ZeroGPT

ZeroGPT

ZeroGPT is an open-source alternative to ChatGPT that is focused on privacy and transparency. It uses a different AI model trained on public domain data to generate human-like responses while aiming to avoid issues like bias and hallucination.
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ZeroGPT: Open-Source Chatbot Alternative

ZeroGPT is an open-source alternative to ChatGPT that is focused on privacy and transparency. It uses a different AI model trained on public domain data to generate human-like responses while aiming to avoid issues like bias and hallucination.

What is ZeroGPT?

ZeroGPT is an open-source conversational AI assistant created as an alternative to ChatGPT. Unlike ChatGPT which is closed-source and owned by OpenAI, ZeroGPT is transparent about its model architecture and training data. It uses an AI model called BlurHAZE which is trained on public domain books and Wikipedia articles.

The goal of ZeroGPT is to provide an AI chatbot that can have natural conversations while avoiding common issues with large language models like bias, toxicity, and hallucination. It uses various techniques during training and inference to minimize these failure modes. For example, the BlurHAZE model has inbuilt uncertainty estimation to know when to abstain from responding.

As an open-source project with public training datasets, ZeroGPT allows more visibility into how the system works under the hood. Developers can customize and extend ZeroGPT for different use cases. It also respects user privacy by not collecting chat logs. Overall, ZeroGPT aims to balance transparency, ethics, and performance in conversational AI.

ZeroGPT Features

Features

  1. Open-source AI chatbot
  2. Trained on public domain data
  3. Focused on privacy and transparency
  4. Avoids issues like bias and hallucination
  5. Generates human-like conversational responses

Pricing

  • Open Source

Pros

Free to use

More privacy-focused than commercial chatbots

Transparent about limitations

Avoids problematic bias

Active open-source community

Cons

Less capable than large commercial models

Still has potential for bias

Responses can be lower quality

Limited amount of training data

Requires more computing resources to run locally


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