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DataDreamer
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.
Overview
Highlights
Multi-step prompting workflows for any LLM
Synthetic dataset generation with built‑in augmentation
Efficient training pipelines with caching, quantization, LoRA
Automatic data/model card creation for easy sharing
Pros
- Research‑grade correctness and reproducibility
- Simple API with sensible defaults
- Supports both open‑source and API‑based LLMs
- Aggressive caching reduces compute costs
Considerations
- Requires Python environment and dependencies
- Effective use assumes knowledge of LLM prompting
- Training large models still needs substantial GPU resources
- Advanced features may need manual configuration
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Fit guide
Great for
- Researchers building reproducible LLM experiments
- Data scientists needing synthetic data for rare tasks
- ML engineers fine‑tuning models with limited data
- Teams that want to share datasets and models with metadata
Not ideal when
- Purely non‑Python stacks
- Production systems requiring ultra‑low latency inference
- Users without access to GPU resources for model training
- Projects that need out‑of‑the‑box UI without coding
How teams use it
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.
Tech snapshot
Python97%
Shell3%
Frequently asked questions
How do I install DataDreamer?
Run `pip3 install datadreamer.dev` in your Python environment.
Which LLMs are supported?
Both open‑source models (e.g., LLaMA, Falcon) and API‑based services (e.g., OpenAI, Anthropic) via LiteLLM integration.
How does DataDreamer ensure reproducibility?
It records workflow configurations, caches intermediate results, and generates data/model cards with full metadata.
Do I need a GPU for training?
GPU acceleration is recommended for fine‑tuning large models, though smaller experiments can run on CPU.
What license is DataDreamer released under?
DataDreamer is released under the MIT License.
Project at a glance
Stable- Stars
- 1,088
- Watchers
- 1,088
- Forks
- 55
LicenseMIT
Repo age2 years old
Last commit12 months ago
Primary languagePython
Last synced yesterday