Sold by: Arcee
Deployed on AWS
Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai.
Overview
Llama-3.1-SuperNova-Lite is an 8B parameter model developed by Arcee.ai, based on the Llama-3.1-8B-Instruct architecture. It is a distilled version of the larger Llama-3.1-405B-Instruct model, leveraging offline logits extracted from the 405B parameter variant. This 8B variation of Llama-3.1-SuperNova maintains high performance while offering exceptional instruction-following capabilities and domain-specific adaptability.
IMPORTANT INFORMATION: The model is available in two package versions. Please make sure to select the appropriate one. Once you have subscribed, we strongly recommend that you deploy it with our sample notebooks at https://github.com/arcee-ai/aws-samples .
Highlights
- The model was trained using a state-of-the-art distillation pipeline and an instruction dataset generated with EvolKit, ensuring accuracy and efficiency across a wide range of tasks. For more information on its training, visit blog.arcee.ai.
- Llama-3.1-SuperNova-Lite excels in both benchmark performance and real-world applications, providing the power of large-scale models in a more compact, efficient form ideal for organizations seeking high performance with reduced resource requirements.
Details
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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, so you pay no software charge for the model itself. You only choose the AWS instance that runs it. Pricing is organized by inference mode and instance size. Twelve dimensions cover real-time inference across several instance families, letting you serve responses on demand. Two dimensions cover batch inference on larger instance types, suited to processing many inputs at once. Within each mode, you pick a larger or smaller instance based on your workload. All dimensions bill per host hour, so cost tracks how long each instance runs.
Top-of-mind questions for buyers
One host hour is one hour that a single running instance is provisioned to serve the model. Charges accrue for every hour the instance runs, even during idle time between requests. The software itself is free, so host-hour charges reflect the AWS compute you keep running.
Real-time dimensions serve responses on demand as requests arrive, so you keep an instance running continuously. Batch dimensions process a group of inputs together in one run on larger instance types. Real-time suits interactive use; batch suits large volumes handled at once. Both bill per host hour.
Instance size sets the compute and memory available to run the 8-billion-parameter model. Larger instances handle heavier throughput or bigger request loads, while smaller ones fit lighter workloads. Cost tracks host hours, so choose the size that matches your traffic to avoid paying for unused capacity.
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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 Inferentia2 instances: inf2.xlarge, inf2.8xlarge, inf2.24xlarge, inf2.48xlarge. As the model has been compiled with a tensor parallelism level of 2, the number of model copies running on the instance is respectively 1, 1, 6, and 12. Context size is set to 8K, and batch size to 4.
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-llama-supernova-lite-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-llama-supernova-lite-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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