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.
Syn - 10B Enterprise-Grade Japanese LLM
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Latest Version:
250325.1
Enterprise-grade 10B Japanese LLM, powered by AWS Trainium, delivering high accuracy, cost-efficient deployment, and seamless fine-tuning.
Product Overview
Syn is a high-performance, lightweight 10B-class Japanese LLM co-developed by Upstage and Karakuri for enterprise AI applications. Combining Upstage’s global AI expertise with Karakuri’s advanced Japanese NLP, Syn offers enterprise-grade quality, reliability, and adaptability. Powered by AWS Trainium, Syn ensures exceptional accuracy, seamless fine-tuning, and cost efficiency, making it a future-ready AI solution. Designed for industry- and domain-specific tasks, it excels in Finance, Law, Healthcare, and other key verticals while optimizing structured text processing. With its lightweight architecture, Syn reduces infrastructure costs while maintaining top-tier performance, enabling businesses to deploy AI efficiently and scale with ease.
Key Data
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Highlights
Enterprise-Optimized Japanese AI: Developed specifically for Japanese enterprises, ensuring high precision in business applications.
Domain-specific Adaptation & Fine-Tunability: Highly adaptable for critical industries such as Finance, Law, Healthcare, and Manufacturing, allowing seamless fine-tuning for specific business needs.
Cost-Effective AI Deployment: Optimized with AWS Trainium, Syn maximizes performance while minimizing operational costs, ensuring efficient and scalable AI adoption.
Advanced Multilingual Capabilities: Built on Upstage’s Solar Mini foundation, offering seamless integration with key languages, including English, Korean, and Japanese.
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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$0.80/hr
running on ml.g5.12xlarge
Model Batch Transform$0.80/hr
running on ml.m5.12xlarge
Infrastructure PricingWith 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
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$7.09/host/hr
running on ml.g5.12xlarge
SageMaker Batch Transform$2.765/host/hr
running on ml.m5.12xlarge
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.g6.12xlarge | $0.80 | |
ml.g5.12xlarge Vendor Recommended | $0.80 |
Usage Information
Model input and output details
Input
Summary
We support the request payload compatible with OpenAI's Chat completion endpoint.
Limitations for input type
Syn(solar-japanese) supports a maximum context length of 32k (32,768) for input and generated tokens.
Input MIME type
application/jsonSample input data
{
"messages": [
{
"role": "system",
"content": "You are a helpful assistant."
},
{
"role": "user",
"content": "Can you provide a Python script to merge two sorted lists?"
}
],
"temperature": 0.7,
}Output
Summary
We support the response payload compatible with OpenAI's Chat completion endpoint.
Output MIME type
application/jsonSample output data
{
"id": "chat-ac562a62f94f468ab133c108cb9d5037",
"object": "chat.completion",
"created": 1742715252,
"model": "/opt/ml/model",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "I am a language model, so I don't have feelings or emotions. However, I am here to assist you with any questions or tasks you may have. How can I help you today?"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 59,
"total_tokens": 100,
"completion_tokens": 41
}
}Sample notebook
Additional Resources
End User License Agreement
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Support Information
Syn - 10B Enterprise-Grade Japanese LLM
Contact us for model fine-tuning and enterprise integration inquiries. https://www.upstage.ai/contact-us?utm_source=marketplace
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