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How to deploy AI21's Jamba-Instruct model with Azure Machine Learning studio

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In this article, you learn how to use Azure Machine Learning studio to deploy AI21's Jamba-Instruct model as a serverless API with pay-as-you-go billing.

The Jamba Instruct model is AI21's production-grade Mamba-based large language model (LLM) which leverages AI21's hybrid Mamba-Transformer architecture. It's an instruction-tuned version of AI21's hybrid structured state space model (SSM) transformer Jamba model. The Jamba Instruct model is built for reliable commercial use with respect to quality and performance.

Important

This feature is currently in public preview. This preview version is provided without a service-level agreement, and we don't recommend it for production workloads. Certain features might not be supported or might have constrained capabilities.

For more information, see Supplemental Terms of Use for Microsoft Azure Previews.

Deploy the Jamba Instruct model as a serverless API

Certain models in the model catalog can be deployed as a serverless API with pay-as-you-go billing, providing a way to consume them as an API without hosting them on your subscription, while keeping the enterprise security and compliance organizations need. This deployment option doesn't require quota from your subscription.

The AI21-Jamba-Instruct model deployed as a serverless API with pay-as-you-go billing is offered by AI21 through Microsoft Azure Marketplace. AI21 can change or update the terms of use and pricing of this model.

To get started with Jamba Instruct deployed as a serverless API, explore our integrations with LangChain, LiteLLM, OpenAI and the Azure API.

Tip

See our announcements of AI21's Jamba-Instruct model available now on Azure AI Model Catalog through AI21's blog and Microsoft Tech Community Blog.

Prerequisites

  • An Azure subscription with a valid payment method. Free or trial Azure subscriptions won't work. If you don't have an Azure subscription, create a paid Azure account to begin.

  • An Azure Machine Learning workspace and a compute instance. If you don't have these, use the steps in the Quickstart: Create workspace resources article to create them. The serverless API model deployment offering for Jamba Instruct is only available with workspaces created in these regions:

    • East US
    • East US 2
    • North Central US
    • South Central US
    • West US
    • West US 3
    • Sweden Central

    For a list of regions that are available for each of the models supporting serverless API endpoint deployments, see Region availability for models in serverless API endpoints.

  • Azure role-based access controls (Azure RBAC) are used to grant access to operations in Azure Machine Learning. To perform the steps in this article, your user account must be assigned the owner or contributor role for the Azure subscription. Alternatively, your account can be assigned a custom role that has the following permissions:

    • On the Azure subscription—to subscribe the workspace to the Azure Marketplace offering, once for each workspace, per offering:

      • Microsoft.MarketplaceOrdering/agreements/offers/plans/read
      • Microsoft.MarketplaceOrdering/agreements/offers/plans/sign/action
      • Microsoft.MarketplaceOrdering/offerTypes/publishers/offers/plans/agreements/read
      • Microsoft.Marketplace/offerTypes/publishers/offers/plans/agreements/read
      • Microsoft.SaaS/register/action

    • On the resource group—to create and use the SaaS resource:

      • Microsoft.SaaS/resources/read
      • Microsoft.SaaS/resources/write

    • On the workspace—to deploy endpoints (the Azure Machine Learning data scientist role contains these permissions already):

      • Microsoft.MachineLearningServices/workspaces/marketplaceModelSubscriptions/*
      • Microsoft.MachineLearningServices/workspaces/serverlessEndpoints/*

    For more information on permissions, see Manage access to an Azure Machine Learning workspace.

Create a new deployment

These steps demonstrate the deployment of AI21-Jamba-Instruct. To create a deployment:

  1. Go to Azure Machine Learning studio.

  2. Select the workspace in which you want to deploy your models. To use the Serverless API model deployment offering, your workspace must belong to the East US 2 or Sweden Central region.

  3. Choose the model you want to deploy from the model catalog.

    Alternatively, you can initiate deployment by going to your workspace and selecting Endpoints > Serverless endpoints > Create.

  4. On the model's overview page in the model catalog, select Deploy and then Serverless API with Azure AI Content Safety.

  5. In the deployment wizard, select the link to Azure Marketplace Terms to learn more about the terms of use.

  6. You can also select the Marketplace offer details tab to learn about pricing for the selected model.

  7. If this is your first time deploying the model in the workspace, you have to subscribe your workspace for the particular offering from Azure Marketplace. This step requires that your account has the Azure subscription permissions and resource group permissions listed in the prerequisites. Each workspace has its own subscription to the particular Azure Marketplace offering, which allows you to control and monitor spending. Select Subscribe and Deploy. Currently you can have only one deployment for each model within a workspace.

  8. Once you sign up the workspace for the particular Azure Marketplace offering, subsequent deployments of the same offering in the same workspace don't require subscribing again. Therefore, you don't need to have the subscription-level permissions for subsequent deployments. If this scenario applies to you, select Continue to deploy.

  9. Give the deployment a name. This name becomes part of the deployment API URL. This URL must be unique in each Azure region.

  10. Select Deploy. Wait until the deployment is finished and you're redirected to the serverless endpoints page.

  11. Select the endpoint to open its Details page.

  12. Select the Test tab to start interacting with the model.

  13. You can also take note of the Target URL and the Secret Key to call the deployment and generate completions.

  14. You can always find the endpoint's details, URL, and access keys by navigating to Workspace > Endpoints > Serverless endpoints.

To learn about billing for Jamba models deployed as a serverless API, see Cost and quota considerations for Jamba Instruct deployed as a serverless API.

Consume Jamba Instruct as a service

You can consume Jamba Instruct models as follows:

  1. In the workspace, select Endpoints > Serverless endpoints.
  2. Find and select the deployment you created.
  3. Copy the Target URL and the Key token values.
  4. Make an API request using either the Azure AI Model Inference API on the route /chat/completions or the AI21's Azure Client on /v1/chat/completions.

For more information on using the APIs, see the reference section.

Reference for Jamba Instruct deployed as a serverless API

Jamba Instruct models accept both of these APIs:

Azure AI model inference API

The Azure AI Model Inference API schema can be found in the reference for Chat Completions article and an OpenAPI specification can be obtained from the endpoint itself.

Single-turn and multi-turn chat have the same request and response format, except that question answering (single-turn) involves only a single user message in the request, while multi-turn chat requires that you send the entire chat message history in each request.

In a multi-turn chat, the message thread has the following attributes:

  • Includes all messages from the user and the model, ordered from oldest to newest.
  • Messages alternate between user and assistant role messages
  • Optionally, the message thread starts with a system message to provide context.

The following pseudocode is an example of the message stack for the fourth call in a chat request that includes an initial system message.

[
    {"role": "system", "message": "Some contextual information here"},
    {"role": "user", "message": "User message 1"},
    {"role": "assistant", "message": "System response 1"},
    {"role": "user", "message": "User message 2"},
    {"role": "assistant"; "message": "System response 2"},
    {"role": "user", "message": "User message 3"},
    {"role": "assistant", "message": "System response 3"},
    {"role": "user", "message": "User message 4"}
]

AI21's Azure client

Use the method POST to send the request to the /v1/chat/completions route:

Request

POST /v1/chat/completions HTTP/1.1
Host: <DEPLOYMENT_URI>
Authorization: Bearer <TOKEN>
Content-type: application/json

Request schema

Payload is a JSON formatted string containing the following parameters:

Key Type Required/Default Allowed values Description
model string Y Must be jamba-instruct
messages list[object] Y A list of objects, one per message, from oldest to newest. The oldest message can be role system. All later messages must alternate between user and assistant roles. See the message object definition below.
max_tokens integer N
4096		 0 – 4096 The maximum number of tokens to allow for each generated response message. Typically the best way to limit output length is by providing a length limit in the system prompt (for example, "limit your answers to three sentences")
temperature float N
1		 0.0 – 2.0 How much variation to provide in each answer. Setting this value to 0 guarantees the same response to the same question every time. Setting a higher value encourages more variation. Modifies the distribution from which tokens are sampled. We recommend altering this or top_p, but not both.
top_p float N
1		 0 < value <=1.0 Limit the pool of next tokens in each step to the top N percentile of possible tokens, where 1.0 means the pool of all possible tokens, and 0.01 means the pool of only the most likely next tokens.
stop string OR list[string] N
"" String or list of strings containing the word(s) where the API should stop generating output. Newlines are allowed as "\n". The returned text won't contain the stop sequence.
n integer N
1		 1 – 16 How many responses to generate for each prompt. With Azure AI Studio's Playground, n=1 as we work on multi-response Playground.
stream boolean N
False		 True OR False Whether to enable streaming. If true, results are returned one token at a time. If set to true, n must be 1, which is automatically set.

The messages object has the following fields:

  • role: [string, required] The author or purpose of the message. One of the following values:
    • user: Input provided by the user. Any instructions given here that conflict with instructions given in the system prompt take precedence over the system prompt instructions.
    • assistant: A response generated by the model.
    • system: Initial instructions to provide general guidance on the tone and voice of the generated message. An initial system message is optional, but recommended to provide guidance on the tone of the chat. For example, "You are a helpful chatbot with a background in earth sciences and a charming French accent."
  • content: [string, required] The content of the message.

Request example

Single-turn example

{
    "model": "jamba-instruct",
    "messages": [
    {
      "role":"user",
      "content":"Who was the first emperor of rome?"}
  ],
    "temperature": 0.8,
    "max_tokens": 512
}

Chat example (fourth request containing third user response)

{
  "model": "jamba-instruct",
  "messages": [
     {"role": "system",
      "content": "You are a helpful genie just released from a bottle. You start the conversation with 'Thank you for freeing me! I grant you one wish.'"},
     {"role":"user",
      "content":"I want a new car"},
     {"role":"assistant",
      "content":"🚗 Great choice, I can definitely help you with that! Before I grant your wish, can you tell me what kind of car you're looking for?"},
     {"role":"user",
      "content":"A corvette"},
     {"role":"assistant",
      "content":"Great choice! What color and year?"},
     {"role":"user",
      "content":"1963 black split window Corvette"}
  ],
  "n":3
}

Response schema

The response depends slightly on whether the result is streamed or not.

In a non-streamed result, all responses are delivered together in a single response, which also includes a usage property.

In a streamed result,

  • Each response includes a single token in the choices field.
  • The choices object structure is different.
  • Only the last response includes a usage object.
  • The entire response is wrapped in a data object.
  • The final response object is data: [DONE].

The response payload is a dictionary with the following fields.

Key Type Description
id string A unique identifier for the request.
model string Name of the model used.
choices list[object] The model-generated response text. For a non-streaming response it is a list with n items. For a streaming response, it is a single object containing a single token. See the object description below.
created integer The Unix timestamp (in seconds) of when the completion was created.
object string The object type, which is always chat.completion.
usage object Usage statistics for the completion request. See below for details.

The choices response object contains the model-generated response. The object has the following fields:

Key Type Description
index integer Zero-based index of the message in the list of messages. Might not correspond to the position in the list. For streamed messages this is always zero.
message OR delta object The generated message (or token in a streaming response). Same object type as described in the request with two changes:
- In a non-streaming response, this object is called message.
- In a streaming response, it is called delta, and contains either message or role but never both.
finish_reason string The reason the model stopped generating tokens:
- stop: The model reached a natural stop point, or a provided stop sequence.
- length: Max number of tokens have been reached.
- content_filter: The generated response violated a responsible AI policy.
- null: Streaming only. In a streaming response, all responses except the last will be null.

The usage response object contains the following fields.

Key Type Value
prompt_tokens integer Number of tokens in the prompt. Note that the prompt token count includes extra tokens added by the system to format the prompt list into a single string as required by the model. The number of extra tokens is typically proportional to the number of messages in the thread, and should be relatively small.
completion_tokens integer Number of tokens generated in the completion.
total_tokens integer Total tokens.

Non-streaming response example

{
  "id":"cmpl-524c73beb8714d878e18c3b5abd09f2a",
  "choices":[
    {
      "index":0,
      "message":{
        "role":"assistant",
        "content":"The human nose can detect over 1 trillion different scents, making it one of the most sensitive smell organs in the animal kingdom."
      },
      "finishReason":"stop"
    }
  ],
  "created": 1717487036,
  "usage":{
    "promptTokens":116,
    "completionTokens":30,
    "totalTokens":146
  }
}

Streaming response example

data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"role": "assistant"}, "created": 1717487336, "finish_reason": null}]}
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": ""}, "created": 1717487336, "finish_reason": null}]}
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": " The"}, "created": 1717487336, "finish_reason": null}]}
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": " first e"}, "created": 1717487336, "finish_reason": null}]}
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": "mpe"}, "created": 1717487336, "finish_reason": null}]}
... 115 responses omitted for sanity ...
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": "me"}, "created": 1717487336, "finish_reason": null}]}
data: {"id": "cmpl-8e8b2f6556f94714b0cd5cfe3eeb45fc", "choices": [{"index": 0, "delta": {"content": "."}, "created": 1717487336,"finish_reason": "stop"}], "usage": {"prompt_tokens": 107, "completion_tokens": 121, "total_tokens": 228}}
data: [DONE]

Cost and quotas

Cost and quota considerations for Jamba Instruct deployed as a serverless API

Jamba models deployed as a serverless API are offered by AI21 through Azure Marketplace and integrated with Azure Machine Learning studio for use. You can find Azure Marketplace pricing when deploying or fine-tuning models.

Each time a workspace subscribes to a given model offering from Azure Marketplace, a new resource is created to track the costs associated with its consumption. The same resource is used to track costs associated with inference and fine-tuning; however, multiple meters are available to track each scenario independently.

For more information on how to track costs, see Monitor costs for models offered through the Azure Marketplace.

Quota is managed per deployment. Each deployment has a rate limit of 200,000 tokens per minute and 1,000 API requests per minute. However, we currently limit one deployment per model per project. Contact Microsoft Azure Support if the current rate limits aren't sufficient for your scenarios.

Content filtering

Models deployed as a serverless API are protected by Azure AI content safety. With Azure AI content safety enabled, both the prompt and completion pass through an ensemble of classification models aimed at detecting and preventing the output of harmful content. The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Learn more about Azure AI Content Safety.

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