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Distilabel is an AI Feedback (AIF) framework for building datasets with and for LLMs.

Project description

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Synthesize data for AI and add feedback on the fly!


Distilabel is the framework for synthetic data and AI feedback for AI engineers that require high-quality outputs, full data ownership, and overall efficiency.

If you just want to get started, we recommend you check the documentation. Curious, and want to know more? Keep reading!

Why use Distilabel?

Whether you are working on a predictive model that computes semantic similarity or the next generative model that is going to beat the LLM benchmarks. Our framework ensures that the hard data work pays off. Distilabel is the missing piece that helps you synthesize data and provide AI feedback.

Improve your AI output quality through data quality

Compute is expensive and output quality is important. We help you focus on data quality, which tackles the root cause of both of these problems at once. Distilabel helps you to synthesize and judge data to let you spend your valuable time on achieveing and keeping high-quality standards for your data.

Take control of your data and models

Ownership of data for fine-tuning your own LLMs is not easy but Distilabel can help you to get started. We integrate AI feedback from any LLM provider out there using one unified API.

Improve efficiency by quickly iterating on the right research and LLMs

Synthesize and judge data with latest research papers while ensuring flexibility, scalability and fault tolerance. So you can focus on improving your data and training your models.

🏘️ Community

We are an open-source community-driven project and we love to hear from you. Here are some ways to get involved:

  • Community Meetup: listen in or present during one of our bi-weekly events.

  • Slack: get direct support from the community.

  • Roadmap: plans change but we love to discuss those with our community so feel encouraged to participate.

What do people build with Distilabel?

Distilabel is a tool that can be used to synthesize data and provide AI feedback. Our community uses Distilabel to create amazing datasets and models, and we love contributions to open-source ourselves too.

  • The 1M OpenHermesPreference is a dataset of ~1 million AI preferences derived from teknium/OpenHermes-2.5. It shows how we can use Distilabel to synthesize data on an immense scale.
  • Our distilabeled Intel Orca DPO dataset and the improved OpenHermes model,, show how we improve model performance by filtering out 50% of the original dataset through AI feedback.
  • The haiku DPO data outlines how anyone can create a dataset for a specific task and the latest research papers to improve the quality of the dataset.

👨🏽‍💻 Installation

pip install distilabel --upgrade

Requires Python 3.8+

In addition, the following extras are available:

  • anthropic: for using models available in Anthropic API via the AnthropicLLM integration.
  • cohere: for using models available in Cohere via the CohereLLM integration.
  • argilla: for exporting the generated datasets to Argilla.
  • groq: for using models available in Groq using groq Python client via the GroqLLM integration.
  • hf-inference-endpoints: for using the Hugging Face Inference Endpoints via the InferenceEndpointsLLM integration.
  • hf-transformers: for using models available in transformers package via the TransformersLLM integration.
  • litellm: for using LiteLLM to call any LLM using OpenAI format via the LiteLLM integration.
  • llama-cpp: for using llama-cpp-python Python bindings for llama.cpp via the LlamaCppLLM integration.
  • mistralai: for using models available in Mistral AI API via the MistralAILLM integration.
  • ollama: for using Ollama and their available models via OllamaLLM integration.
  • openai: for using OpenAI API models via the OpenAILLM integration, or the rest of the integrations based on OpenAI and relying on its client as AnyscaleLLM, AzureOpenAILLM, and TogetherLLM.
  • vertexai: for using Google Vertex AI proprietary models via the VertexAILLM integration.
  • vllm: for using vllm serving engine via the vLLM integration.


To run the following example you must install distilabel with both openai extra:

pip install "distilabel[openai]" --upgrade

Then run:

from distilabel.llms import OpenAILLM
from distilabel.pipeline import Pipeline
from distilabel.steps import LoadHubDataset
from distilabel.steps.tasks import TextGeneration

with Pipeline(
    description="A simple text generation pipeline",
) as pipeline:
    load_dataset = LoadHubDataset(output_mappings={"prompt": "instruction"})

    generate_with_openai = TextGeneration(llm=OpenAILLM(model="gpt-3.5-turbo"))


if __name__ == "__main__":
    distiset =
                "repo_id": "distilabel-internal-testing/instruction-dataset-mini",
                "split": "test",
                "llm": {
                    "generation_kwargs": {
                        "temperature": 0.7,
                        "max_new_tokens": 512,


If you build something cool with distilabel consider adding one of these badges to your dataset or model card.

[<img src="" alt="Built with Distilabel" width="200" height="32"/>](

Built with Distilabel

[<img src="" alt="Built with Distilabel" width="200" height="32"/>](

Built with Distilabel


To directly contribute with distilabel, check our good first issues or open a new one.

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