HuggingGPT: Empowering ChatGPT Models to Utilize External Resources
In Brief
HuggingFace Hub serves as a platform enabling researchers and developers to disseminate and work together on natural language processing models, datasets, and other vital resources.
It also offers a user-friendly interface designed to help you search for and download pre-trained models tailored for a variety of NLP applications.
This strategy promotes greater adaptability and productivity in using GPT-based language models, as they can tap into an extensive array of specialized models without requiring specific training for each task.
It's likely you had a sneaking suspicion that these models exist within a centralized hub, where individuals can upload their training models, allowing others to download and utilize them seamlessly. Currently, HuggingFace Hub has set the benchmark for open-source models.

HuggingFace Hub is a resource-rich platform facilitating the sharing and collaboration of natural language processing models, datasets, and various tools among researchers and developers. It also features an intuitive interface for browsing and downloading pre-trained models aimed at diverse NLP tasks.
This piece proposes the exciting idea that ChatGPT should tap into an extensive library of existing models—numbering in the thousands and addressing countless varying issues—through external connections. This shift would significantly simplify the training process for ChatGPT, allowing it to leverage external tools instead of focusing on every individual skill like image generation or speech translation. Consequently, it enhances the model's efficiency and adapts it toward creating sophisticated AI solutions. HuggingGPT ChatGPT interprets a human command.
In total, we get something like this:
- Next, ChatGPT translates this command into a list of actionable tasks.
- For each designated task, it selects the appropriate model from thousands available, based on the specified criteria.
- ChatGPT reviews the model's requirements and organizes the necessary inputs accordingly.
- Once a task is completed, ChatGPT examines the results and methodically follows through the outlined plan, repeating the steps of task selection and execution.
- To illustrate this, let's consider an example from the article: imagine a request to 'produce an image of a girl absorbed in a book, mirroring the boy's pose in the provided image, and then articulate a detailed description of the newly created picture.' The model identifies up to six distinct tasks and skillfully manages to execute them in succession. result The model, intriguingly named 'JARVIS,' likely takes inspiration from the AI assistant character featured in the 'Iron Man' film series.

Microsoft's researchers propose a new initiative to integrate ChatGPT with 15 additional AI models.
The code is available in a repository Universities face criticisms for allegedly enabling students to utilize AI as a means of plagiarizing.
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