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Update deploy-jais-models.md
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articles/machine-learning/deploy-jais-models.md

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title: How to deploy JAIS models with Azure Machine Learning Studio
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titleSuffix: Azure Machine Learning Studio
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description: Learn how to deploy JAIS models with Azure Machine Learning Studio.
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title: How to deploy JAIS models with Azure Machine Learning studio
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titleSuffix: Azure Machine Learning studio
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description: Learn how to deploy JAIS models with Azure Machine Learning studio.
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manager: scottpolly
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ms.service: machine-learning
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ms.topic: how-to
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ms.custom: references_regions, build-2024
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# How to deploy JAIS with Azure Machine Learning Studio
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# How to deploy JAIS with Azure Machine Learning studio
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In this article, you learn how to use Azure Machine Learning Studio to deploy the JAIS model as a service with pay-as you go billing.
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In this article, you learn how to use Azure Machine Learning studio to deploy the JAIS model as a service with pay-as you go billing.
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The JAIS model is available in Azure Machine Learning Studio with pay-as-you-go token based billing with Models as a Service.
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The JAIS model is available in Azure Machine Learning studio with pay-as-you-go token based billing with Models as a Service.
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You can find the JAIS model in the model catalog by filtering on the JAIS collection.
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### JAIS 30b Chat
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JAIS 30b Chat is an auto-regressive bi-lingual LLM for **Arabic** & **English**. The tuned versions use supervised fine-tuning (SFT). The model is finetuned with both Arabic and English prompt-response pairs. The finetuning datasets included a wide range of instructional data across various domains. The model covers a wide range of common tasks including question answering, code generation, and reasoning over textual content. To enhance performance in Arabic, the Core42 team developed an in-house Arabic dataset as well as translating some open-source English instructions into Arabic.
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JAIS 30b Chat is an auto-regressive bi-lingual LLM for **Arabic** & **English**. The tuned versions use supervised fine-tuning (SFT). The model is fine-tuned with both Arabic and English prompt-response pairs. The fine-tuning datasets included a wide range of instructional data across various domains. The model covers a wide range of common tasks including question answering, code generation, and reasoning over textual content. To enhance performance in Arabic, the Core42 team developed an in-house Arabic dataset as well as translating some open-source English instructions into Arabic.
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*Context length:* JAIS 30b Chat supports a context length of 8K.
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