Sold by: NC
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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Pricing
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Dimensions summary
This listing is a Korean-and-English language model offered at no software charge. Pricing is organized by how you run the model, not by tiers. You pick one of two deployment modes, each tied to a specific instance type. The batch mode runs on the ml.g4dn.12xlarge instance and processes grouped requests. The real-time mode runs on the ml.g5.12xlarge instance and serves live requests. Both bill by host-hour, so your cost scales with how long each instance runs. You choose the mode and instance that match your workload.
Top-of-mind questions for buyers
Batch mode runs on the ml.g4dn.12xlarge instance type. Real-time mode runs on the ml.g5.12xlarge instance type. Billing is per host-hour, meaning each hour an instance stays running counts as one unit. Costs accrue for every hour the instance is active, regardless of request volume.
Batch mode processes grouped requests together on the ml.g4dn.12xlarge instance. Real-time mode serves live, individual requests on the ml.g5.12xlarge instance. Both bill by host-hour. Batch fits scheduled or bulk processing. Real-time fits interactive applications that need immediate responses. You pick the mode matching your use case.
The model software itself carries no charge. Your cost comes from the running instance measured in host-hours. You pay for how long the ml.g4dn.12xlarge or ml.g5.12xlarge instance runs. The underlying AWS infrastructure for that instance is billed separately through AWS.
huggingface.co
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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:
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 |
Resources
Vendor resources
Support
Vendor support
NC R&D Center, 12, Daewangpangyo-ro 644beon-gil, Bundang-gu, Seongnam-si, Gyeonggi-do, Republic of Korea Tel :02-6201-0099/Email :nc-ai@ncsoft.com
AWS infrastructure support
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