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 - 7B Free trial
By:
Latest Version:
1.0
Medical model exceling at clinical tasks with efficient deployment and cost-effectiveness, ideal for rapid, high-accuracy responses.
Product Overview
This compact yet powerful 7B-parameter medical language model delivers exceptional performance for common clinical tasks while maintaining efficiency in deployment and operation. Trained on comprehensive medical texts, it excels at clinical summarization, standard medical question-answering, and processing routine patient documentation. Its smaller size enables faster inference and reduced computational costs, making it ideal for organizations seeking to balance performance with resource optimization. Perfect for high-throughput environments requiring quick responses, this model maintains high accuracy in core medical tasks while consuming significantly less computing power than larger variants. Like its siblings, it's optimized for Retrieval-Augmented Generation (RAG), seamlessly integrating with healthcare databases and EHR systems. Choose this model when rapid response times and cost-effectiveness are priorities, without compromising on essential medical comprehension capabilities.
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 21 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 during batch transform operations:
- Summarization: up to 38 tokens per second
- QA: up to 135 tokens per second
Accuracy
- Outperforms Med-PaLM-1 in clinical reasoning (86.81% vs 83.8%)
- Achieves 71.70% average across OpenMed benchmarks, comparable to larger models
- Superior performance in PubMedQA (75.6%) vs similar-sized models
- Matches GPT-4's accuracy in medical QA tasks while being 100x smaller
- Ideal for cost-efficient clinical deployments with fast inference
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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 - 7B
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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