Overview
This specialized 24B parameter model provides comprehensive medical language capabilities optimized for Spanish-speaking healthcare environments.
The model processes Spanish medical terminology, clinical documentation, and patient communications with high precision, bridging the language gap in medical AI. With a 32K context window, it handles extensive Spanish medical texts, including clinical notes, research papers, and patient records.
The model summarizes complex Spanish clinical information, responds to medical queries in natural Spanish, and transforms detailed clinical documentation into concise summaries while maintaining critical details. It recognizes region-specific medical terminology variations across Spanish-speaking countries and adapts to local healthcare contexts. Healthcare providers can efficiently analyze Spanish patient histories and medical literature without translation, maintaining the nuance and precision of native medical language.
Optimized for integration with Spanish-language medical databases and EHR systems, it enhances RAG applications in Spanish-language healthcare settings.
IMPORTANT USAGE INFORMATION:
After subscribing to this product and creating a SageMaker endpoint, billing occurs on an HOURLY BASIS for as long as the endpoint is running.
-Charges apply even if the endpoint is idle and not actively processing requests.
-To stop charges, you MUST DELETE the endpoint in your SageMaker console.
-Simply stopping requests will NOT stop billing.
This ensures you are only billed for the time you actively use the service.
Highlights
- Performance Metrics: - Achieves 84.54% accuracy on Spanish medical benchmarks - Outperforms general Medical VLM - 24B by +3.28% on Spanish content - Approaches Medical LLM - Medium performance (85.13%) at fraction of parameters - Exceeds Medical Reasoning 32B (83.32%) despite smaller size
- Technical Specifications: - Maximum Model Length: 32K tokens - Optimized for Spanish medical terminology and documentation - Supports regional Spanish medical vocabulary variations - Maintains clinical accuracy in translation-free Spanish medical text processing - Processes Spanish medical literature with native-language understanding - Handles dialect variations across Latin American and European Spanish medical contexts
- **Performance metrics for Real Time QA:** Instance Type: ml.g5.48xlarge * Text Completion: up to 540 tokens per second * Chat Completion: up to 790 tokens per second Instance Type: ml.p5.48xlarge * Text completion: up to 2500 tokens per second * Chat completion: up to 3300 tokens per second
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Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $19.96 |
ml.g5.48xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.48xlarge instance type, real-time mode | $19.96 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $19.96 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $19.96 |
ml.g6e.48xlarge Inference (Real-Time) | Model inference on the ml.g6e.48xlarge instance type, real-time mode | $19.96 |
ml.p5.48xlarge Inference (Real-Time) | Model inference on the ml.p5.48xlarge instance type, real-time mode | $19.96 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $19.96 |
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Amazon SageMaker model
An Amazon SageMaker model package is a pre-trained machine learning model ready to use without additional training. Use the model package to create a model on Amazon SageMaker for real-time inference or batch processing. Amazon SageMaker is a fully managed platform for building, training, and deploying machine learning models at scale.
Version release notes
This specialized 24B parameter model provides comprehensive medical language capabilities optimized for Spanish-speaking healthcare environments. The model processes Spanish medical terminology, clinical documentation, and patient communications with high precision, bridging the language gap in medical AI.
Additional details
Inputs
- Summary
Input Format
1. Chat Completion
Example Payload
{
"model": "/opt/ml/model",
"messages": [
{"role": "system", "content": "Eres un asistente médico útil."},
{"role": "user", "content": "¿Qué debo hacer si tengo fiebre y dolores corporales?"}
],
"max_tokens": 1024,
"temperature": 0.7
}For additional parameters:
ChatCompletionRequest OpenAI's Chat APIÂ
2. Text Completion
Single Prompt Example
{
"model": "/opt/ml/model",
"prompt": "¿Qué debo hacer si tengo fiebre y dolores corporales?",
"max_tokens": 512,
"temperature": 0.6
}Multiple Prompts Example
{
"model": "/opt/ml/model",
"prompt": [
"ÂżCĂłmo puedo mantener una buena salud renal?",
"ÂżCuáles son los sĂntomas de la hipertensiĂłn?"
],
"max_tokens": 512,
"temperature": 0.6
}Reference
CompletionRequest OpenAI's Completions APIÂ
Important Notes:
- Streaming Responses: Add "stream": true to your request payload to enable streaming
- Model Path Requirement: Always set "model": "/opt/ml/model" (SageMaker's fixed model location)
- Input MIME type
- application/json
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For any assistance, please reach out to support@johnsnowlabs.com .
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