MiniGPT-4 creates new opportunities at the edge

With the rapid development of artificial intelligence technology, generative large models have shown great potential in various fields. However, running such models on edge devices presents a number of challenges. Fortunately, the Jetson platform has been adapted to run generative large models on edge devices, breaking the limitations of traditional AI applications in the field of edge computing.

Edge devices combined with the advantages of generative large models

Those who follow us should learn from the last article that Feiyun Smart Box RTSS-X102 has pioneered the adaptation of large model LLM and Stable Diffussion. The deployment of generative AI based on large models at the edge provides an unprecedented new perspective for edge computing. Generative large models are deployed and run at the edge, which can make full use of the powerful capabilities of generative large models to achieve efficient and real-time natural language processing and image generation tasks.

1. Efficient inference: Edge devices achieve an efficient inference process by optimizing algorithms and model architectures. Respond quickly and process large amounts of data, even on resource-constrained devices.

2. Real-time generation: Edge devices are able to receive inputs in real time and generate corresponding outputs. Whether it's text, images, or audio, it can be generated in a short time, ensuring instant interaction with user needs.

3. Privacy protection: Data processing and model inference on edge devices help protect user privacy. Data does not need to be transferred to the cloud, greatly reducing the risk of leakage.

Feiyun Smart Box RTSS-X304 is an edge large model all-in-one machine, and we can also run MiniGPT-4 after the previous ViT test adaptation operation MiniGPT-4

MiniGPT-4

More recently, GPT-4 has demonstrated extraordinary multimodal capabilities, such as generating websites directly from handwritten text and recognizing humorous elements in images. These features were rarely observed in previous visual language models. The main reason for GPT-4's advanced multimodal generation capabilities is the use of more advanced large language models (LLMs). The creative team of MiniGPT-4 studied this phenomenon and came up with MiniGPT-4, which uses only one projection layer to align the frozen visual encoder with the frozen LLM Vicuna.

MiniGPT-4 consists of a visual encoder with pre-trained ViT and Q-Former, a single linear projection layer, and an advanced Vikuna large language model. MiniGPT-4 only needs to train the linear layer to make the visual features consistent with Vicuna. MiniGPT-4 has many features similar to those demonstrated by GPT-4, such as detailed image description generation and website creation from handwritten drafts. In addition, MiniGPT-4 has other emerging features, including writing stories and poems inspired by a given image, providing solutions to problems shown in images, teaching users how to cook based on food photos, and more.

The RTSS-X304 adapts to the performance of ViT

Feiyun Smart Box RTSS-X304 is a product independently developed and designed for Orin Nano/NX by Realtimes. As an edge large model all-in-one machine, it can run large oracle models locally. It has the ultimate value for money. After starting MiniGPT-4, you can import an image, and you can ask questions about the content of the image, which can well experience the analysis and understanding of the image and video at the edge of the large model. 

GTC2024

The latest GTC2024 conference will be held from March 18 to 21, 2024 at the SAN Jose Convention Center in California, USA, and the online conference will also be open at the same time. Scan the QR code on the poster below to register for GTC today. We look forward to your participation, attention and support.