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.
Product Overview
Solar Mini Chat is a compact yet powerful advanced English/Korean large language model. It is specifically fine-tuned for multi-turn chat purposes, demonstrating enhanced performance across a wide range of natural language processing tasks. This fine-tuning equips it with the ability to handle extended conversations more effectively, making it particularly adept for interactive applications. It employs a scaling method called ‘depth up-scaling’ (DUS), which is comprised of depthwise scaling and continued pretraining. DUS allows for a much more straightforward and efficient enlargement of smaller models than other scaling methods such as mixture-of-experts.
Key Data
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Model Package
Highlights
Key Features
- High Performance: Solar Mini Chat is specially optimized for chat applications in Korean. It surpasses many open-source models, especially in handling the intricacies of Korean language interactions, offering more natural and accurate responses.
- Enhanced User Experience: Solar Mini Chat advanced grasp of Korean language nuances significantly elevates user interactions in chat environments. It provides precise responses to user inputs, ensuring clearer communication and more efficient problem resolution in Korean chat applications.
Key Applications
The capabilities of Solar Mini Chat are particularly valuable in scenarios requiring a nuanced understanding and responsive execution of complex conversational exchanges, particularly in the Korean language. With its advanced ability to interpret and engage in detailed dialogues, Solar Mini Chat is an exceptional choice for applications demanding interactive, context-aware communication, especially tailored to the subtleties and intricacies of the Korean language.
Key Tasks
- Instruction Following
- Natural Language Understanding
- Natural Language Generation
- Question Answering
- Text Generation and Editing
- Translation Services
- Summarization
- Information Extraction
- Chatbots and Conversational Interfaces
- Educational and Learning Assistance
- Data Analysis and Report Writing
- Code Generation and Debugging
- Content Recommendation Systems
- Sentiment Analysis and Customer Feedback Processing
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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$0.00/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
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.g4dn.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
Solar 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": "cmpl-cc3f71b58086441fa6a53480b94fd0ec",
"object": "chat.completion",
"created": 3421,
"model": "solar-mini",
"choices": [
{
"index": 0,
"message": {
"role": "assistant",
"content": "What do you call a fake noodle?\n\nAn impasta!"
},
"finish_reason": "stop"
}
],
"usage": {
"prompt_tokens": 34,
"total_tokens": 334,
"completion_tokens": 300
}
}
Sample notebook
Additional Resources
End User License Agreement
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Support Information
Solar Mini Chat
Contact us for model fine-tuning request.
AWS Infrastructure
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Learn MoreRefund Policy
We do not support any refunds currently.
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