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LLAVA: Open-Source Model Can SEE Just like GPT-4-V
10/29/23
Editorial team at Bits with Brains
Recently, a large multimodal model has been developed that has been trained on a smaller amount of data and a faster GPU than other solutions, yet still beats all benchmarks.

The model consists of a large language model called Vicuna 1, which has 13 billion parameters, and a vision encoder called CLIP with L, which is connected to the language model by an MLP layer.
Vicuna 1 has been shown to beat all benchmarks on a variety of multimodal tasks, such as image captioning, visual question answering, and video summarization. This means that it is the best multimodal model that has been developed so far.
CLIP with L is a vision encoder that is trained on a large dataset of text and image pairs. It is able to learn the relationship between images and text, which allows it to be used for a variety of tasks, such as image captioning, visual question answering, and image retrieval. The "L" in CLIP with L stands for "language". This means that CLIP with L can learn the relationship between images and language. This is important because it allows CLIP with L to be used for tasks that require understanding the relationship between images and text, such as image captioning and visual question answering.
An MLP layer, or multilayer perceptron layer, is a type of neural network layer that is fully connected. This means that every neuron in the layer is connected to every neuron in the previous layer and the next layer.
This is an impressive achievement, especially considering that models of this size and complexity typically require days or even weeks to train. The fact that this model was trained on a smaller amount of data and a faster GPU than other solutions is a testament to the efficiency of the training process.
At the core of the model is the Vicuna 1 language model, which has 13 billion parameters. This is a medium sized model, and it is capable of processing vast amounts of text data. The vision encoder, CLIP with L, is connected to the language model by an MLP layer. The MLP layer is a multi-layer perceptron, which is a type of neural network that is commonly used in deep learning.
The CLIP model is based on the contrastive learning of image and text pairs. This means that the model is trained on pairs of images and text, and it learns to associate the two. This is a core fundamental of many large multimodal models, and it is what allows the model to understand the relationship between images and text.
The code and model are available for use, and the data set includes instructions for given images. This means that anyone can use the model to analyze images and text, and the data set provides a way to test the model's accuracy.
Overall, this is an impressive achievement in the field of deep learning. The fact that this model was able to beat all benchmarks while being trained on a smaller amount of data and a faster GPU than other solutions is a testament to the efficiency of the training process. The code and model are available for use, and the data set provides a way to test the model's accuracy.
For a CEO, this means that Vicuna 1 represents a new generation of multimodal AI models that are more affordable and easier to train. This could lead to a wide range of new applications for multimodal AI, such as in customer service, marketing, and product development.
Comparison with GPT-V
While not quite on par with GPT-4V, this open-source model can surpass GPT-4V in some tasks. It achieves an impressive 85% score compared to GPT-4-V on a synthetic multi-model instruction following dataset, and it is available for download and potential commercial use under the Apache 2.0 license. This is very impressive for a model having just 13 billion parameters.
The LLaVA model demonstrates impressive understanding of images, accurately identifying weather conditions and water-related dangers when asked about visiting a lake. It can recognize and identify food in images, providing accurate descriptions and even recipes. So, it can not only see an image but also understand and describe it in detail, which is extremely impressive.
The plot compares the accuracy of the new state-of-the-art model with the previous model, showing that the new model has likely been trained on more advanced algorithms and techniques, although there are some discrepancies in the accuracy values mentioned.
This open-source model is impressive in size and progress, but as with all LLMs, it can still occasionally hallucinate.
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