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articles/ai-foundry/how-to/deploy-models-jais.md

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ms.service: azure-ai-foundry
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ms.topic: how-to
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ms.date: 11/21/2024
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ms.date: 03/04/2025
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ms.reviewer: haelhamm
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ms.author: ssalgado

articles/ai-foundry/how-to/deploy-models-jamba.md

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ms.service: azure-ai-foundry
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ms.topic: how-to
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ms.date: 08/06/2024
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ms.date: 03/04/2025
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articles/ai-foundry/how-to/deploy-models-llama.md

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ms.topic: how-to
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ms.date: 12/04/2024
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ms.date: 03/04/2025
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articles/ai-foundry/how-to/deploy-models-tsuzumi.md

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ms.topic: how-to
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ms.date: 10/24/2024
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ms.date: 03/04/2025
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articles/ai-services/agents/includes/quickstart-javascript.md

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| Run | Activation of an agent to begin running based on the contents of Thread. The agent uses its configuration and Thread’s Messages to perform tasks by calling models and tools. As part of a Run, the agent appends Messages to the Thread. |
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| Run Step | A detailed list of steps the agent took as part of a Run. An agent can call tools or create Messages during its run. Examining Run Steps allows you to understand how the agent is getting to its results. |
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Run the following commands to install the npm packages.
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First, initialize a new project by running:
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```console
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npm init -y
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```
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Run the following commands to install the npm packages required.
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```console
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npm install @azure/ai-projects
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npm install @azure/identity
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npm install dotenv
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```
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Next, to authenticate your API requests and run the program, use the [az login](/cli/azure/authenticate-azure-cli-interactively) command to sign into your Azure subscription.
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`eastus.api.azureml.ms;12345678-abcd-1234-9fc6-62780b3d3e05;my-resource-group;my-project-name`
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Set this connection string as an environment variable named `PROJECT_CONNECTION_STRING`.
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Set this connection string as an environment variable named `PROJECT_CONNECTION_STRING` in a `.env` file.
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Next, create an `index.js` file and paste in the code below:
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```javascript
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// index.js
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ToolUtility,
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} from "@azure/ai-projects";
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import { DefaultAzureCredential } from "@azure/identity";
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import dotenv from 'dotenv';
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dotenv.config();
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const connectionString =
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process.env["AZURE_AI_PROJECTS_CONNECTION_STRING"] || "<project connection string>";
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// Set the connection string from the environment variable
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const connectionString = process.env.PROJECT_CONNECTION_STRING;
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// Throw an error if the connection string is not set
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if (!connectionString) {
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throw new Error("AZURE_AI_PROJECTS_CONNECTION_STRING must be set in the environment variables");
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throw new Error("Please set the PROJECT_CONNECTION_STRING environment variable.");
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}
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export async function main() {
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// messages[0] is the most recent
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for (let i = messages.data.length - 1; i >= 0; i--) {
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const m = messages.data[i];
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if (isOutputOfType(m.content[0], "text")) {
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if (m.content && m.content.length > 0 && isOutputOfType(m.content[0], "text")) {
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const textContent = m.content[0];
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console.log(`${textContent.text.value}`);
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console.log(`---------------------------------`);
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console.error("The sample encountered an error:", err);
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});
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```
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Run the code using `node index.js` and observe.

articles/ai-services/openai/concepts/provisioned-throughput.md

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To help with simplifying the sizing effort, the following table outlines the TPM per PTU for the specified models. To understand the impact of output tokens on the TPM per PTU limit, use the 3 input token to 1 output token ratio. For a detailed understanding of how different ratios of input and output tokens impact the throughput your workload needs, see the [Azure OpenAI capacity calculator](https://oai.azure.com/portal/calculator). The table also shows Service Level Agreement (SLA) Latency Target Values per model. For more information about the SLA for Azure OpenAI Service, see the [Service Level Agreements (SLA) for Online Services page](https://www.microsoft.com/licensing/docs/view/Service-Level-Agreements-SLA-for-Online-Services?lang=1)
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|Topic| **gpt-4o** | **gpt-4o-mini** |
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| --- | --- | --- |
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|Global & data zone provisioned minimum deployment|15|15|
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|Global & data zone provisioned scale increment|5|5|
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|Regional provisioned minimum deployment | 50 | 25|
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|Regional provisioned scale increment|50|25|
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|Input TPM per PTU | 2,500 | 37,000 |
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|Latency Target Value |25 Tokens Per Second|33 Tokens Per Second|
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|Topic| **gpt-4o** | **gpt-4o-mini** | **o1**|
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| --- | --- | --- | --- |
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|Global & data zone provisioned minimum deployment|15|15|15|
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|Global & data zone provisioned scale increment|5|5|5|
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|Regional provisioned minimum deployment|50|25|50|
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|Regional provisioned scale increment|50|25|50|
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|Input TPM per PTU |2,500|37,000|230|
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|Latency Target Value |25 Tokens Per Second|33 Tokens Per Second|25 Tokens Per Second|
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For a full list see the [Azure OpenAI Service in Azure AI Foundry portal calculator](https://oai.azure.com/portal/calculator).
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articles/ai-services/openai/how-to/fine-tuning-deploy.md

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:::image type="content" source="../media/fine-tuning/global-standard.png" alt-text="Screenshot of the global standard deployment user experience with a fine-tuned model." lightbox="../media/fine-tuning/global-standard.png":::
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Global Standard fine-tuning deployments currently do not support vision and structured outputs.
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Global Standard fine-tuned deployments currently support structured outputs only on GPT-4o.
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### Provisioned Managed (preview)
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[Provisioned managed](./deployment-types.md#provisioned) fine-tuned deployments offer [predictable performance](../concepts/provisioned-throughput.md#what-do-the-provisioned-deployment-types-provide) for fine-tuned deployments. As part of public preview, provisioned managed deployments may be created regionally via the data-plane [REST API](../reference.md#data-plane-inference) version `2024-10-01` or newer. See below for examples.
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Provisioned Managed fine-tuning deployments currently do not support vision and structured outputs.
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Provisioned Managed fine-tuned deployments currently support structured outputs only on GPT-4o.
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#### Creating a Provisioned Managed deployment
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articles/ai-services/openai/how-to/fine-tuning-vision.md

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Fine-tuning is also possible with images in your JSONL files. Just as you can send one or many image inputs to chat completions, you can include those same message types within your training data. Images can be provided either as publicly accessible URLs or data URIs containing [base64 encoded images](/azure/ai-services/openai/how-to/gpt-with-vision?tabs=rest#call-the-chat-completion-apis).
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## Model support
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Vision fine-tuning is supported for `gpt-4o` version `2024-08-06` models only.
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- Your training file can contain a maximum of 50,000 examples that contain images (not including text examples).

articles/ai-services/openai/includes/model-matrix/provisioned-global.md

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---
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| **Region** | **gpt-4o**, **2024-05-13** | **gpt-4o**, **2024-08-06** | **gpt-4o**, **2024-11-20** | **gpt-4o-mini**, **2024-07-18** |
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|:-------------------|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|
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| australiaeast |||||
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| brazilsouth |||||
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| canadacentral |||||
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| canadaeast |||||
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| eastus |||||
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| francecentral |||||
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| germanywestcentral |||||
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| **Region** | **o1**, **2024-12-17** | **gpt-4o**, **2024-05-13** | **gpt-4o**, **2024-08-06** | **gpt-4o**, **2024-11-20** | **gpt-4o-mini**, **2024-07-18** |
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|:-------------------|:----------------------:|:--------------------------:|:--------------------------:|:--------------------------:|:-------------------------------:|
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| australiaeast | - |||||
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| brazilsouth | - |||||
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| canadacentral | - |||||
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| canadaeast | - |||||
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| eastus ||||||
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| eastus2 | - |||||
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| francecentral ||||||
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| germanywestcentral | - |||||
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| italynorth | - |||||
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| japaneast | - |||||
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| koreacentral | - |||||
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| northcentralus | - |||||
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| norwayeast | - |||||
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| polandcentral | - |||||
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| spaincentral ||||||
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| switzerlandnorth ||||||
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| switzerlandwest ||||||
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articles/ai-services/openai/quotas-limits.md

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### `o3-mini` data zone standard
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| Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |
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| `o3-mini` | Enterprise agreement | 20 M | 2 K |
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| Model|Tier| Quota Limit in tokens per minute (TPM) | Requests per minute |

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