Although ChatGPT and ShareGPT are both substantial language models created by OpenAI, they differ in terms of their sizes, structures, and uses.
A language model called ChatGPT was created exclusively for creating text-based dialogues between people and machines, such chatbots. To produce responses that are human-like, it was trained on a vast corpus of text data and fine-tuned using conversation data. With 1.5 billion parameters, ChatGPT is a smaller model than OpenAI’s more complex GPT-2 and GPT-3 models.
A language model called ShareGPT, on the other hand, is made for more general-purpose activities including text generation, translation, summarization, and more. It is larger than ChatGPT but smaller than GPT-3 because to its 6 billion parameters. ShareGPT is made accessible for research and development purposes and was trained using a number of sources, including books, websites, and Wikipedia articles.
The transformer design, a class of neural network that has been extensively applied to natural language processing (NLP) tasks, serves as the foundation for ChatGPT. It is learned using generative pre-training, a variation on unsupervised learning, where the model is trained to anticipate the following word in a string of text.
ChatGPT has a number of additional features in addition to its 1.5 billion parameters that make it ideal for creating text-based chats. For instance, it has a mechanism for managing the chatbot’s “persona,” which enables it to assume various linguistic nuances and adapt its responses to various users. Additionally, it has a memory system that enables it to recall earlier in the discussion and keep context.
On the other hand, ShareGPT is intended to be a more adaptable language model that can be used for a variety of NLP activities. Compared to ChatGPT, it has 6 billion more parameters, enabling it to capture text data with more complex relationships. Similar pre-training techniques are used to train ShareGPT as ChatGPT, but with a bigger dataset and more varied text sources.
Researchers and developers now have access to ShareGPT for research reasons, including the model and related training data. This is meant to promote innovation in the creation of new AI applications while also advancing the area of NLP.
Certainly! Two substantial language models, ChatGPT and ShareGPT, were created by OpenAI for various uses.
A language model called ChatGPT was created exclusively for creating text-based dialogues between people and machines, such chatbots. It is built on the transformer architecture, a class of neural network frequently used in natural language processing, and has 1.5 billion parameters. A system for managing the chatbot’s “persona” and a memory mechanism that aids in context maintenance are just two of the features that enable ChatGPT to produce responses that resemble those of a human.
The ShareGPT learning algorithm, on the other hand, is more versatile and can be applied to a variety of NLP tasks, including text creation, translation, summarization, and more. It is trained using a similar pre-training method and is considerably larger than ChatGPT with 6 billion parameters. In order to advance the field of NLP and foster innovation in AI applications, OpenAI has made ShareGPT accessible for research, giving academics and developers access to the model and the related training data.
| ChatGPT | ShareGPT | |
|---|---|---|
| Project | ||
| 45 | Mentions | 21 |
| 7,463 | Stars | 460 |
| – | Growth | – |
| 10.0 | Activity | 10.0 |
| 4 months ago | Latest Commit | 8 days ago |
| Python | Language | TypeScript |
| GNU General Public License v3.0 only | License | – |
What other options are there?
The following initiatives can also be taken into account when contrasting ChatGPT and sharegpt:
PyChatGPT is a Python client for the unofficial ChatGPT API that includes features like conversation tracking, auto token regeneration, and proxy support.
ChatGPT raycast plugin, chatgpt-raycast
granting ChatGPT access to an actual terminal, Alice
Unauthorized-chatgpt-api – This is an unofficial ChatGPT api repository. On Daniel Gross’ WhatsApp GPT, it is based.
Awesome-chatgpt-prompts – To make ChatGPT more useful, this repository contains curated ChatGPT prompts.
chatgpt-api – A client for the official ChatGPT API written in Node.js.
Use ChatGPT with the help of the VSCode plugin chatgpt-vscode.
ChatGPT-ProBot A GitHub robot that uses GPTChat for dialogue/CR/etc.
Summarize web pages with OpenAI ChatGPT at summarize.site
Artificial Intelligence Infrastructure-as-Code Generator is referred to as aiac.