The English word Llama means alpaca, and this word was long used by Meta (Facebook's parent company) to name its own AI big language model. Meta just released a series of new AI models called Llama 2 specifically to power modern chatbots like ChatGPT, Bard, and Bing Chat. Llama 2 is a new generation version of Llama, a natural continuation of the development of its series of large models. Llama can generate models of text and code based on prompts, similar to other chatbot systems. According to Meta, Llama 2's performance has improved significantly compared to the previous generation Llama model because it is trained on a combination of publicly available data.
Come to Baidu APP to enjoy high-definition pictures
Impact on the market
The large-scale launch of Llama 2 has not only sparked a wave of attention in the AI circle, but also caused a large number of investor discussions. The launch of Llama 2 has had a significant impact on the large model market and competition. First of all, the Llama 2's large-scale parameters and excellent performance make it one of the most competitive large models, posing a direct threat to other large models. Secondly, Llama 2's free commercial open source policy allows other enterprises and developers to use the model for free and secondary development, which greatly increases Llama 2's competitiveness and influence in the large model market. In addition, Llama 2 provides more tools and interfaces for other enterprises to integrate into their applications, further expanding the scope and impact of Llama 2. In short, the launch of Llama 2 has an important impact on the large model market and competition, which not only improves the performance and competitiveness of large models, but also promotes the development of open source ecology, providing more choices and possibilities for the application of artificial intelligence.
Llama 2 has also partnered with companies such as Microsoft and AWS. Llama 2's partnership with Azure makes it easier for developers to use and deploy the Llama 2 model on the Azure platform. Through its partnership with Meta, Azure provides cloud services that developers can build and train with Llama 2 on the Azure platform and leverage its cloud-native tools for content filtering and security features. The cooperation with Azure is very important for the promotion and application of Llama 2, which provides users with a more convenient experience and provides a wider range of platforms and resources. In addition, Meta has also partnered with AWS to deploy the Llama model on AWS and make it available to users through AWS cloud services. These collaborations are very important for the promotion and application of Llama 2, which not only provides a wider range of platforms and resources, but also provides users with a more convenient experience. At the same time, these collaborations have also promoted the development and optimization of Llama 2, providing more opportunities and possibilities for further improving the performance and reliability of large models.
One big difference between Llama 2 and OpenAI's GPT is that Llama 2 is both an open framework for deep learning and a series of large language models in itself. First, let's take a look at the basics of the Llama 2 framework.
Use of frameworks
The installation and use of the Llama 2 framework is very convenient, providing various capabilities of deep learning algorithms, in addition to the Llama 2 model, the installation and operation of the framework is not very demanding on resource requirements, and can be installed and run on general computer systems. For example, the following is the installation process on a regular ThinkPad:
The figure below shows an example of using the Llama 2 framework to fit your own training data.
In the example above, we used Llama 2 to load a very classic Boston house price prediction dataset.
This code defines a neural network model. The model consists of the following layers:
- Input layer: The size of the input image is 32x32x3, that is, 32 heights, 32 widths, 3 channels (red, green, blue).
- Convolutional layer: A convolutional layer with a convolution kernel size of 3x3 is used to output 32 feature maps. The activation function is ReLU (Modified Linear Unit).
- Maximum pooling layer: Maximum pooling of the output of the convolutional layer, using a pooling window with a size of 2x2.
- Tiled layer: Flattens the output of the convolutional layer into a one-dimensional vector for input into a fully connected layer.
- Fully connected layer: Use a fully connected layer with 10 neurons and the activation function is softmax.
The subsequent code trains the model.
If you are working with large datasets or complex models, Llama2 allows you to take advantage of distributed training, and the code snippet above shows the basic components of distributed training under the Llama 2 framework.
The above code shows how to implement on-device inference. When deploying a model in a real-world application, it is often necessary to make predictions about the user's device. LLAMA2 provides an easy way to prepare models for inference on devices. This is a very practical function and a very key point for the application of AI large models in the future. We can see that the Llama 2 framework is still convenient to implement this.
Llama 2 model
There are two versions of Llama 2: Llama 2 and Llama 2-Chat. Llama 2-Chat is specially designed for two-way conversations. The two versions differ in complexity and in the number of parameters, which determines the model's ability to generate text from the training data. Meta's Llama big language model is a free and commercial open source big language model. The LLAMA model currently supports multiple parameter scale versions such as 7 billion, 13 billion, and 70 billion. Compared to the first generation of Llama, Llama 2 has been trained on 2 trillion tokens, and its context training is twice as long as Llama, reaching 4096. In addition, the Llama-2-chat model has been trained on more than 1 million human annotations. Due to its large parameter scale and performance, Llama 2 is considered to be the most capable of challenging the GPT-4 large model.
Here's how the Llama 2 model compares to other models from Meta.
Image source: Meta website
The performance of the Llama 2 model is excellent, with the following advantages: Large parameter scale: The Llama 2 model has multiple parameter scale versions such as 7 billion, 13 billion, 70 billion, etc., which greatly improves the performance and reliability of the model. Large amount of training data: The Llama 2 model receives a large amount of training data, which enables the model to better understand and generate text, improving the accuracy and generalization ability of the model. Excellent performance: The Llama 2 model performs very well, showing high accuracy and efficiency in a variety of natural language processing tasks. Free and open source: The Llama 2 model is a free and open source model that gives developers more choice and flexibility.
Developers, the community, the market, and investors are full of expectations and expectations for Meta's Llama 2, which is why Meta announced open source research and commercial models the night before yesterday (Beijing time), Meta and Microsoft jointly announced a cloud model cooperation, and everyone was excited to publicize and research for the first time. As an open source big language model with large-scale parameters and excellent performance, Llama 2 has a wide range of application prospects and commercial value. It can be used not only for various natural language processing tasks, but also for intelligent customer service, natural language processing, text generation and other fields. In addition, Llama 2 can also cooperate with Microsoft, AWS and other companies to provide a wider range of applications and services. Therefore, developers, communities, markets and investors are full of expectations and expectations for Llama 2, believing that it will become one of the important cornerstones of the future artificial intelligence field.
#AI大模型应用#