Azure GPT4教程: 完整指南

Azure GPT4是一种基于云的预训练语言模型，旨在帮助开发者创建高质量的语言应用。它基于GPT-4的技术，能够理解和生成人类语言。

Azure GPT4支持多种开发语言，包括Python、Java和C#。开发者可以使用这些语言来创建自己的语言应用，例如聊天机器人和语言翻译工具。

Azure GPT4提供了多种功能，包括语言理解、语言生成和多任务学习。这些功能使得开发者能够创建复杂的语言应用。

Before diving into Azure GPT-4, it's essential to have a solid understanding of the basics. Azure GPT-4 is a powerful language model that can be fine-tuned for various tasks.

To get started, you'll need to create an Azure account if you haven't already. This will give you access to the Azure portal, where you can manage your resources and configure your GPT-4 model.

The Azure portal is user-friendly and easy to navigate, even for those new to cloud computing. You can find tutorials and documentation to help you get started.

You'll also need to install the Azure CLI, which is a command-line tool that allows you to manage your Azure resources from the command line. This will come in handy when you're working with your GPT-4 model.

It's also a good idea to familiarize yourself with the Azure GPT-4 pricing model, which is based on the number of tokens processed. This will help you plan your budget and avoid any unexpected costs.

需要准备好几个变量，包括 token、订阅、resource_group、resource_name、model_deployment_name 和 fine_tuned_model。token 可以通过 Azure 门户启动 Cloud Shell 和运行 az account get-access-token 来获得。订阅、resource_group 和 resource_name 需要关联到 Azure OpenAI 资源。

fine_tuned_model 可以从上一步的微调作业结果中检索，类似于 gpt-4o-mini-2024-07-18.ft-0e208cf33a6a466994aff31a08aba678。

部署自定义模型后，如果部署处于非活动状态的时间超过 15 天，则会删除部署。删除非活动状态的部署时，不会删除或影响基础自定义模型，并且可以随时重新部署自定义模型。

部署的每个自定义（微调）模型都会产生每小时的托管成本，无论是否对模型进行补全或聊天补全调用。

GPT-4模型提供了多种选择，每种模型都有其特定的优势和应用场景。例如，GPT-4中有多种语言模型，包括英语、西班牙语、法语等。每种语言模型都经过了优化，适合于不同的应用场景。

选择模型时需要考虑的因素包括模型的大小、计算资源需求以及适用的场景。例如，GPT-4小模型适合于低资源场景，而GPT-4大模型则适合于高资源场景和复杂任务。

在Azure GPT-4教程中，我们将指导您如何选择合适的模型并配置其参数。

Now that you've learned the basics of Azure GPT-4, it's time to dive deeper.

To get a more in-depth understanding of Azure OpenAI, I recommend checking out the official documentation.

If you're looking to get started with GPT-4 Turbo with Vision quickly, the GPT-4 Turbo with Vision 快速入门 is a great resource.

Common questions about GPT-4 Turbo with Vision are answered in the GPT-4 Turbo with Vision 常见问答.

For those who want to integrate GPT-4 Turbo with Vision into their applications, the GPT-4 Turbo with Vision API 引用 provides the necessary information.

If you're interested in learning more about the GPT-4 Turbo with Vision API, here are some additional resources to explore:

To register and set up Azure OpenAI, you'll need to create an Azure account. This will give you a free $200 credit. You'll also need to provide some company information, such as your company email, address, and phone number.

To apply for the OpenAI service, you'll need to answer 25 questions, including using your company email and filling out company information accurately. Make sure to use the subscription ID you copied earlier when asked for it.

Once you've completed the application, you'll be able to create an Azure OpenAI service. This will involve configuring the service and selecting a model deployment.

To use the Azure OpenAI service, you'll need to get an API key and endpoint from the Azure OpenAI resource details page. You can use this to configure a client, such as openai-translator, by setting the API URL and path.

Here's what you'll need to do:

You can also use a Cloudflare Worker to map the Azure OpenAI service API to the OpenAI service API. This will allow you to use clients that don't support the Azure OpenAI service API.

To set up the Cloudflare Worker, you'll need to create a new worker and configure it to map the Azure OpenAI service API to the OpenAI service API. You'll need to set the `resourceName` and `deployName` variables, as well as the `apiVersion` variable.

On a similar theme: Azure Kubernetes Service vs Azure Container Apps

Once you've set up the Cloudflare Worker, you can use it to access the OpenAI service API. You'll need to set the API key and endpoint in the client configuration.

You can also use the cf-openai-azure-proxy to map the Azure OpenAI service API to the OpenAI service API. This will allow you to use clients that don't support the Azure OpenAI service API.

To set up the cf-openai-azure-proxy, you'll need to create a new worker and configure it to map the Azure OpenAI service API to the OpenAI service API. You'll need to set the `resourceName` and `deployName` variables, as well as the `apiVersion` variable.

Explore further: What Is Azure Openai