Sold by: Arcee
Deployed on AWS
Llama-Spark is a conversational model developed by Arcee.ai. It excels at engaging in natural and informative conversations.
Overview
Llama-Spark is built on the foundation of Llama-3.1-8B and merges the power of our Tome Dataset with Llama-3.1-8B-Instruct, resulting in a remarkable conversationalist that punches well above its 8B parameter weight class. The model has a 128 KB context size.
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-spark-on-sagemaker.ipynb .
Highlights
- Llama-Spark excels across a wide range of language tasks, demonstrating particular strength in: * Reasoning: Solving complex problems and drawing logical conclusions. * Creative Writing: Generating engaging and original content across various genres. * Coding: Assisting with programming tasks, from code generation to debugging. * General Language Understanding: Comprehending and generating human-like text in diverse contexts.
- Llama-Spark can be applied to various business tasks: * Customer Service: Implement sophisticated chatbots and virtual assistants. * Content Creation: Generate high-quality written content for marketing and documentation. * Software Development: Accelerate coding processes and improve code quality. * Data Analysis: Enhance data interpretation and generate insightful reports. * Research and Development: Assist in literature reviews and hypothesis generation. * Legal and Compliance: Automate contract analysis and regulatory compliance checks.
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 to use, so you pay only for the AWS compute you run. Pricing is organized by how you deploy the model. You choose between batch inference on two instance types and real-time inference on eight instance types. Each dimension bills per host hour on a specific instance size. Larger instances within a family handle heavier workloads, while the batch options suit non-interactive processing and the real-time options serve live requests. Your cost scales with the instance you pick and the hours you run it.
Top-of-mind questions for buyers
One host hour is one hour that your chosen instance runs the model. Billing meters the running time of the instance you deploy. Each instance type bills separately per hour it stays active. Stopping the instance stops the host-hour charges tied to it.
Batch dimensions run inference on grouped, non-interactive jobs using the two p3 instance types. Real-time dimensions serve live requests using the g5 and g6 instance types. Both meter per host hour. Batch suits scheduled processing; real-time suits applications needing immediate responses, like chat or virtual assistants.
The model software carries no license fee. You pay only the AWS compute for the instance type you select, billed per host hour. Your total cost depends on which instance you choose and how many hours you keep it running.
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This product is offered for free. If there are any questions, please contact us for further clarifications.
Legal
Vendor terms and conditions
Upon subscribing to this product, you must acknowledge and agree to the terms and conditions outlined in the vendor's End User License Agreement (EULA) .
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
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 single-GPU instances of the g5 and g6 families. Context size is 4 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-llama-spark-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-spark-on-sagemaker.ipynb . This is the best way to guarantee proper configuration.
Bugs, questions, feature requests: please create an issue in the aws-samples repository on Github.
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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