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Tutorials

  • End-to-end tutorials provide detailed step-by-step explanations and the code used for end-to-end workflows.
  • Paper implementations provide reproductions of fundamental papers in the synthetic data domain.
  • Examples don't provide explenations but simply show code for different tasks.

End-to-end tutorials

  • Generate a preference dataset


    Learn about synthetic data generation for ORPO and DPO.

    Tutorial

  • Clean an existing preference dataset


    Learn about how to provide AI feedback to clean an existing dataset.

    Tutorial

  • Retrieval and reranking models


    Learn about synthetic data generation for fine-tuning custom retrieval and reranking models.

    Tutorial

Paper Implementations

  • Deepseek Prover


    Learn about an approach to generate mathematical proofs for theorems generated from informal math problems.

    Example

  • DEITA


    Learn about prompt, response tuning for complexity and quality and LLMs as judges for automatic data selection.

    Paper

  • Instruction Backtranslation


    Learn about automatically labeling human-written text with corresponding instructions.

    Paper

  • Prometheus 2


    Learn about using open-source models as judges for direct assessment and pair-wise ranking.

    Paper

  • UltraFeedback


    Learn about a large-scale, fine-grained, diverse preference dataset, used for training powerful reward and critic models.

    Paper

  • APIGen


    Learn how to create verifiable high-quality datases for function-calling applications.

    Paper

  • CLAIR


    Learn Contrastive Learning from AI Revisions (CLAIR), a data-creation method which leads to more contrastive preference pairs.

    Paper

Examples

  • Benchmarking with distilabel


    Learn about reproducing the Arena Hard benchmark with disitlabel.

    Example

  • Structured generation with outlines


    Learn about generating RPG characters following a pydantic.BaseModel with outlines in distilabel.

    Example

  • Structured generation with instructor


    Learn about answering instructions with knowledge graphs defined as pydantic.BaseModel objects using instructor in distilabel.

    Example

  • Create a social network with FinePersonas


    Learn how to leverage FinePersonas to create a synthetic social network and fine-tune adapters for Multi-LoRA.

    Example