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

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  Learn about synthetic data generation for ORPO and DPO.

  Tutorial

• Clean an existing preference dataset

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

  Tutorial

• Retrieval and reranking models

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

  Tutorial

• Generate text classification data

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  Learn about how synthetic data generation for text classification can help address data imbalance or scarcity.

  Tutorial

Paper Implementations

• Deepseek Prover

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

  Example

• DEITA

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

  Paper

• Instruction Backtranslation

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

  Paper

• Prometheus 2

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

  Paper

• UltraFeedback

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

  Paper

• APIGen

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

  Paper

• CLAIR

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

  Paper

• Math Shepherd

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  Learn about Math-Shepherd, a framework to generate datasets to train process reward models (PRMs) which assign reward scores to each step of math problem solutions.

  Paper

Examples

• Benchmarking with distilabel

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  Learn about reproducing the Arena Hard benchmark with disitlabel.

  Example

• Structured generation with outlines

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

  Example

• Structured generation with instructor

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

  Example

• Create a social network with FinePersonas

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

  Example

• Create questions and answers for a exam

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  Learn how to generate questions and answers for a exam, using a raw wikipedia page and structured generation.

  Example

• Text generation with images in distilabel

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  Ask questions about images using distilabel.

  Example