Article directory
1 Introduction
I have recommended the website Poe to everyone before. Few people use it, but once you get in touch with it, you will find that it is actually quite powerful.
Because it is an online aggregation platform that can support several large models at the same time. It supports commonly used GPT4, GPT3.5, Claude, Llama and the like.
What are the benefits of this? The benefits are actually many.
Because each large model has a different answer or understanding of the same question, the best way is to collect each answer and compare which one is more suitable for you.
2. Qianfan large model platform
The Qianfan large model platform does something similar to Poe.
It’s just that the work Qianfan does is more complicated than that of Poe , because for Poe, as long as it gets the API interfaces of different models, it can easily integrate large models into its own platform, so The key point is to get the other party's API.
What Qianfan does is to run various open source large models on its own computing platform first, and optimize capabilities such as Chinese enhancement, performance enhancement, and context enhancement , and then provide them to different users. In fact, to be honest, for a person who is not familiar with computer systems and AI, it is not an easy task to run it on his own platform, not to mention that there are many open source models on the market.
However, from the explosion of popularity last year to the current settling period, large models have actually proven their role and potential in all walks of life.
Then the problem is that it is still a new technology. Everyone wants to use it, but they don’t know how to use it, because it involves a lot of details.
As for the Qianfan large model platform, if you want to deploy a large model on your platform or service, you only need a few very simple steps to deploy it successfully.
3. Very user-friendly features
In addition to this, it has several very user-friendly features.
Judging from the functions provided by the platform, they mainly include data-related, large model training, management and service functions. The function here is to simplify the development, training, fine-tuning and deployment of large models.
For example, there were many large models before, and you didn’t know whether to choose Llama or ChatGLM, and you didn’t know how many parameters should be used to achieve sufficient service effects in your business. Then you can use the model to evaluate it. You can Select multiple large models, and select different amounts of parameters for each model.
If your field has its own special data set, you can also customize it yourself to choose the most suitable large model for your field.
In this way, for users, it is almost like turning the most advanced artificial intelligence application into a point-and-shoot camera. They can completely ignore the various complex terms of neural networks and focus on the service effect rather than the complicated training process.
In addition to making it easier for users to deploy and use large models, the Qianfan Large Model Platform has actually made a lot of optimizations and adjustments to large models. Its main purpose is to further lower the threshold for using large models.
The newly upgraded Chinese enhancement function is specially optimized for internationally mainstream large-scale models, greatly improving the performance of these models in processing Chinese tasks. Unlike the previous situation where the best performance could only be achieved by relying on English conversations, now the same excellent results can be obtained by communicating in Chinese, such as the Llama2 model.
Also, for users who are new to large models, it is difficult to write a standard, efficient and useful prompt, because a useful prompt can only be refined after long-term dialogue with large models, and it does not happen overnight. things.
The Qianfan large-scale model platform has also taken this into consideration, and has built-in a lot of prompt templates, which can be used in most scenes.
On the other hand, the performance enhancement functions provided by Qianfan comprehensively improve the training and inference performance of the model . Specifically, the overall throughput of training the LlaMA 2 model can be increased by up to 25%, and the inference performance is expected to be doubled. This is critical for user experience: previously, if you were to process an e-book of several hundred pages with a large model with poor performance, the user would have to wait quite a while for a response. But now, optimized performance allows users to view model-generated content summaries in less than a second.
In addition, the Qianfan platform also provides long context enhancement functions for open source models to meet the reasoning needs of various long context application scenarios including knowledge enhancement, long-term memory enhancement, and document knowledge question and answer. This feature not only enhances the diversity of the model, but also greatly expands its application potential in complex tasks.
After all, from a business perspective, the simpler and easier-to-use a product is, the more attractive it is, and users do not care about complex operations. User experience is the main focus of large-scale model implementation.
4. comate code assistant
In addition to the large model platform, I also saw an application that makes programmers ecstatic, and that is the comate code assistant.
In fact, I have used similar functions or software, such as Copolit and code whisperer. Their main function is to facilitate programmers, people like me.
Because for programmers, there are only two things that need to be done for the code, the first is to understand the code, and the second is to be able to write the code.
The coding assistant specializes in these two things.
If you just come to work in a company, and this project has been around for a long time, then it will have a lot of code over time, and these codes will be full of the personal characteristics of the previous programmers, such as strange naming and irregularities. The way of writing and so on.
In the past, if you wanted to understand these codes, it would take unimaginable time and energy.
But now that the code assistant exists, you can quickly understand the historical code through the explanation given very conveniently.
The time saved can be fully focused on new code development and optimization of existing code.
I have discovered this problem before when using other people's open source code, especially when a file has thousands of lines of code. It may take several days to understand all the code, but with the help of the code assistant, this Time will be greatly saved to do more interesting things.
In addition, for comate, it can also act as a technical master.
Because it has been trained on thousands of lines of code and documents, in theory, its experience is enough to guide you as a master in the vast field of programming.
If you get stuck during the development process, you can ask comate directly.
Especially for problems that you need to check documents to solve, it will be very professional because it has been learned in advance, and with the blessing of large model logic and expression capabilities, you will find that the code assistant is simply a good mentor in code learning. , I don’t think you’re the kind of teacher who doesn’t know how to do this or that.
I think novice programmers can also use it to learn programming, because for a beginner, there are many ideas, but how to program to implement it is the main problem.
And many people are stuck at the very first step, and they are persuaded to leave even if they can't get in.
And the code assistant has a very powerful point, that is, to write code based on comments. In other words, you speak human language, and it outputs code.
If you can't read it, it doesn't matter, there is a code explanation function.
Whether the logic is closed loop or not, it can be said that it is the best learning tool for beginners and an artifact of code writing efficiency for programmers.
5. Summary
At this press conference, my two favorite updates are the large model platform and this code assistant. I think these two have very strong practical significance, both from the perspective of technical updates and practical applications.
