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    VARCO LLM 2.0 small Finetuning

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    Sold by: NCSOFT 
    Deployed on AWS
    VARCO LLM 2.0 is NCSOFT's large language model that can be applied to the development of natural language processing-based AI services.

    Overview

    VARCO LLM 2.0 is NCSOFT's large language model that can be applied to the development of various natural language processing-based AI services such as text generation, question answering, chatbots, summarization, and information extraction. NCSOFT's VARCO LLM 2.0 was developed with our own technology, including data construction, pre-training, instruction tuning and alignment tuning. We evaluated VARCO LLM 2.0 on various NLP tasks and its performance has significantly improved compared to VARCO LLM 1.0, and it boasts the highest performance among other Korean LLMs of similar sizes. In particular, it has been trained to be used in high-level natural language processing applications such as creative writing, summarization, question and answering, chatbots and translation, and shows high performance in related quantitative indicators. For inquiries regarding further performance improvement or collaboration for service applications, please contact us by email (varco_llm@ncsoft.com ).

    Highlights

    • Korean Text Generation : VARCO LLM 2.0 is optimized for Korean natural language generation applications. In particular, it provides more natural and creative responses in understanding user instructions and generating text.
    • Fine-tuning your own language model : You can fine-tune VARCO LLM with your own data on the AWS platform. Create your own language model with VARCO LLM.
    • Key Skills * Question Answering * Summarization * Translation * Text Generation * Chatbots * Information Extraction * Natural Language Understanding * Creative Writing * Instruction Following * Sentiment Analysis

    Details

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    Latest version

    Deployed on AWS

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    Pricing

    VARCO LLM 2.0 small Finetuning

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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 (3)

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    Dimension
    Description
    Cost/host/hour
    ml.g4dn.4xlarge Inference (Batch)
    Recommended
    Model inference on the ml.g4dn.4xlarge instance type, batch mode
    $0.00
    ml.g5.4xlarge Inference (Real-Time)
    Recommended
    Model inference on the ml.g5.4xlarge instance type, real-time mode
    $0.70
    ml.g5.4xlarge Training
    Recommended
    Algorithm training on the ml.g5.4xlarge instance type
    $0.70

    Vendor refund policy

    Please contact us : varco_llm@ncsoft.com 

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    Usage information

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    Delivery details

    Amazon SageMaker algorithm

    An Amazon SageMaker algorithm is a machine learning model that requires your training data to make predictions. Use the included training algorithm to generate your unique model artifact. Then deploy the 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:
    Before deploying the model, train it with your data using the algorithm training process. You're billed for software and SageMaker infrastructure costs only during training. Duration depends on the algorithm, instance type, and training data size. When training completes, the model artifacts save to your Amazon S3 bucket. These artifacts load into the model when you deploy for real-time inference or batch processing. For more information, see Use an Algorithm to Run a Training Job  .
    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

    Major Version release of VARCO LLM 2.0 small Finetuning from NCSOFT

    Additional details

    Inputs

    Summary

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

    Input MIME type
    application/json
    { "repetition_penalty": 1.0, "temperature": 0.7, "top_k": 50, "top_p": 1.0, "text": "input text here" }
    https://github.com/ncsoft/ncresearch/blob/feature/VARCO_LLM_2_0/notebooks/train_data_example.json

    Input data descriptions

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

    Field name
    Description
    Constraints
    Required
    repetition_penalty
    The parameter for repetition penalty. 1.0 means no penalty.
    Default value: 1.0 Type: Continuous
    No
    temperature
    The value used to modulate the next token probabilities.
    Default value: 0.7 Type: Continuous
    No
    top_k
    The number of highest probability vocabulary tokens to keep for top-k-filtering.
    Default value: 50 Type: Integer
    No
    top_p
    If set to float < 1, only the smallest set of most probable tokens with probabilities that add up to top_p or higher are kept for generation.
    Default value: 1.0 Type: Continuous
    No
    text
    The sequence used as a prompt for the generation or as model inputs to the encoder.
    Type: FreeText
    Yes

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