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    Llama-VARCO-8B-Instruct

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    Sold by: NCSOFT 
    Deployed on AWS
    Llama-VARCO-8B-Instruct is a generative model built with Llama, specifically designed to excel in Korean through additional training

    Overview

    Llama-VARCO-8B-Instruct is a generative model built with Llama, specifically designed to excel in Korean through additional training. The model uses continual pre-training with both Korean and English datasets to enhance its understanding and generation capabilites in Korean, while also maintaining its proficiency in English. It performs supervised fine-tuning (SFT) and direct preference optimization (DPO) in Korean to align with human preferences.

    Highlights

    • Developed by: NC Research, Language Model Team
    • Languages (NLP): Korean, English
    • License: LLAMA 3.1 COMMUNITY LICENSE AGREEMENT Base model: meta-llama/Meta-Llama-3.1-8B

    Details

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    Deployed on AWS

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    Llama-VARCO-8B-Instruct

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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

    Init release

    Additional details

    Inputs

    Summary

    Model accepts JSON requests. You can check examples and fields descriptions.

    Input MIME type
    application/json
    { "messages": [ { "role":"user", "content":"안녕 넌 누구야?" } ] }
    https://huggingface.co/NCSOFT/Llama-VARCO-8B-Instruct

    Input data descriptions

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

    Field name
    Description
    Constraints
    Required
    messages
    Input messages, GPT style input.
    Type: FreeText
    Yes

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