Today, experience a tool that can handle multi-modal tasks: HuggingGPT. You can find more details at this link: https://huggingface.co/spaces/microsoft/HuggingGPT.
The corresponding paper link is as follows: https://arxiv.org/abs/2303.17580.
The main goal of the HuggingGPT system is to assist large language models (LLMs) in handling complex AI tasks.
A brief introduction to how HuggingGPT works is as follows: First, it uses ChatGPT to perform task planning based on user needs; then, it selects appropriate models according to the functional descriptions provided by the Hugging Face platform; next, it executes each subtask using the selected AI model; finally, it generates a summary of the response based on the execution results. This method enables HuggingGPT to effectively handle complex AI tasks across various modalities and domains, including challenging tasks in language, vision, speech, and more, achieving remarkable results.
HuggingGPT, as a promising new approach, can assist LLMs in achieving general artificial intelligence. By combining LLMs with expert models, it helps LLMs learn new skills and knowledge, thereby enabling them to better understand the world around them.
This paper also summarizes some of the key features of HuggingGPT:
HuggingGPT is a collaborative system composed of an LLM as the controller and numerous expert models as collaborative executors. The paper proposes an inter-model collaboration protocol that fully leverages the strengths of large language models and expert models. In this protocol, the large language model serves as the central hub for planning and decision-making, while smaller models act as executors for specific tasks, providing a new path for designing general AI models. The workflow of HuggingGPT includes four stages: task planning, task execution, task evaluation, and task learning. By integrating the Hugging Face Hub with over 400 task-specific models centered around ChatGPT, HuggingGPT can handle generalized AI tasks, offering users multimodal and reliable conversational services through open model collaboration.
HuggingGPT uses a demonstration-based parsing method to better understand the intent and standards of task planning. It has been proven effective in solving various complex AI tasks, including question answering, summarization, and translation. Through extensive experiments on multiple challenging AI tasks in language, vision, speech, and cross-modality, HuggingGPT's capabilities are demonstrated. The results show that HuggingGPT can understand and solve complex tasks from multiple modalities and domains.
Let me give an example. For instance, if there are two pictures and I want to recreate the action in picture 1 in picture 2, we can manually operate using Controlnet in the stable diffusion webui, but we can also provide natural language instructions to HuggingGPT, allowing it to call upon drawing AI to execute the task.
and there is still a significant gap. We look forward to more exciting products coming out in the future.
