Sold by: Writer
Deployed on AWS
Palmyra-Med, a powerful LLM designed for healthcare
Overview
Palmyra-Med-70b-32k, created by Writer, builds upon the foundation of Palmyra-Med-70b and offers an extended context length and meets the needs of the healthcare industry. The leading LLM on biomedical benchmarks, with an average score of 85.87%, outperforming GPT-4, claude Opus, Gemini and Med-PaLM-2 base model and a medically trained human test-taker.
Highlights
- **Palmyra-Med-70B-32k**, developed by **Writer**, is the leading LLM on biomedical benchmarks with an average score of 85.87%. It outperforms larger models like GPT-4, Gemini, and Med-PaLM-1 across 9 diverse biomedical datasets, showcasing state-of-the-art results in critical areas such as Clinical KG, Medical Genetics, and PubMedQA. This exceptional performance underscores its robust grasp of biomedical knowledge and its potential to significantly advance healthcare applications.
- With an extended context window of **32,768 tokens**, Palmyra-Med-70B-32k excels in processing lengthy medical documents and complex healthcare scenarios. This capability, combined with specialized training on high-quality biomedical data, enables superior performance in analyzing clinical notes, summarizing EHR data, and extracting key information from research articles. These features make it an invaluable tool for enhancing clinical decision-making, supporting medical research, and advancing healthcare informatics.
Details
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Latest version
Deployed on AWS
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Pricing
Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
Additional AWS infrastructure costs may apply. Use the AWS Pricing Calculator to estimate your infrastructure costs.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.g5.48xlarge Inference (Batch) Recommended | Model inference on the ml.g5.48xlarge instance type, batch mode | $57.08 |
ml.p4d.24xlarge Inference (Real-Time) Recommended | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $57.08 |
Dimensions summary
You pay by the hour for the compute instance that runs this healthcare-focused language model. Pricing is usage-based, so charges accrue only while an instance runs. Two options separate the workload type and hardware. The ml.g5.48xlarge instance handles batch inference, where you process grouped requests together. The ml.p4d.24xlarge instance handles real-time inference, where you get responses on demand. Each option pairs a specific instance size with a specific inference mode. You choose based on whether your workload needs immediate responses or scheduled bulk processing.
Top-of-mind questions for buyers
Charges accrue per instance-hour only while the instance runs. When you stop the instance, software charges stop. Underlying AWS infrastructure fees, such as storage tied to a stopped instance, may still apply separately. To halt all software charges, terminate the running instance.
The ml.g5.48xlarge batch option meters instance-hours while processing grouped requests together, which suits scheduled bulk jobs like summarizing many clinical documents. The ml.p4d.24xlarge real-time option meters instance-hours while serving on-demand responses, which suits interactive workloads needing immediate replies. Both bill only for running time.
Each rate pairs the model with one specific AWS instance type. The batch rate runs on an ml.g5.48xlarge instance. The real-time rate runs on an ml.p4d.24xlarge instance. You pay the listed hourly price per running instance of that type. Larger or additional instances multiply the hourly charge.
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Delivery details
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.
Deploy the model on Amazon SageMaker AI using the following options:
Version release notes
Initial release: Batch transform is not supported.
Additional details
Inputs
- Summary
The model accepts JSON requests with parameters that can be used to control the generated text. See examples and fields descriptions below.
- Input MIME type
- application/json
https://github.com/writer/writer-aws/blob/main/examples/sample_input_med.json
https://github.com/writer/writer-aws/blob/main/examples/sample_input_med.json
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
messages | Text input for the model to respond to. | Type: FreeText | Yes |
stream | If set to `true`, the system will return a stream of JSON events as the response. The stream concludes with a final event marked by an `event_type` of `"stream-end"`, which contains the full response. This streaming approach is particularly useful for user interfaces that display content incrementally as it's generated, allowing for a more dynamic and responsive experience. | Default value: FALSE
Type: Categorical
Allowed values: TRUE, FALSE | No |
temperature | To make the response more predictable and less random, choose a lower value for this setting. If you want to increase the variety and unpredictability in the output, you can do so by raising the value of the `p` parameter instead. | Default value: 0.0
Type: Continuous
Minimum: 0
Maximum: 2 | No |
max_tokens | The maximum number of tokens the model will generate as part of the response. Note: Setting a low value may result in incomplete generations. | Default value: 1024
Type: Integer
Minimum: 0
Maximum: 32768 | No |
Top P (top_p) | Use a lower value to ignore less probable options. | Default value: 0.99
Type: Continuous
Minimum: 0.01
Maximum: 0.99 | No |
presence_penalty | Used to reduce repetitiveness of generated tokens. Similar to `frequency_penalty`, except that this penalty is applied equally to all tokens that have already appeared, regardless of their exact frequencies. | Default value: 0
Type: Continuous
Minimum: -2.0
Maximum: 2.0 | No |
frequency_penalty | Reduces repetition in the output. Higher values apply a stronger penalty to tokens that have already appeared, based on their frequency in the prompt or previous generation. | Default value: 0.0
Type: Continuous
Minimum: -2.0
Maximum: 2.0 | No |
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Support
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Email support services are available from Monday to Friday. support@writer.com
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