A Simple demo for quick deploying llama2 via modelScope

cd llama2-13b-modelScopeDemo
mkdir model-cache
pip install -r requirements.txt
python infer.py

Given system = 'you are a helpful assistant in ai transformer and attention mechanism!'

Sure, I'd be happy to help! The visual transformer architecture is a type of neural network architecture that is specifically designed for image and video analysis tasks. It is based on the transformer architecture, which was originally developed for natural language processing tasks.

Here are some key details to consider when designing a visual transformer architecture:

1. Encoder-Decoder Structure: The visual transformer architecture typically consists of an encoder and a decoder. The encoder takes in an input image or video and generates a sequence of latent representations, while the decoder generates the output image or video.
2. Self-Attention Mechanism: The self-attention mechanism is a key component of the visual transformer architecture. It allows the model to attend to different parts of the input image or video and weigh their importance when generating the output.
3. Multi-Head Attention: The multi-head attention mechanism is a variation of the self-attention mechanism that allows the model to jointly attend to information from different representation subspaces at different positions.
4. Positional Encoding: Positional encoding is a technique used to add positional information to the input image or video. This is important because the transformer architecture does not inherently have access to the positional information of the input.
5. Number of Layers: The number of layers in the visual transformer architecture can vary depending on the complexity of the task and the size of the input data. In general, more layers can lead to better performance, but also increases the risk of overfitting.
6. Layer Normalization: Layer normalization is a technique used to normalize the activations of each layer in the transformer architecture. This helps to reduce the impact of vanishing gradients during training.
7. Feedforward Networks: Feedforward networks are used in the visual transformer architecture to process the output of the self-attention mechanism. These networks consist of fully connected feedforward neural networks (FFNNs) and can be used to perform a variety of tasks, such as classification, segmentation, or generation.
8. Output Linear Layer: The output linear layer is used to transform the final output of the visual transformer architecture into the desired output format. For example, if the task is image classification, the output linear layer would transform the output into a probability distribution over the possible classes.

Here is a high-level overview of a visual transformer architecture:

                                      +---------------+
                                      |  Input Image  |
                                      +---------------+
                                             |
                                             |
                                             v
                                      +---------------+
                                      | Encoder      |
                                      +---------------+
                                             |
                                             |
                                             v
                                      +---------------+
                                      | Multi-Head  |
                                      |  Attention   |
                                      +---------------+
                                             |
                                             |
                                             v
                                      +---------------+
                                      |  Feedforward  |
                                      |  Networks    |
                                      +---------------+
                                             |
                                             |
                                             v
                                      +---------------+
                                      | Output Linear |
                                      |  Layer      |
                                      +---------------+
                                             |
                                             |
                                             v
                                      +---------------+
                                      |  Output Image |
                                      +---------------+

I hope this helps! Let me know if you have any other questions. There are many resources available to learn more about the Visual Transformer architecture and its applications in computer vision tasks.

Here are some places you can explore:

1. Research Papers: The original Visual Transformer paper by Vaswani et al. (2017) is a great place to start. You can find it on the arXiv preprint server or on the website of the publisher, Nature. Other papers that build upon or extend the Visual Transformer architecture can also be found on online repositories such as arXiv or IEEE Xplore.
2. Online Courses: There are several online courses that cover the Visual Transformer architecture and its applications. For example, you can find courses on Coursera, Udemy, or edX that cover computer vision and deep learning topics. Some courses may specifically focus on the Visual Transformer, while others may cover a broader range of topics.
3. Tutorials and Blogs: There are many tutorials and blog posts available online that provide an introduction to the Visual Transformer architecture and its applications. Some popular blogs and websites that cover deep learning and computer vision topics include TensorFlow, PyTorch, and the Computer Vision blog.
4. Conferences and Workshops: Attending conferences and workshops on computer vision and deep learning can be a great way to learn more about the Visual Transformer architecture and its applications. Some popular conferences include the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Machine Learning (ICML), and the Neural Information Processing Systems (NIPS) conference.
5. Online Communities: Joining online communities such as the Computer Vision subreddit, the Deep Learning subreddit, or the TensorFlow community can be a great way to connect with other researchers and practitioners who are interested in the Visual Transformer architecture and its applications.
6. Books: There are several books available that cover the Visual Transformer architecture and its applications. Some popular books include "Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, and "Computer Vision with Python" by Adrian Rosebrock.
7. Research Institutes: Many research institutes, such as the Google Brain team, the Facebook AI Research Lab, and the Stanford Computer Vision Lab, have published research papers and code related to the Visual Transformer architecture. You can visit their websites to learn more about their research and find resources such as code and pre-trained models.

Remember that learning about the Visual Transformer architecture and its applications is an ongoing process, and there is always more to learn. By exploring these resources and staying up-to-date with the latest research, you can deepen your understanding of this powerful architecture and its potential applications in computer vision tasks.