bikash119

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

Installation

You will need to have at least Python 3.9 or higher, up to Python 3.12, since support for the latter is still a work in progress.

Define LLMs as local or remote models

Working with LLMs

LLM subclasses are designed to be used within a [Task][distilabel.steps.tasks.Task], but they can also be used standalone.

from distilabel.llms import OpenAILLM

llm = OpenAILLM(model="gpt-4")

Generating Synthetic Data with Distilabel

🧑 human (Sep 27, 2024, 05:42 PM)

help me with code to generate synthetic data using distilabel

🤖 assistant (Sep 27, 2024, 05:42 PM)

Certainly, I can help you with code to generate synthetic data using Distilabel. Distilabel is a library for creating labeled datasets using language models. Let's create a simple example to demonstrate how to use it.

<antArtifact identifier="distilabel-synthetic-data" type="application/vnd.ant.code" language="python" title="Synthetic Data Generation with Distilabel">

synthetic data

🧑 human (Sep 27, 2024, 05:45 PM)

help me create synthetic data with distilabel

🤖 assistant (Sep 27, 2024, 05:45 PM)

Certainly! I'd be happy to help you create synthetic data using Distilabel. Let's walk through the process of setting up a simple pipeline to generate synthetic data. We'll use the OpenAI API for this example, but you can adjust it based on your preferred LLM provider.

Here's a step-by-step guide to create a basic synthetic data generation pipeline:

First, make sure you have Distilabel installed:

	Configure output
	Bikash-Patras-MacBook:node bikashpatra$ ./configure --without-ssl
	Checking for program g++ or c++ : /usr/bin/g++
	Checking for program cpp : /usr/bin/cpp
	Checking for program ar : /usr/bin/ar
	Checking for program ranlib : /usr/bin/ranlib
	Checking for g++ : ok
	Checking for program gcc or cc : /usr/bin/gcc
	Checking for program ar : /usr/bin/ar
	Checking for program ranlib : /usr/bin/ranlib