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.
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Solar Embedding Large Free trial
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Latest Version:
v1.0.0
Solar Embedding Large: A multilingual model optimized for retrieval tasks in English, Korean, Japanese, and more.
Product Overview
Solar Embedding Large is a powerful multilingual embedding model offering robust performance across multiple languages, including English, Korean, Japanese, and more. It's specifically fine-tuned for retrieval tasks, significantly enhancing multilingual retrieval results. This model is divided into two specialized versions: 'solar-embedding-1-large-query', optimized for embedding user's question, and 'solar-embedding-1-large-passage', designed for embedding documents to be searched. Utilizing these purpose-specific models increases retrieval efficiency, which leads to improved performance of Retrieval Augmented Generation (RAG) systems.
Key Data
Version
By
Type
Model Package
Highlights
Key Features
Enhanced RAG System Efficiency: The specialized models for query and passage embedding in Solar Embedding Large significantly improve the performance and precision of Retrieval Augmented Generation (RAG) systems.
Robust Multilingual Capabilities: This model demonstrates strong performance in multiple languages. It is exceptional choice for multilingual retrieval tasks, including English, Korean, Japanese, and more.
Key Applications
Solar Embedding Large excels in diverse tasks, particularly in Retrieval Augmented Generation (RAG), where precise information retrieval is crucial. Solar's query and passage models are optimized for these tasks. With robust language support including English, Korean, Japanese, and more, it is well-suited for a wide range of information retrieval applications requiring high accuracy across multiple languages.
Key Tasks
- Retrieval Augmented Generation
- Semantic Search
- Reranking
- Text Embeddings
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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.g5.2xlarge
Model Batch Transform$0.00/hr
running on ml.g5.2xlarge
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$1.515/host/hr
running on ml.g5.2xlarge
SageMaker Batch Transform$1.515/host/hr
running on ml.g5.2xlarge
About Free trial
Try this product for 7 days. There will be no software charges, but AWS infrastructure charges still apply. Free Trials will automatically convert to a paid subscription upon expiration.
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.g5.2xlarge Vendor Recommended | $0.80 |
Usage Information
Model input and output details
Input
Summary
We support the request payload compatible with OpenAI's Embeddings API endpoint.
Limitations for input type
Solar-embedding-1-large supports a maximum context length of 2000 for input tokens.If you require additional input tokens beyond the supported limit, please contact us.
Input MIME type
application/jsonSample input data
# Input is single string
input = {
"input": "How is the performance of Solar embeddings?",
"model": "solar-embedding-1-large-query"
}
# Input is a list of string
input = {
"input": [
"Solar embeddings are awesome.",
"Solar embedding large model demonstrates strong performance in multiple languages."
],
"model": "solar-embedding-1-large-passage"
}
Output
Summary
We support the response payload compatible with OpenAI's Embeddings API endpoint.
Output MIME type
application/jsonSample output data
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Solar Embedding Large
Contact us for model fine-tuning request.
AWS Infrastructure
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Learn MoreRefund Policy
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