⚗️ distilabel

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

Tip

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Features

• Integrations with the most popular libraries and APIs for LLMs: HF Transformers, OpenAI, vLLM, etc.
• Multiple tasks for Self-Instruct, Preference datasets and more.
• Dataset export to Argilla for easy data exploration and further annotation.

Warning

distilabel is currently under active development and we're iterating quickly, so take into account that we may introduce breaking changes in the releases during the upcoming weeks, and also the README might be outdated the best place to get started is the documentation.

Installation

pip install distilabel --upgrade

Requires Python 3.8+

In addition, the following extras are available:

• hf-transformers: for using models available in transformers package via the TransformersLLM integration.
• hf-inference-endpoints: for using the HuggingFace Inference Endpoints via the InferenceEndpointsLLM integration.
• openai: for using OpenAI API models via the OpenAILLM integration.
• vllm: for using vllm serving engine via the vLLM integration.
• llama-cpp: for using llama-cpp-python as Python bindings for llama.cpp.
• ollama: for using Ollama and their available models via their Python client.
• together: for using Together Inference via their Python client.
• anyscale: for using Anyscale endpoints.
• ollama: for using Ollama.
• mistralai: for using Mistral AI via their Python client.
• vertexai: for using both Google Vertex AI offerings: their proprietary models and endpoints via their Python client google-cloud-aiplatform.
• argilla: for exporting the generated datasets to Argilla.

Example

To run the following example you must install distilabel with both openai and argilla extras:

pip install "distilabel[openai,argilla]" --upgrade

Then run the following example:

from datasets import load_dataset
from distilabel.llm import OpenAILLM
from distilabel.pipeline import pipeline
from distilabel.tasks import TextGenerationTask

dataset = (
    load_dataset("HuggingFaceH4/instruction-dataset", split="test[:10]")
    .remove_columns(["completion", "meta"])
    .rename_column("prompt", "input")
)

# Create a `Task` for generating text given an instruction.
task = TextGenerationTask()

# Create a `LLM` for generating text using the `Task` created in
# the first step. As the `LLM` will generate text, it will be a `generator`.
generator = OpenAILLM(task=task, max_new_tokens=512)

# Create a pre-defined `Pipeline` using the `pipeline` function and the
# `generator` created in step 2. The `pipeline` function will create a
# `labeller` LLM using `OpenAILLM` with the `UltraFeedback` task for
# instruction following assessment.
pipeline = pipeline("preference", "instruction-following", generator=generator)

dataset = pipeline.generate(dataset)

Additionally, you can push the generated dataset to Argilla for further exploration and annotation:

import argilla as rg

rg.init(api_url="<YOUR_ARGILLA_API_URL>", api_key="<YOUR_ARGILLA_API_KEY>")

# Convert the dataset to Argilla format
rg_dataset = dataset.to_argilla()

# Push the dataset to Argilla
rg_dataset.push_to_argilla(name="preference-dataset", workspace="admin")

More examples

Find more examples of different use cases of distilabel under examples/.

Or check out the following Google Colab Notebook:

Badges

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

[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-light.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)

[<img src="https://raw.githubusercontent.com/argilla-io/distilabel/main/docs/assets/distilabel-badge-dark.png" alt="Built with Distilabel" width="200" height="32"/>](https://github.com/argilla-io/distilabel)

Contribute

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

References

• UltraFeedback: Boosting Language Models with High-quality Feedback
• JudgeLM: Fine-tuned Large Language Models are Scalable Judges
• Self-Instruct: Aligning Language Models with Self-Generated Instructions