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    Liquid LFM 7B (L40S)

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    Sold by: Liquid AI 
    Deployed on AWS
    Free Trial
    Liquid LFM 7B is designed to handle complex tasks, offering an optimal balance between size and output quality.

    Overview

    LFM-7B is specifically optimized for response quality, accuracy, and usefulness. To assess its chat capabilities, we leverage a diverse frontier LLM jury to compare responses generated by LFM-7B against other models in the 7B-8B parameter category. It allows us to reduce individual biases and produce more reliable comparisons.

    Highlights

    • **Innovative Model Architecture**: Liquid AI's Foundation Models utilize a unique architecture that combines liquid neural networks and non-transformer designs, allowing these models to be efficient in memory usage and capable of handling sequential data, such as text, video, and real-time signals. This setup optimizes performance while minimizing computational demands.
    • **Enhanced Adaptability and Real-Time Learning**: Unlike conventional models, LFMs can adapt their internal processes based on new inputs in real time, making them highly responsive.
    • **Efficiency in Long-Context Processing**: Liquid AI's models can efficiently process extended input sequences without the steep memory and processing requirements typical of transformer-based models, supporting applications like document summarization and complex chatbot interactions with minimal hardware demands. With LFMs, it’s possible to fit up to 1 million tokens-worth of data and map it onto 16 gigabytes of memory. **

    Details

    Delivery method

    Latest version

    Deployed on AWS

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    Financing for AWS Marketplace purchases

    Pricing

    Free trial

    Try this product free for 7 days according to the free trial terms set by the vendor.

    Liquid LFM 7B (L40S)

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    Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
    Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator  to estimate your infrastructure costs.

    Usage costs (6)

     Info
    Dimension
    Description
    Cost/host/hour
    ml.g4dn.12xlarge Inference (Batch)
    Recommended
    Model inference on the ml.g4dn.12xlarge instance type, batch mode
    $10.00
    ml.g6e.2xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.g6e.2xlarge instance type, real-time mode
    $3.00
    ml.g6e.xlarge Inference (Real-Time)
    Model inference on the ml.g6e.xlarge instance type, real-time mode
    $3.00
    ml.g6e.4xlarge Inference (Real-Time)
    Model inference on the ml.g6e.4xlarge instance type, real-time mode
    $3.00
    ml.g6e.16xlarge Inference (Real-Time)
    Model inference on the ml.g6e.16xlarge instance type, real-time mode
    $3.00
    ml.g6e.8xlarge Inference (Real-Time)
    Model inference on the ml.g6e.8xlarge instance type, real-time mode
    $3.00

    Vendor refund policy

    We don’t offer refunds, but we’re happy to assist! Contact us anytime at  support+aws@liquid.ai .

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    Usage information

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    Delivery details

    Amazon SageMaker model

    An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.

    Deploy the model on Amazon SageMaker AI using the following options:
    Deploy the model as an API endpoint for your applications. When you send data to the endpoint, SageMaker processes it and returns results by API response. The endpoint runs continuously until you delete it. You're billed for software and SageMaker infrastructure costs while the endpoint runs. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Deploy models for real-time inference  .
    Deploy the model to process batches of data stored in Amazon Simple Storage Service (Amazon S3). SageMaker runs the job, processes your data, and returns results to Amazon S3. When complete, SageMaker stops the model. You're billed for software and SageMaker infrastructure costs only during the batch job. Duration depends on your model, instance type, and dataset size. AWS Marketplace models don't support Amazon SageMaker Asynchronous Inference. For more information, see Batch transform for inference with Amazon SageMaker AI  .
    Version release notes

    Initial version

    Additional details

    Inputs

    Summary

    The model leverages OpenAI's chat format as detailed in OpenAI API documentation , with the following key specifics:

    • Supports text-only interactions.
    • Mandates the model parameter to be explicitly set to /opt/ml/model for proper functionality.
    Input MIME type
    application/json
    https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json
    https://github.com/Liquid4All/aws-samples/blob/main/notebooks/sagemaker/data/input/real-time/simple.json

    Input data descriptions

    The following table describes supported input data fields for real-time inference and batch transform.

    Field name
    Description
    Constraints
    Required
    Request
    https://platform.openai.com/docs/api-reference/chat
    Type: FreeText
    Yes

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    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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