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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Medical LLM - 10B Free trial
By:
Latest Version:
1.0
Enhance medical reasoning and performance in complex terminology and clinical analysis, optimized for RAG applications.
Product Overview
Building on the foundation of the 7B model, this 10B-parameter variant offers enhanced medical reasoning capabilities while maintaining reasonable computational requirements. It demonstrates superior performance in complex medical terminology processing, detailed clinical analysis, and nuanced healthcare documentation interpretation. The model excels in generating more comprehensive medical summaries and handling intricate clinical scenarios with greater context awareness. Its balanced architecture delivers improved accuracy in specialized medical tasks while keeping resource utilization manageable. Optimized for RAG applications, it effectively processes and synthesizes information from medical literature, clinical guidelines, and patient records. This model is ideal for healthcare institutions requiring deeper medical understanding and more sophisticated analysis capabilities while maintaining operational efficiency.
Key Data
Version
Type
Model Package
Highlights
Real-Time Inference
- Instance Type ml.g4dn.12xlarge
- Maximum Model Length: 32,000 tokens
Tokens per Second during real-time inference:
- Summarization: up to 16 tokens per second
- QA: up to 34 tokens per second
Batch Transform
- Instance Type ml.g4dn.12xlarge
- Maximum Model Length: 32,000 tokens
Tokens per Second during batch transform operations:
- Summarization: up to 24 tokens per second
- QA: up to 125 tokens per second
Accuracy
- Surpasses Med-PaLM-1 with 75.19% average on medical benchmarks
- Exceptional clinical analysis (88.19%), approaching GPT-4's performance
- Outperforms larger models in Medical Genetics (82% vs Med-PaLM-1's 75%)
- Maintains competitive accuracy with models 7x its size
- Perfect balance of performance and computational efficiency
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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$9.98/hr
running on ml.g4dn.12xlarge
Model Batch Transform$9.98/hr
running on ml.g4dn.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$4.89/host/hr
running on ml.g4dn.12xlarge
SageMaker Batch Transform$4.89/host/hr
running on ml.g4dn.12xlarge
About Free trial
Try this product for 15 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 Vendor Recommended | $9.98 |
Usage Information
Model input and output details
Input
Summary
To use the model, provide input in one of the following formats: Single Input, Multiple Inputs or JSON Lines (JSONL). For a complete sample for each of the accepted formats, see the documentation here
Input MIME type
application/json, application/jsonlinesSample input data
Output
Summary
The output is a JSON object or a set of JSON Lines objects that contain the generated text(s)
JSON Format { "response": [ "model response for input 1", "model response for input 2", ... ] } JSON Lines (JSONL) Format {"response": "model response for input 1"} {"response": "model response for input 2"}
The JSON Lines format consists of separate JSON objects, where each object represents a model response for the respective input.
Output MIME type
application/json, application/jsonlinesSample output data
Sample notebook
End User License Agreement
By subscribing to this product you agree to terms and conditions outlined in the product End user License Agreement (EULA)
Support Information
Medical LLM - 10B
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
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Learn MoreRefund Policy
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