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articles/ai-foundry/concepts/models-featured.md

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## Microsoft
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Phi is a family of lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets. The datasets include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
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Microsoft models include various model groups such as MAI models, Phi models, healthcare AI models, and more. To see all the available Microsoft models, view [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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| Model | Type | Capabilities |
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| ------ | ---- | ------------ |
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| [MAI-DS-R1](https://ai.azure.com/explore/models/MAI-DS-R1/version/1/registry/azureml) | [chat-completion with reasoning content](../model-inference/how-to/use-chat-reasoning.md?context=/azure/ai-foundry/context/context) | - **Input:** text (163,840 tokens) <br /> - **Output:** text (163,840 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text. |
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| [Phi-4-multimodal-instruct](https://ai.azure.com/explore/models/Phi-4-multimodal-instruct/version/1/registry/azureml) | [chat-completion (with image and audio content)](../model-inference/how-to/use-chat-multi-modal.md?context=/azure/ai-foundry/context/context) | - **Input:** text, images, and audio (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-4-mini-instruct](https://ai.azure.com/explore/models/Phi-4-mini-instruct/version/1/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-4](https://ai.azure.com/explore/models/Phi-4/version/2/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (16,384 tokens) <br /> - **Output:** text (16,384 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-3-medium-128k-instruct](https://ai.azure.com/explore/models/Phi-3-medium-128k-instruct/version/6/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (131,072 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-3-medium-4k-instruct](https://ai.azure.com/explore/models/Phi-3-medium-4k-instruct/version/5/registry/azureml) | [chat-completion](../model-inference/how-to/use-chat-completions.md?context=/azure/ai-foundry/context/context) | - **Input:** text (4,096 tokens) <br /> - **Output:** text (4,096 tokens) <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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#### Inference examples: Microsoft models
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See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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#### Inference examples: Microsoft Phi
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For more examples of how to use Phi-3 family models, see the following examples:
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For more examples of how to use Microsoft models, see the following examples:
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| Description | Language | Sample |
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|-------------------------------------------|-------------------|-----------------------------------------------------------------|
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| Llama-Index | Python | [Link](https://aka.ms/azureai/llamaindex) |
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See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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## Mistral AI
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Mistral AI offers two categories of models, namely:

articles/ai-foundry/includes/region-availability-maas.md

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Llama 3.1 405B Instruct | [Microsoft Managed Countries/Regions](/partner-center/marketplace/tax-details-marketplace#microsoft-managed-countriesregions) | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3 | Not available |
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### Microsoft Phi family models
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### Microsoft models
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| Model | Offer Availability Region | Hub/Project Region for Deployment | Hub/Project Region for Fine tuning |
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|---------|---------|---------|---------|
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MAI-DS-R1 | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> West US <br> West US 3 | Not available |
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Phi-4 <br> Phi-4-mini-instruct <br> Phi-4-multimodal-instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | East US 2 <br> East US <br> North Central US <br> South Central US <br> West US <br> West US 3 |
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Phi-3.5-vision-Instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | Not available |
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Phi-3.5-MoE-Instruct | Not applicable | East US <br> East US 2 <br> North Central US <br> South Central US <br> Sweden Central <br> West US <br> West US 3 | East US 2 <br> East US <br> North Central US <br> South Central US <br> West US <br> West US 3 |

articles/ai-foundry/model-inference/concepts/models.md

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### Microsoft
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Phi is a family of lightweight, state-of-the-art open models. These models were trained with Phi-3 datasets. The datasets include both synthetic data and the filtered, publicly available websites data, with a focus on high quality and reasoning-dense properties. The models underwent a rigorous enhancement process, incorporating both supervised fine-tuning, proximal policy optimization, and direct preference optimization to ensure precise instruction adherence and robust safety measures.
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Microsoft models include various model groups such as MAI models, Phi models, healthcare AI models, and more. To see all the available Microsoft models, view [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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| Model | Type | Tier | Capabilities |
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| ------ | ---- | --- | ------------ |
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| [MAI-DS-R1](https://ai.azure.com/explore/models/MAI-DS-R1/version/1/registry/azureml) | chat-completion <br /> [(with reasoning content)](../how-to/use-chat-reasoning.md) | Global standard | - **Input:** text (163,840 tokens) <br /> - **Output:** (163,840 tokens) <br /> - **Languages:** `en` and `zh` <br /> - **Tool calling:** No <br /> - **Response formats:** Text. |
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| [Phi-4-mini-instruct](https://ai.azure.com/explore/models/Phi-4-mini-instruct/version/1/registry/azureml) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** (4,096 tokens) <br /> - **Languages:** `ar`, `zh`, `cs`, `da`, `nl`, `en`, `fi`, `fr`, `de`, `he`, `hu`, `it`, `ja`, `ko`, `no`, `pl`, `pt`, `ru`, `es`, `sv`, `th`, `tr`, and `uk` <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-4-multimodal-instruct](https://ai.azure.com/explore/models/Phi-4-multimodal-instruct/version/1/registry/azureml) | chat-completion | Global standard | - **Input:** text, images, and audio (131,072 tokens) <br /> - **Output:** (4,096 tokens) <br /> - **Languages:** `ar`, `zh`, `cs`, `da`, `nl`, `en`, `fi`, `fr`, `de`, `he`, `hu`, `it`, `ja`, `ko`, `no`, `pl`, `pt`, `ru`, `es`, `sv`, `th`, `tr`, and `uk` <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-4](https://ai.azure.com/explore/models/Phi-4/version/2/registry/azureml) | chat-completion | Global standard | - **Input:** text (16,384 tokens) <br /> - **Output:** (16,384 tokens) <br /> - **Languages:** `en`, `ar`, `bn`, `cs`, `da`, `de`, `el`, `es`, `fa`, `fi`, `fr`, `gu`, `ha`, `he`, `hi`, `hu`, `id`, `it`, `ja`, `jv`, `kn`, `ko`, `ml`, `mr`, `nl`, `no`, `or`, `pa`, `pl`, `ps`, `pt`, `ro`, `ru`, `sv`, `sw`, `ta`, `te`, `th`, `tl`, `tr`, `uk`, `ur`, `vi`, `yo`, and `zh` <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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| [Phi-3-small-128k-instruct](https://ai.azure.com/explore/models/Phi-3-small-128k-instruct/version/4/registry/azureml) | chat-completion | Global standard | - **Input:** text (131,072 tokens) <br /> - **Output:** (4,096 tokens) <br /> - **Languages:** `en` <br /> - **Tool calling:** No <br /> - **Response formats:** Text |
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See [this model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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See [the Microsoft model collection in Azure AI Foundry portal](https://ai.azure.com/explore/models?&selectedCollection=phi).
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### Mistral AI
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articles/ai-services/openai/audio-completions-quickstart.md

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::: zone-end
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## Clean-up resources
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If you want to clean up and remove an Azure OpenAI resource, you can delete the resource. Before deleting the resource, you must first delete any deployed models.
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- [Azure portal](../multi-service-resource.md?pivots=azportal#clean-up-resources)
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- [Azure CLI](../multi-service-resource.md?pivots=azcli#clean-up-resources)
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## Troubleshooting
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> [!NOTE]
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> When using `gpt-4o-audio-preview` for chat completions with the audio modality and `stream` is set to true the only supported audio format is pcm16.
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## Related content
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* Learn more about Azure OpenAI [deployment types](./how-to/deployment-types.md).

articles/ai-services/openai/concepts/prompt-engineering.md

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# Prompt engineering techniques
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GPT-3, GPT-3.5, GPT-4, and GPT-4o models from OpenAI are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. This completion is the model’s continuation of the input text. These techniques are note recommended for o-series models.
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GPT-3, GPT-3.5, GPT-4, and GPT-4o models from OpenAI are prompt-based. With prompt-based models, the user interacts with the model by entering a text prompt, to which the model responds with a text completion. This completion is the model’s continuation of the input text. These techniques are not recommended for o-series models.
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While these models are extremely powerful, their behavior is also very sensitive to the prompt. This makes prompt construction an important skill to develop.
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articles/ai-services/openai/includes/api-versions/latest-inference-preview.md

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| Name | Type | Description | Required | Default |
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| audio | object | Parameters for audio output. Required when audio output is requested with<br>`modalities: ["audio"]`. <br> | No | |
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| └─ format | enum | Specifies the output audio format. Must be one of `wav`, `mp3`, `flac`,<br>`opus`, or `pcm16`. <br><br>Possible values: `wav`, `mp3`, `flac`, `opus`, `pcm16` | No | |
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| └─ format | enum | Specifies the output audio format. Must be one of `wav`, `mp3`, `flac`,<br>`opus`, or `pcm16`. <br><br>Possible values: `wav`, `mp3`, `flac`, `opus`, `pcm16`<br> When using `gpt-4o-audio-preview` and `stream` is set to true the only supported audio format is `pcm16`. | No | |
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| └─ voice | enum | Specifies the voice type. Supported voices are `alloy`, `echo`, <br>`fable`, `onyx`, `nova`, and `shimmer`.<br><br>Possible values: `alloy`, `echo`, `fable`, `onyx`, `nova`, `shimmer` | No | |
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| data_sources | array | The configuration entries for Azure OpenAI chat extensions that use them.<br> This additional specification is only compatible with Azure OpenAI. | No | |
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| frequency_penalty | number | Number between -2.0 and 2.0. Positive values penalize new tokens based on their existing frequency in the text so far, decreasing the model's likelihood to repeat the same line verbatim.<br> | No | 0 |

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