Sold by: Arcee
Deployed on AWS
Arcee-SuperNova-Medius is a 14B parameter language model developed by Arcee.ai, built on the Qwen2.5-14B-Instruct architecture.
Overview
This unique model is the result of a cross-architecture distillation pipeline, combining knowledge from both the Qwen2.5-72B-Instruct model and the Llama-3.1-405B-Instruct model. By leveraging the strengths of these two distinct architectures, SuperNova-Medius achieves high-quality instruction-following and complex reasoning capabilities in a mid-sized, resource-efficient form.
SuperNova-Medius performs exceptionally well in instruction-following (IFEval) and complex reasoning tasks (BBH), demonstrating its capability to handle a variety of real-world scenarios. It outperforms Qwen2.5-14B and SuperNova-Lite in multiple benchmarks, making it a powerful yet efficient choice for high-quality generative AI applications.
Highlights
- Customer Support: With its robust instruction-following and dialogue management capabilities, SuperNova-Medius can handle complex customer interactions, reducing the need for human intervention.
- Content Creation: The model’s advanced language understanding and generation abilities make it ideal for creating high-quality, coherent content across diverse domains.
- Technical Assistance: SuperNova-Medius has a deep reservoir of technical knowledge, making it an excellent assistant for programming, technical documentation, and other expert-level content creation.
Details
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Delivery method
Latest version
Deployed on AWS
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Pricing
This product is available free of charge. Free 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.
Dimensions summary
This listing is free to license, so you pay only for the AWS compute you run. Pricing is organized by AWS instance type and inference mode. Seven dimensions cover real-time inference across a range of GPU instance families, letting you match hardware to your throughput needs. Two dimensions cover batch inference on separate instance types for offline processing of larger jobs. Each dimension bills per host hour, so cost scales with how many hours you run each instance. You choose the instance size and mode that fit your workload.
Top-of-mind questions for buyers
One host hour is one hour that a single model inference instance runs. Each instance type you launch bills separately per running hour. Cost scales with how long the instance stays active and how many instances you run at once.
Real-time inference keeps an instance running to serve live requests, so you pay per host hour while it stays up. Batch inference processes queued jobs on separate instance types, billing host hours only during the job run. Real-time suits interactive use; batch suits large offline jobs.
The model license is free, so no software fee applies. You pay only the per-host-hour charges for the AWS instance type you run. This 14-billion-parameter model is available under the Apache-2.0 license, letting you run inference on the hardware you select.
huggingface.co
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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
This version is configured for 4-GPU and 8-GPU instances in the g5, g6, p4, and p5 families. Context size is 32 KB and the OpenAI Messages API is enabled.
Additional details
Inputs
- Summary
You can invoke the model using the OpenAI Messages AI. Please see the sample notebook for details.
- Input MIME type
- application/json, application/jsonlines
{
"messages": [
{
"role": "system",
"content": "As a friendly technical assistant engineer, answer the question in detail.",
},
{"role": "user", "content": "Why are transformers better models than LSTM?"},
],
"max_tokens": 256
}
https://github.com/arcee-ai/aws-samples/blob/main/model_package_notebooks/sample-notebook-supernova-medius-on-sagemaker.ipynb
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
OpenAI Messages API | Please see sample notebook. | Type: FreeText | Yes |
Resources
Vendor resources
Support
Vendor support
IMPORTANT INFORMATION: Once you have subscribed to the model, we strongly recommend that you deploy it with our sample notebook at https://github.com/arcee-ai/aws-samples/blob/main/model_package_notebooks/sample-notebook-supernova-medius-on-sagemaker.ipynb . This is the best way to guarantee proper configuration.
Contact: julien@arcee.ai
AWS infrastructure support
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.
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