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.

Medical LLM - 24B Free trial
By:
Latest Version:
1.0
Medical model offering top-tier depth and accuracy in processing complex medical cases and literature, ideal for specialized medical use
Product Overview
Matching the performance of industry leaders with quicker inference times and lower deployment costs, the model is ideal for healthcare institutions needing medical analysis without substantial computational resources. Trained on a wide array of medical texts, the model is highly adept at summarizing complex clinical information, answering medical questions, and summarizing detailed clinical notes and various medical reports. Its summarization capability enhances efficiency while retaining essential details. The model's ability to answer both open and closed medical queries with precise, context-specific responses further aids in clinical decision-making. It provides physicians with rapid insights into a patient's medical history, enabling timely and well-informed decisions. Optimized for Retrieval-Augmented Generation (RAG), it works effectively with healthcare databases, EHR systems, and scientific literature repositories to improve the quality of responses.
Key Data
Version
Type
Model Package
Highlights
Real-Time Inference
- Instance Type: ml.g5.48xlarge
- Maximum Model Length: 32K tokens
Tokens per Second during real-time inference:
- Summarization: up to 427 tokens per second
- QA: up to 1,292 tokens per second
Batch Transform
- Instance Type: ml.g5.48xlarge
- Maximum Model Length: 32K tokens
Tokens per Second during batch transform operations:
- Summarization: up to 50 tokens per second
- QA: up to 162 tokens per second
Accuracy:
- Achieves 82.83% OpenMed average, matching GPT-4 (82.85%) with fraction of parameters
- Outperforms Med-PaLM-2 in medical genetics (92% vs 90%) and college medicine (79.19% vs 80.9%)
- Matches GPT-4's clinical knowledge accuracy (86.04%)
- Surpasses Med-PaLM-1 across all benchmarks by +8.13% average
- Processes medical MCQAs with 70.27% accuracy, comparable to Med-PaLM-2 (71.3%)
- Maintains competitive performance with leading models while requiring significantly less compute
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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$19.96/hr
running on ml.g5.48xlarge
Model Batch Transform$19.96/hr
running on ml.g5.48xlarge
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$20.36/host/hr
running on ml.g5.48xlarge
SageMaker Batch Transform$20.36/host/hr
running on ml.g5.48xlarge
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.g6e.12xlarge | $19.96 | |
ml.g6e.24xlarge | $19.96 | |
ml.p4d.24xlarge | $19.96 | |
ml.g6e.48xlarge | $19.96 | |
ml.g5.48xlarge Vendor Recommended | $19.96 | |
ml.p5.48xlarge | $19.96 |
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 - 24B
For any assistance, please reach out to support@johnsnowlabs.com.
AWS Infrastructure
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