Select your cookie preferences

We use essential cookies and similar tools that are necessary to provide our site and services. We use performance cookies to collect anonymous statistics, so we can understand how customers use our site and make improvements. Essential cookies cannot be deactivated, but you can choose “Customize” or “Decline” to decline performance cookies.

If you agree, AWS and approved third parties will also use cookies to provide useful site features, remember your preferences, and display relevant content, including relevant advertising. To accept or decline all non-essential cookies, choose “Accept” or “Decline.” To make more detailed choices, choose “Customize.”
Sign in
Sign in
or
Create a new account
About
Categories
Delivery Methods
Solutions
Resources
 Your Saved List Become a Channel Partner Sell in AWS Marketplace Amazon Web Services Home Help

Amazon Sagemaker

Amazon SageMaker is a fully-managed platform that enables developers and data scientists to quickly and easily build, train, and deploy machine learning models at any scale. With Amazon SageMaker, all the barriers and complexity that typically slow down developers who want to use machine learning are removed. The service includes models that can be used together or independently to build, train, and deploy your machine learning models.

product logo

Command R 08-2024 (H100) Free trial

By:
Cohere
Latest Version:
v1.0.5
Command R 08-2024 is a generative language model optimized for large scale production workloads.
Continue to Subscribe

    Product Overview

    Command R 08-2024 is an updated highly performant generative large language model. It is designed to excel across a variety of use cases including reasoning, summarization, and question answering, with increased performance on coding, math, and reasoning. The model has the capability for multilingual generation evaluated in 10 key business languages, alongside pre-training for an additional 13 languages. With highly performant RAG capabilities, it is built for enterprises that plan to leverage their internal data and documents for a tailored and accurate language model. Batch transform is not supported with this model.

    Key Data

    Version
    v1.0.5
    Usage Instructions
    Release Notes
    Show other versions
    By
    Cohere
    Categories
    Generation-Text
    Natural Language Processing
    Summarization-Text
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Command R 08-2024 utilizes retrieval-augmented generation (RAG) to deliver accurate and verified responses. This approach ensures credible and trustworthy interactions by providing clear and in-line citations for users to verify. With RAG, Command R 08-2024 can be grounded in internal documents and data, providing accurate and actional model outputs.

    • Command R 08-2024 has a 128k token context window. This updated model excels at tool use, which can be implemented to build user-defined tools. These tools enable the model to automate highly sophisticated tasks using internal infrastructure as well as external tools.

    • Command R 08-2024 is proficient in the 10 most commonly used business languages (Arabic, Mandarin, English, French, German, Italian, Spanish, Japanese, Korean, and Portuguese) and pre-trained on 13 additional languages, ensuring seamless understanding and response in various tongues. The model excels at long-context tasks such as: document summarization, content Q&A, long-form generation, and content generation amongst others. It can power knowledge assistants, chatbots, customer support agents and more.

    Not quite sure what you’re looking for? AWS Marketplace can help you find the right solution for your use case. Contact us

    Pricing Information

    Use this tool to estimate the software and infrastructure costs based your configuration choices. Your usage and costs might be different from this estimate. They will be reflected on your monthly AWS billing reports.

    Contact us to request contract pricing for this product.


    Estimating your costs

    Choose your region and launch option to see the pricing details. Then, modify the estimated price by choosing different instance types.

    Version
    Region

    Software Pricing

    Model Realtime Inference$16.93/hr

    running on ml.p5.48xlarge

    Model Batch Transform$16.93/hr

    running on ml.g4dn.12xlarge

    Infrastructure Pricing

    With Amazon SageMaker, you pay only for what you use. Training and inference is billed by the second, with no minimum fees and no upfront commitments. Pricing within Amazon SageMaker is broken down by on-demand ML instances, ML storage, and fees for data processing in notebooks and inference instances.
    Learn more about SageMaker pricing

    SageMaker Realtime Inference$113.07/host/hr

    running on ml.p5.48xlarge

    SageMaker Batch Transform$4.89/host/hr

    running on ml.g4dn.12xlarge

    About Free trial

    Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.

    Model Realtime Inference

    For model deployment as Real-time endpoint in Amazon SageMaker, the software is priced based on hourly pricing that can vary by instance type. Additional infrastructure cost, taxes or fees may apply.
    InstanceType
    Realtime Inference/hr
    ml.p5.48xlarge
    Vendor Recommended
    $16.93

    Usage Information

    Model input and output details

    Input

    Summary

    The model accepts JSON requests with parameters that can be used to control the generated text. See examples and fields descriptions below.

    Input MIME type
    application/json
    Sample input data
    Expand all input descriptions
    Field name
    message
    Description
    Text input for the model to respond to.
    Constraints

    Type: FreeText

    Required
    Yes
    Field name
    chat_history
    Description
    chat_history – (array of messages) A list of previous messages between the user and the model, meant to give the model conversational context for responding to the user's message. Required fields: role – (enum string) Takes “USER” or “CHATBOT”. message – (string) Text contents of the message.
    Constraints

    Default value: [] Type: FreeText

    Required
    No
    Field name
    documents
    Description
    A list of texts that the model can cite to generate a more accurate reply. Each document is a string-string dictionary. The resulting generation will include citations that reference some of these documents. It is recommended to keep the total word count of the strings in the dictionary to under 300 words. An `_excludes` field (array of strings) can be optionally supplied to omit some key-value pairs from being shown to the model.
    Constraints

    Default value: [] Type: FreeText

    Required
    No
    Field name
    search_queries_only
    Description
    When `true`, the response will only contain a list of generated search queries, but no search will take place, and no reply from the model to the user's `message` will be generated.
    Constraints

    Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE

    Required
    No
    Field name
    preamble
    Description
    Overrides the default preamble for search query generation. Has no effect on tool use generations.
    Constraints

    Default value: [] Type: FreeText

    Required
    No
    Field name
    stream
    Description
    When `true`, the response will be a JSON stream of events. The final event will contain the complete response, and will have an `event_type` of `"stream-end"`. Streaming is beneficial for user interfaces that render the contents of the response piece by piece, as it gets generated.
    Constraints

    Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE

    Required
    No
    Field name
    max_tokens
    Description
    The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations.
    Constraints

    Default value: [] Type: Integer Minimum: 0

    Required
    No
    Field name
    temperature
    Description
    Use a lower value to decrease randomness in the response. Randomness can be further maximized by increasing the value of the `p` parameter.
    Constraints

    Default value: 0.3 Type: Continuous Minimum: 0 Maximum: 2

    Required
    No
    Field name
    Top P (p)
    Description
    Use a lower value to ignore less probable options. Set to 0 or 1.0 to disable. If both p and k are enabled, p acts after k.
    Constraints

    Default value: 0.75 Type: Continuous Minimum: 0.01 Maximum: 0.99

    Required
    No
    Field name
    Top K (k)
    Description
    Specify the number of token choices the model uses to generate the next token. If both p and k are enabled, p acts after k.
    Constraints

    Default value: 0 Type: Continuous Minimum: 0 Maximum: 500

    Required
    No
    Field name
    prompt_truncation
    Description
    Dictates how the prompt will be constructed. With `prompt_truncation` set to "AUTO_PRESERVE_ORDER", some elements from `chat_history` and `documents` will be dropped to construct a prompt that fits within the model's context length limit. During this process the order of the documents and chat history will be preserved. With `prompt_truncation` set to "OFF", no elements will be dropped.
    Constraints

    Default value: OFF Type: Categorical Allowed values: AUTO_PRESERVE_ORDER, AUTO, OFF

    Required
    No
    Field name
    frequency_penalty
    Description
    Used to reduce repetitiveness of generated tokens. The higher the value, the stronger a penalty is applied to previously present tokens, proportional to how many times they have already appeared in the prompt or prior generation.
    Constraints

    Default value: 0.0 Type: Continuous Minimum: 0.0 Maximum: 1.0

    Required
    No
    Field name
    presence_penalty
    Description
    Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies.
    Constraints

    Default value: 0.0 Type: Continuous Minimum: 0.0 Maximum: 1.0

    Required
    No
    Field name
    seed
    Description
    If specified, the backend will make a best effort to sample tokens deterministically, such that repeated requests with the same seed and parameters should return the same result. However, determinism cannot be totally guaranteed.
    Constraints

    Default value: [] Type: Integer

    Required
    No
    Field name
    return_prompt
    Description
    Returns the full prompt that was sent to the model when True.
    Constraints

    Default value: FALSE Type: Categorical Allowed values: TRUE, FALSE

    Required
    No
    Field name
    response_format
    Description
    Configuration for forcing the model output to adhere to the specified format. The model can be forced into outputting JSON objects (with up to 5 levels of nesting) by setting `{ "type": "json_object" }`. A JSON schema can optionally be provided, to ensure a specific structure. See full documentation at: https://docs.cohere.com/docs/structured-outputs-json
    Constraints

    Default value: [] Type: FreeText

    Required
    No
    Field name
    safety_mode
    Description
    Controls the contents of the safety section of the system prompt. “CONTEXTUAL” Responds as instructed while rejecting harmful or illegal suggestions. “STRICT” Designed to avoid sensitive topics. Strict content guardrails provide an extra safe experience by prohibiting inappropriate responses or recommendations. "NONE" Omits the safety section of the system prompt.
    Constraints

    Default value: CONTEXTUAL Type: Categorical Allowed values: CONTEXTUAL, STRICT, NONE

    Required
    No

    Output

    Summary

    The output is a JSON object that has the generated text along with likelihoods of tokens, if requested. See example json.

    Output MIME type
    application/json
    Sample output data
    Expand all output descriptions
    Field name
    response_id
    Description
    Unique identifier for chat completion Default value: [] Type: FreeText
    Always returned
    No
    Data type
    Field name
    generation_id
    Description
    Unique identifier for chat completion, used with Feedback endpoint on Cohere’s platform Default value: [] Type: FreeText
    Always returned
    No
    Data type
    Field name
    text
    Description
    Model’s response to chat message input Default value: [] Type: FreeText
    Always returned
    No
    Data type
    Field name
    finish_reason
    Description
    complete – The completion reached the end of generation token\nerror_toxic – could not be completed due to our content filters. \nerror_limit – could not be completed because the model’s context limit was reached.\nerror – could not be completed due to an error.\nuser_cancel - could not be completed because it was aborted by the user.\nmax_tokens – could not be completed because the user specified a max_tokens limit in the request and this limit was reached. May not result in best performance. Default value: [] Type: FreeText
    Always returned
    No
    Data type
    Field name
    token_count
    Description
    Count of tokens used Default value: [] Type: Integer
    Always returned
    No
    Data type
    Field name
    meta
    Description
    API usage data, including current version and billable tokens. Default value: [] Type: FreeText
    Always returned
    No
    Data type

    Sample notebook

    Additional Resources

    End User License Agreement

    By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)

    Support Information

    Command R 08-2024 (H100)

    AWS Infrastructure

    AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.

    Learn More

    Refund Policy

    No refunds. Please contact support+aws@cohere.com for further assistance.

    Customer Reviews

    There are currently no reviews for this product.
    View all