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 - Small Free trial
By:
Latest Version:
5.4.3
Use for tasks like medical summarization or open-book question answering with context of up to 32K tokens.
Product Overview
Trained on diverse medical texts, this model excels in summarizing, answering complex clinical questions, and transforming detailed clinical notes, patient encounters, and various medical reports into concise, digestible summaries. The summarization feature boosts efficiency while preserving critical details, supporting optimal patient care. Its question-answering capability ensures accurate, context-specific responses to both open and closed medical queries, further enhancing decision-making. For physicians, this tool offers a quick grasp of a patient’s medical history, aiding timely and informed decisions. Instead of sifting through extensive documentation, doctors can rely on these summaries to understand a patient’s journey, condition, and treatment protocols swiftly. Optimized for Retrieval-Augmented Generation (RAG), the model can be used in combination with healthcare databases, EHR, and scientific literature repositories (like PubMed) to enhance response quality.
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 36 tokens per second
- QA: up to 46 tokens per second
Batch Transform
- Instance Type: ml.g4dn.12xlarge
- Maximum Model Length: 32,000 tokens
- Tokens per Second: Summarization: up to 100 tokens during batch transform operations. QA: up to 226 tokens per second
Benchmarking Results:
In a randomized blind evaluation by medical experts, this model outperformed GPT-4o on:- Text Summarization: Preferred 88% more often on factuality, 92% on relevance, 68% on conciseness.
- Clinical Notes Question Answering: Preferred 46% more on factuality, 50% on relevance, 44% on conciseness.
- Biomedical Research Question Answering: Preferred 175% more on factuality, 200% on relevance, 256% on conciseness.
- General Knowledge Question Answering: Comparable performance to GPT-4o.
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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 - Small
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
AWS Support is a one-on-one, fast-response support channel that is staffed 24x7x365 with experienced and technical support engineers. The service helps customers of all sizes and technical abilities to successfully utilize the products and features provided by Amazon Web Services.
Learn MoreRefund Policy
No refunds are possible.
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