Distilabel: An Introduction
Distilabel is an innovative framework designed for the synthesis of data and the addition of AI feedback. It is especially aimed at engineers requiring efficient, reliable, and scalable pipelines grounded in verified research. Distilabel caters to a wide array of projects and prides itself on easy integration with various AI models and data types.
Why Choose Distilabel?
Distilabel is useful for generating synthetic data and providing AI feedback across numerous project types, including traditional predictive NLP tasks like classification and extraction as well as generative and large language model tasks like instruction following and dialogue generation. By adopting a programmatic approach, Distilabel enables the creation of scalable pipelines that are essential for both data generation and AI feedback processes. The primary aim is to speed up AI development by swiftly generating diverse, high-quality datasets that align with verified research techniques.
Enhance AI Output Quality with Data Quality
Given the high costs of computational resources, Distilabel helps focus on improving the quality of data, which directly enhances the quality of AI outputs. This approach helps address underlying issues, ensuring that time is utilized in maintaining the highest quality standards for datasets.
Data and Model Control
Obtaining ownership of data for fine-tuning AI models, such as large language models (LLMs), can be challenging. Distilabel streamlines this process by integrating AI feedback from any LLM provider using a unified API, thereby providing greater ease and control.
Boost Efficiency with Iterative Research
With Distilabel, users can synthesize and evaluate data with the latest research papers, ensuring flexibility, scalability, and fault tolerance. This allows users to focus on enhancing data quality and improving model training processes.
Community and Collaboration
Distilabel operates under an open-source and community-driven philosophy, welcoming contributions and support. The community regularly engages through events like community meetups, active Discord channels, and open discussions about the project's roadmap.
Practical Applications Built with Distilabel
The Argilla community utilizes Distilabel to create remarkable datasets and models. For example, the 1M OpenHermesPreference dataset showcases Distilabel's ability to synthesize extensive data volumes. Furthermore, Distilabel has contributed to improving model performance by refining datasets with AI feedback. The community harnesses both general and task-specific datasets aligned with cutting-edge research to enhance data quality.
Installation and Integrations
To start using Distilabel, users need simply install it via pip:
pip install distilabel --upgrade
Distilabel requires Python 3.9 or greater. Additional integrations are available for extended functionality, including:
- Large language model support with various providers like Anthropic, Cohere, OpenAI, and more.
- Structured data generation using frameworks like outlines and Instructor.
- Data processing features including distribution with Ray and text clustering.
Installation Example
To use a specific example for text generation, users can install Distilabel with the hf-inference-endpoints extra:
pip install "distilabel[hf-inference-endpoints]" --upgrade
The following Python code demonstrates a simple text generation pipeline using Distilabel:
from distilabel.llms import InferenceEndpointsLLM
from distilabel.pipeline import Pipeline
from distilabel.steps import LoadDataFromHub
from distilabel.steps.tasks import TextGeneration
with Pipeline(
name="simple-text-generation-pipeline",
description="A simple text generation pipeline",
) as pipeline:
load_dataset = LoadDataFromHub(output_mappings={"prompt": "instruction"})
text_generation = TextGeneration(
llm=InferenceEndpointsLLM(
model_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
tokenizer_id="meta-llama/Meta-Llama-3.1-8B-Instruct",
),
)
load_dataset >> text_generation
if __name__ == "__main__":
distiset = pipeline.run(
parameters={
load_dataset.name: {
"repo_id": "distilabel-internal-testing/instruction-dataset-mini",
"split": "test",
},
text_generation.name: {
"llm": {
"generation_kwargs": {
"temperature": 0.7,
"max_new_tokens": 512,
}
}
},
},
)
distiset.push_to_hub(repo_id="distilabel-example")
Contributing to Distilabel
The Distilabel project invites contributions from the broader community. Interested individuals can explore identified "good first issues" or create new issues on the GitHub repository. By collaborating with Distilabel, contributors help advance the framework and enhance its capabilities across various applications.