Sold by: Arcee
Deployed on AWS
Arcee-Spark is a powerful 7B language model developed by Arcee.ai.
Overview
Arcee Spark offers a 32 KB context size. Initialized from Qwen2, it underwent a sophisticated training process:
- Fine-tuned on 1.8 million samples
- Merged with Qwen2-7B-Instruct using Arcee's mergekit
- Further refined using Direct Preference Optimization (DPO)
This meticulous process results in exceptional performance, with Arcee Spark achieving the highest score on MT-Bench for models of its size, outperforming even GPT-3.5 on many tasks.
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-arcee-spark-on-sagemaker.ipynb
Highlights
- Arcee-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.
- Arcee-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 instance you run it on. Pricing is organized by instance type and inference mode. Two dimensions cover batch inference on ml.p3.8xlarge and ml.p3.16xlarge instances. The remaining ten dimensions cover real-time inference across g5 and g6 instance families, in sizes from xlarge up to 16xlarge. All dimensions bill per host hour. You choose an instance based on your workload size and whether you need batch or real-time processing. Larger instances handle heavier workloads.
Top-of-mind questions for buyers
Both bill per host hour on the chosen instance. Batch mode processes grouped requests together and runs on p3 instances. Real-time mode serves live requests with low latency and runs on g5 and g6 instances. You pick the mode that fits your workload; each accrues hourly while the instance runs.
Charges accrue per host hour while the instance runs. The software carries no fee since the model is free, so you pay only the AWS instance cost. Stopped instances stop accruing host-hour charges, though underlying AWS storage may still apply. Terminate instances you no longer need to avoid ongoing cost.
Each dimension maps to one AWS instance type, from xlarge up to 16xlarge. Larger sizes carry more GPU and memory to handle heavier request loads or higher concurrency. You pick one instance based on your traffic and latency needs. Only that instance's host hours appear on your bill.
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Legal
Vendor terms and conditions
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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 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-arcee-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-arcee-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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