Hugging Face: Exploring the Potential of an Open-Source AI Chatbot
Hugging Face, an AI company with an unconventional name, has developed an AI chatbot called HuggingChat. The company’s goal is to create AI models that can interact positively with humans on a personal level, similar to a hug. While HuggingChat has a similar vibe to ChatGPT, there are some notable differences, the most significant being that it is open source. This means that developers and AI enthusiasts can freely access and experiment with the tool online.
It should be noted that earlier versions of HuggingChat did not require a login and did not collect any information. However, the latest web version now requires users to create an account with Hugging Face and allows developers to review and examine chat data. Apologies are in order for any profanity sent to the developers during testing, as curiosity got the best of me.
When referring to the web version of HuggingChat in this article, it specifically pertains to the version provided by Hugging Face at hf.co/chat. It’s important to acknowledge that there are likely numerous other web-based versions of HuggingChat created by different developers, as is the nature of open source. However, for the purpose of this discussion, we will focus on the version made available by Hugging Face to showcase the technology.
The web version of HuggingChat utilizes a large language model (LLM) called OpenAssistant, which is also accessible on Github. There are several LLMs available on Github, which opens up various possibilities for exploration. Similar to ChatGPT, users can ask HuggingChat questions, and it will provide answers. What sets HuggingChat apart is the option to enable the “Search web” feature, allowing the chatbot to retrieve information from the live web to enhance its responses.
While HuggingChat is an intriguing tool, it may not replace ChatGPT or other similar platforms. The answers provided by HuggingChat are often incomplete, ending abruptly or lacking formatting. The chatbot’s responsiveness is slower compared to ChatGPT, and sometimes the answers are unrelated to the question asked.
It’s important to note that the web version of HuggingChat is still in its early stages and primarily serves as a feature demonstration for the software itself. It may not be suitable for academic purposes, but it does offer a platform for tinkering with AI projects.
For those interested in building their own chatbot, HuggingChat can be used as the user interface, but additional tools are required for the AI and data components. OpenAssistant is one language model that can be trained on various datasets. This aspect makes HuggingChat particularly appealing to AI enthusiasts, as they can leverage open-source LLMs and datasets to power their own chatbot projects.
One potential application of this technology is developing an enterprise chatbot that is trained on proprietary data hosted within a company’s firewall, ensuring it remains confidential. By combining HuggingChat, an inference server, and an LLM, it is possible to run a chatbot on local hardware, isolated from the internet. This opens up opportunities for customized chatbot solutions tailored to specific business needs.
Building your own chatbot does require powerful hardware, including a robust server with significant GPU capabilities. However, for those willing to invest in the necessary resources, the possibilities are endless.
In conclusion, Hugging Face’s HuggingChat offers a unique approach to AI chatbots with its open-source nature. While it may not be a direct competitor to ChatGPT, it provides a platform for developers and AI enthusiasts to experiment and create their own chatbot projects. With the right tools and resources, the potential for customized chatbot solutions is vast.
