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v0.38.0
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Get Started

  • 🎉 Installation
  • 🚀 Quick Tour
    • Synthetic Data Generation
    • Motivation and Design
    • Training an “Abstract to Tweet Model” with Fully Synthetic Data
    • Generating Training Data with Attributed Prompts
    • Distilling GPT-4 Capabilities to GPT-3.5
    • Augmenting an Existing Dataset
    • Cleaning an Existing Dataset
    • Bootstrapping Synthetic Few-Shot Examples
    • Instruction-Tuning and Aligning Models
    • Instruction-Tuning a LLM
    • Aligning a LLM with Human Preferences
    • Training a Self-Improving LLM with Self-Rewarding
  • 💡 Motivation and Design
  • 📖 Overview Guide
  • 🎓 Advanced Usage
    • Caching and Saved Outputs
    • Creating a New DataDreamer ...
      • Step
      • LLM
      • Trainer
      • Other
    • Parallelization
      • Running Steps in Parallel
      • Running Models on Multiple GPUs
      • Training Models on Multiple GPUs
    • Quantization
    • Parameter-Efficient Training

References

  • API Reference
    • datasets
    • embedders
    • errors
    • llms
    • retrievers
    • steps
    • task_models
    • trainers
  • Index

About

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  • License
  • Citation
  • Contact
  • Contributing
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Advanced Usage#

Advanced users may want to configure DataDreamer to their needs or learn more about DataDreamer internals. This section provides a deeper look into DataDreamer and covers various advanced topics.

  • Caching and Saved Outputs
  • Creating a New DataDreamer ...
  • Parallelization
  • Quantization
  • Parameter-Efficient Training
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Caching and Saved Outputs
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Overview Guide
Copyright © 2024, Ajay Patel ([email protected])
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