices for reproducibility. The library allows researchers to share and publish synthetic datasets and models, and it provides synthetic data and model cards to help prevent contamination of pre-training sources with model-generated synthetic data. DataDreamer also supports various optimizations, including parallelization, quantization, and parameter-efficient fine-tuning, to improve efficiency and reduce computational costs. The library is designed to be user-friendly, with a standardized API that allows researchers to easily switch between models and experiment with different configurations. DataDreamer's features make it easier to implement, share, and reproduce LLM workflows, promoting open science and reproducibility in NLP research.DataDreamer is an open-source Python library designed to simplify the implementation of complex large language model (LLM) workflows, including synthetic data generation, fine-tuning, instruction-tuning, and model alignment. It addresses challenges associated with the use of LLMs, such as their scale, closed-source nature, and lack of standardized tooling, by providing a standardized interface and best practices for reproducibility and open science. The library supports a wide range of LLM workflows, including synthetic data generation, task evaluation, fine-tuning, and model distillation. It also includes features for caching, resumability, and multi-GPU training, making it easier to implement and reproduce LLM workflows. DataDreamer integrates with other open-source and commercial LLM libraries and APIs, and it automatically implements best practices for reproducibility. The library allows researchers to share and publish synthetic datasets and models, and it provides synthetic data and model cards to help prevent contamination of pre-training sources with model-generated synthetic data. DataDreamer also supports various optimizations, including parallelization, quantization, and parameter-efficient fine-tuning, to improve efficiency and reduce computational costs. The library is designed to be user-friendly, with a standardized API that allows researchers to easily switch between models and experiment with different configurations. DataDreamer's features make it easier to implement, share, and reproduce LLM workflows, promoting open science and reproducibility in NLP research.