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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 082024 Finetuning Free trial

By:
Latest Version:
v1.0.0
Fine-tunable Cohere Command R 082024 with 16k context and multi-LoRA, optimized for long-context tasks and large-scale production.

    Product Overview

    Fine-tunable Cohere Command R 082024 with 16k context and multi-LoRA, optimized for long-context tasks and large-scale production.

    Key Data

    Type
    Algorithm
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Cohere Command R 082024 finetuning 16k context length support for training 128k context length support for inference

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    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$12.89/hr

    running on ml.p4de.24xlarge

    Model Realtime Inference$12.89/hr

    running on ml.p4de.24xlarge

    Model Batch Transform$12.89/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
    $12.89
    ml.p5.48xlarge
    $16.93

    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] .

    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

    lora_rank

    The rank of the LORA tensor
    Type: Integer
    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

    You can read about the Hyperparameters to tune here

    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

    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

    Cohere Command R 082024 Finetuning

    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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