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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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Writer Palmyra-Fin-70B-32K

By:
Writer
Latest Version:
v1.0.0
Palmyra-Fin, a powerful LLM designed for Finance
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    Product Overview

    Palmyra-Fin-70B-32K is a model built by Writer specifically to meet the needs of the financial industry. It is a leading LLM on financial benchmarks, outperforming other large language models in various financial tasks and evaluations.

    Key Data

    Version
    v1.0.0
    Usage Instructions
    Release Notes
    By
    Writer
    Categories
    Generation-Text
    Natural Language Processing
    Summarization-Text
    Type
    Model Package
    Fulfillment Methods
    Amazon SageMaker

    Highlights

    • Palmyra-Fin-70B-32K is a state-of-the-art model for financial applications, achieving 100% accuracy on needle-in-haystack tasks across its entire 32,768 token context window. It's the first model to pass the CFA Level III test with a 73% score, surpassing the average passing score of 60%. On the long-fin-eval benchmark, it outperforms both open-source and proprietary models, demonstrating superior capabilities in processing and synthesizing information from lengthy financial documents.

    • With its extended 32,768 token context window, Palmyra-Fin-70B-32K excels at analyzing and summarizing complex financial reports, market data, and economic indicators. It enhances financial decision-making through advanced entity recognition and deep understanding of financial terminology. The model's long-context capabilities make it ideal for in-depth financial research, investment analysis, and knowledge discovery from extensive financial sources.

    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$57.08/hr

    running on ml.p4d.24xlarge

    Model Batch Transform$57.08/hr

    running on ml.g5.48xlarge

    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$37.688/host/hr

    running on ml.p4d.24xlarge

    SageMaker Batch Transform$20.36/host/hr

    running on ml.g5.48xlarge

    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.p4d.24xlarge
    Vendor Recommended
    $57.08

    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
    messages
    Description
    Text input for the model to respond to.
    Required
    Yes
    Data type
    FreeText
    Field name
    stream
    Description
    If set to `true`, the system will return a stream of JSON events as the response. The stream concludes with a final event marked by an `event_type` of `"stream-end"`, which contains the full response. This streaming approach is particularly useful for user interfaces that display content incrementally as it's generated, allowing for a more dynamic and responsive experience.
    Required
    No
    Data type
    Categorical
    Default value
    FALSE
    Allowed values
    TRUE, FALSE
    Field name
    temperature
    Description
    To make the response more predictable and less random, choose a lower value for this setting. If you want to increase the variety and unpredictability in the output, you can do so by further raising the value of the `top_p` parameter instead.
    Required
    No
    Data type
    Continuous (Float)
    Range
    Min: 0
    Max: 2
    Default value
    0.0
    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.
    Required
    No
    Data type
    Integer
    Range
    Min: 0
    Max: 32768
    Default value
    1024
    Field name
    Top P (top_p)
    Description
    Use a lower value to ignore less probable options.
    Required
    No
    Data type
    Continuous (Float)
    Range
    Min: 0.01
    Max: 0.99
    Default value
    0.99
    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.
    Required
    No
    Data type
    Continuous (Float)
    Range
    Min: -2.0
    Max: 2.0
    Default value
    0.0
    Field name
    frequency_penalty
    Description
    Reduces repetition in the output. Higher values apply a stronger penalty to tokens that have already appeared, based on their frequency in the prompt or previous generation
    Required
    No
    Data type
    Continuous (Float)
    Range
    Min: -2.0
    Max: 2.0
    Default value
    0.0

    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
    id
    Description
    response id: Unique identifier for chat completion
    Always returned
    No
    Data type
    FreeText
    Default value
    []
    Field name
    object
    Description
    chat.completion or chat.completion.chunk
    Always returned
    No
    Data type
    FreeText
    Default value
    []
    Field name
    created
    Description
    The timestamp of when the chat completion was created.
    Always returned
    No
    Data type
    Integer
    Range
    Default value
    []
    Field name
    choices
    Description
    The Choice object contains four fields: "index" (integer): Represents the index of the choice. "message" (Message object): Contains a chat completion message generated by the model. "logprobs" (Logprobs object): Represents the log probability of the token. "finish_reason" (string enum): Indicates why the model stopped generating tokens. Possible values are "length", "eos_token", or "stop_sequence".
    Always returned
    No
    Data type
    FreeText
    Default value
    []
    Field name
    usage
    Description
    The Usage object contains three fields, all of integer type: "completion_tokens": Represents the number of tokens in the generated completion. Example value: 33. "prompt_tokens": Indicates the number of tokens in the prompt. Example value: 100. "total_tokens": Shows the total number of tokens used in the request, which is the sum of prompt and completion tokens. Example value: 133.
    Always returned
    No
    Data type
    FreeText
    Default value
    []

    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

    Writer Palmyra-Fin-70B-32K

    Email support services are available from Monday to Friday. support@writer.com

    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.

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    Refund Policy

    All fees are non-refundable and non-cancellable except as required by law.

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