Hugging Face, the startup headquartered in New York City, recognized for its widely utilized repository that serves developers in search of open source AI code and frameworks, has introduced a new offering today. The latest product, named Hugging Chat Assistants, is now available for customization by third parties.
This innovative feature, offered at zero cost, enables users of Hugging Chat to easily create personalized AI chatbots with specific functionalities. This offering is reminiscent of OpenAI’s custom GPT Builder, but without the requirement for a paid subscription like ChatGPT Plus (starting at \(20 per month), Team (priced at \)25 per user per month billed annually), and Enterprise (with pricing that varies based on specific requirements).
Empowering Tailored AI Chatbot Development
Phillip Schmid, the Technical Lead & LLMs Director at Hugging Face, announced the news on X (formerly known as Twitter), emphasizing that users can establish their unique Hugging Face Chat Assistant “in 2 clicks!” Schmid drew parallels between the new capabilities and OpenAI’s custom GPTs.
Introducing Hugging Chat Assistant! ? Craft your personal Assistant within Hugging Face Chat effortlessly with just 2 clicks! Similar to @OpenAI GPTs, you can now generate customized versions of @huggingface Chat! ?
An Assistant is defined by
?️ Name, Avatar, and Description
? Any available open… pic.twitter.com/9XaReKgg9m— Philipp Schmid (@_philschmid) February 2, 2024
In contrast to the GPT Builder and GPT Store, which rely solely on OpenAI’s proprietary large language models (LLM) GPT-4 and GPT-4 Vision/Turbo, the key differentiator with Hugging Chat Assistant lies in its autonomy and freedom of choice.
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Unlike users of Hugging Chat Assistant, who have the freedom to choose from various open source LLMs to enhance their AI Assistant, ranging from Mistral’s Mixtral to Meta’s Llama 2. This aligns with Hugging Face’s overarching strategy of offering users a diverse range of models and frameworks to select from, reflecting the approach taken with Hugging Chat, where users can opt for different open source models to drive its functionalities.
A Competitor to the GPT Store?
Similar to OpenAI’s recent launch of the GPT Store, Hugging Face has introduced a centralized repository of third-party customized Hugging Chat Assistants. Users can explore and utilize these Assistants at their convenience.
The layout of the Hugging Chat Assistants aggregator page bears a resemblance to the GPT Store page, including its visual aesthetics, showcasing custom Assistants similar to custom GPTs in distinct rectangular boxes with circular logos.
Screenshot of OpenAI’s GPT Store.
Screenshot of Hugging Face’s Hugging Chat Assistants page.
Advantages Over GPTs and Areas for Improvement
Within the open source AI community, some users are praising Hugging Chat Assistants as superior to GPTs. Mathieu Trachino, the founder of GenDojo.ai, an enterprise AI software provider, took to X to outline the benefits, particularly emphasizing the customization options for the underlying models and the cost-free nature of the service compared to OpenAI’s subscription-based offerings.
Why @huggingface Assistants are better than GPTs
Today, Hugging Face released Assistants, akin to OpenAI GPTs.
Here are the key advantages:
- Choose your model:
Experiment with various open-source models and select the ideal fit for your scenario. You can opt for models like… pic.twitter.com/dHmHRHfFyR
— Mathieu Trachino (@AI_NewsWaltz) February 2, 2024
Trachino also pointed out areas where custom GPTs outperform Hugging Chat Assistants, such as the lack of support for web search, retrieval augmented generation (RAG), and the inability to autonomously generate logos (a capability of GPTs leveraging OpenAI’s image generation AI model DALL-E 3).
The introduction of Hugging Chat Assistants highlights the rapid advancement of the open source community in keeping pace with closed competitors like the ironically named “Open” AI, especially following the recent leak of a new open source model, Miqu, from Mistral. This model nearly matches the performance of the closed GPT-4, which currently serves as the benchmark for LLMs. However, the question remains – for how long?
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