Introduction
GPT-4 is the latest Large Language Model that OpenAI has released. Its multimodal nature sets it apart from all the previously introduced LLMs. GPT-4 has shown tremendous performance in solving tasks like producing detailed and precise image descriptions, explaining unusual visual phenomena, developing websites using handwritten text instructions, and so on. The reason behind GPT-4’s exceptional performance is not fully understood. Experts believe that GPT-4’s advanced abilities may be due to the use of a more advanced Large Language Model. Which is mostly not present in smaller models and that's where Mini GPT comes into picture. Mini GPT4 is developed by a team of Ph.D. students from King Abdullah University of Science and Technology, Saudi Arabia.
What is Mini GPT4?
Mini GPT4 is a new advanced large language model that can understand both images and text. It is an open-source project that can understand images, generate recipes for images, identify problems in the images and give potential solutions, and even create working code for websites just from an image. The project combines a pre-trained language model Vicuna (built upon LLaMA and is reported to achieve 90% of ChatGPT’s quality as evaluated by GPT-4) with a visual encoder (BLIP 2) to get amazing results.
How Does Mini GPT4 Work?
The Frozen input visual encoder from BLIP2 processes the input image to generate a fixed-length vector representation of the image. This vector is then combined with the text input and processed through the Frozen language model of Vicuna 13B. This generates a sequence of hidden states that represent the language understanding. The hidden states from the language model are then fixed-length vector representations of the image which are connected and fed through a single projection layer. This projection layer transforms a concentrated-like representation into a final output, generating a text caption of the overall image.
Training Process
The training process was done in two stages. In the first stage, they used roughly 5 million text-image pairs and trained the model for 10 hours using four A100 GPUs. In the second stage, they only used 3500 high-quality text-image pairs generated by the model itself using Chat GPT. This fine-tuning took only seven minutes on a single A100 GPU.
Model Abilities
The model can generate very detailed descriptions of images based on human prompts or questions. The output from this model is simple but impressive. For example, it can describe logos or designs in detail. The data set used for fine-tuning the model is available as well.
Data Collection
During the initial stage of training, an enormous number of text-image pairs, approximately 5 million, were collected. For the next phase, a selection of 3500 top-quality text-image pairs created by the ChatGPT model were used for fine-tuning. These pairs are accompanied by image descriptions stored in a separate Json file, while the images themselves are kept in a separate folder.
Image Description and Analysis (examples)
- Detailed Image Descriptions - The language model is able to generate detailed descriptions of images, including the presence of motorcycles on the side of the road, people walking down the street, a clock tower with Roman numerals and a small spiral on top, blue skies with clouds in the distance, and a cactus plant standing in the middle of a frozen lake surrounded by large ice crystals.
- Understanding Humor in Memes - The language model is able to explain why a meme featuring a tired dog with the caption "Monday just Monday" is funny by recognizing that many people dislike Mondays and can relate to feeling tired or sleepy like the dog.
- Identifying Unusual Contents in Images - The language model is able to identify unusual contents in images such as a cactus plant standing in the middle of a frozen lake and recognize that it is not common in real life. It can also identify problems from photos such as brown spots on leaves caused by fungal infections or overflowing soap from washing machines.
- Providing Solutions to Image-Related Problems - The system analyzes images using encoders and information from large language models to objectify problems and provide solutions.
Where Can You Find More Information?
For further details regarding the Mini GPT4's data sets, language models, and the objectives of their application, a white paper is available. In addition, they have made their data set available for processing and other purposes. To interact with the project's assistant, an online demo is available where you can upload an image and initiate a conversation. Four different links are provided in case one of them is unavailable or busy. The 'source' section at the end of this page contains these links, and to begin chatting with the assistant, you can either click on a button or drag an image onto the webpage.
Benefits of a GitHub Repo
A GitHub repo would allow users to access both the model and code. Users could easily download and run the model on their own systems. The availability of code would make it easier for users to understand how the model works.
source
document link: https://github.com/Vision-CAIR/MiniGPT-4/blob/main/MiniGPT_4.pdf
deomo link: https://minigpt-4.github.io/
github code link: https://github.com/Vision-CAIR/MiniGPT-4
Conclusion
Mini GPT4 is a new advanced large language model designed to enhance vision language understanding. Mini GPT4 is a powerful tool with many potential uses. As the technology develops further, it will be able to provide better outputs and projections that solve users' overall objectives.
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