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👋🏻 Welcome

OpenPO simplifies building synthetic datasets by leveraging AI feedback from 200+ LLMs.

Key Features

  • 🤖 Multiple LLM Support: Collect diverse set of outputs from 200+ LLMs

  • ⚡ High Performance Inference: Native vLLM support for optimized inference

  • 🚀 Scalable Processing: Built-in batch processing capabilities for efficient large-scale data generation

  • 📊 Research-Backed Evaluation Methods: Support for state-of-art evaluation methods for data synthesis

  • 💾 Flexible Storage: Out of the box storage providers for HuggingFace and S3.

How It Works

  1. Generate responses from various models on HuggingFace and OpenRouter.
  2. Run evaluations on the response dataset.
  3. Store, publish and fine-tune your model using the synthesized dataset.

Why Synthetic Datasets?

The cornerstone of AI excellence is data quality - a principle often expressed as "garbage in, garbage out." However, obtaining high-quality training data remains one of the most significant bottlenecks in AI development, demanding substantial time and resources from teams.

Recent researches has demonstrated breakthrough results using synthetic datasets, challenging the traditional reliance on human annotated data. OpenPO empowers developers to harness this potential by streamlining the synthesis of high-quality training data.

OpenPO aims to eliminate the data preparation bottleneck, allowing developers to focus on what matters most: building exceptional AI applications.