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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.

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Cohere Command R Fine-tuning Free trial

By:
Latest Version:
v1.0.0
A fine-tunable version of Command R. Command R is a generative model optimized for long-context tasks and large scale production workloads.

    Product Overview

    Command R is a highly performant generative large language model, optimized for a variety of use cases including reasoning, summarization, and question answering. Command R has the capability for multilingual generation evaluated in 10 languages and 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. This instance is fine-tunable, allowing for customization on advanced use cases by leveraging your data. To access Cohere's Command R Finetuning model, please refer to the Sagemaker listing as Jumpstart is currently not supporting finetuning capabilities. Batch transform is not supported with this model.

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Command R with fine-tuning allows you to customize your models to be performant for your business, domain, and industry. Alongside the fine-tuned model, users additionally benefit from Command R’s proficiency in the most commonly used business languages (10 languages) and retrieval-augmented generation (RAG) with citations for accurate and verified information.

    • Command R with fine-tuning achieves high levels of performance with less resource usage on targeted use cases. Enterprises will see lower operational costs, improved latency and increased throughput without extensive computational demands.

    • It excels at tasks such as: document summarization, content Q&A, long-form generation, and content generation amongst others. With fine-tuning it can power industry and business specific 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

    Algorithm Training$32.89/hr

    running on ml.p4de.24xlarge

    Model Realtime Inference$32.89/hr

    running on ml.p4de.24xlarge

    Model Batch Transform$32.89/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 Algorithm Training$47.1106/host/hr

    running on ml.p4de.24xlarge

    SageMaker Realtime Inference$47.111/host/hr

    running on ml.p4de.24xlarge

    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.

    Algorithm Training

    For algorithm training 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
    Algorithm/hr
    ml.p4de.24xlarge
    Vendor Recommended
    $32.89

    Usage Information

    Training

    To train a custom model, please see the example below for parameters to pass to co.finetuning.create_finetuned_model(), or visit our [API guide] . You can read about the Hyperparameters to tune here

    Channel specification

    Fields marked with * are required

    training

    *
    Input modes: File
    Content types: -
    Compression types: None

    evaluation

    Input modes: File
    Content types: -
    Compression types: None

    Hyperparameters

    Fields marked with * are required

    name

    *
    Name of the custom model
    Type: FreeText
    Tunable: No

    train_epochs

    Maximum number of training epochs to run for
    Type: Integer
    Tunable: No

    learning_rate

    The learning rate
    Type: Continuous
    Tunable: No

    train_batch_size

    Batch size to use used during training. Should be a multiple of 8
    Type: Integer
    Tunable: No

    early_stopping_enabled

    When enabled, stop traininig if the loss metric does not improve beyond 'early_stopping_threshold' for `early_stopping_patience` times of evaluations
    Type: Categorical
    Tunable: No

    early_stopping_patience

    Stop traininig if the loss metric does not improve beyond 'early_stopping_threshold' for this many times of evaluations
    Type: Integer
    Tunable: No

    early_stopping_threshold

    How much the loss must improve to prevent early stopping
    Type: Continuous
    Tunable: No

    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

    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

    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

    Cohere Command R Fine-tuning

    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.

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