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Prompt, generate synthetic data, and train models efficiently
DataDreamer is a Python library that streamlines prompting, synthetic dataset creation, and model training with reproducible, efficient workflows for researchers and practitioners.
When teams consider DataDreamer, these hosted platforms usually appear on the same shortlist.
ML hub with curated foundation models, pretrained algorithms, and solution templates you can deploy and fine-tune in SageMaker
Enterprise AI platform providing LLMs (Command, Aya) plus Embed/Rerank for retrieval
API-first platform to run, fine-tune, and deploy AI models without managing infrastructure
Looking for a hosted option? These are the services engineering teams benchmark against before choosing open source.
Create a synthetic medical records dataset
Generate realistic patient records to augment scarce real data, improving model performance while preserving privacy.
Fine‑tune a LLaMA model on domain‑specific instructions
Use DataDreamer’s LoRA pipeline to align the model quickly with minimal compute.
Benchmark prompting strategies across multiple LLM providers
Run reproducible multi‑step prompting workflows to compare output quality and cost.
Publish a research dataset with full provenance
Automatically generate data cards and citation lists, enabling easy sharing on Hugging Face.
Run `pip3 install datadreamer.dev` in your Python environment.
Both open‑source models (e.g., LLaMA, Falcon) and API‑based services (e.g., OpenAI, Anthropic) via LiteLLM integration.
It records workflow configurations, caches intermediate results, and generates data/model cards with full metadata.
GPU acceleration is recommended for fine‑tuning large models, though smaller experiments can run on CPU.
DataDreamer is released under the MIT License.
Project at a glance
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