Sold by: Arcee
Deployed on AWS
Arcee Agent is a cutting-edge 7B parameter language model developed by Arcee.ai specifically for function calling and tool use.
Overview
Arcee Agent excels at interpreting, executing, and chaining function calls. This capability allows it to interact seamlessly with a wide range of external tools, APIs, and services. The model is compatible with various tool use formats, including Glaive FC v2, Salesforce, and Agent-FLAN. Arcee-Agent performs best when using the VLLM OpenAI FC format, but it also excels with prompt-based solutions.
Initialized from Qwen2-7B, it rivals the performance of much larger models while maintaining efficiency and speed. This model is particularly suited for developers, researchers, and businesses looking to implement sophisticated AI-driven solutions without the computational overhead of larger language models.
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_notebooks/sample-notebook-arcee-agent-on-sagemaker.ipynb .
Highlights
- Arcee Agent's unique capabilities make it an invaluable asset for businesses across various industries: * Customer Support Automation: Implement AI-driven chatbots that handle complex customer inquiries and support tickets. Automate routine support tasks such as password resets, order tracking, and FAQ responses. * Sales and Marketing Automation: Automate lead qualification and follow-up using personalized outreach based on user behavior. Generate dynamic marketing content tailored to specific audiences and platforms.
- * Financial Services Automation: Automate financial reporting and compliance checks. Implement AI-driven financial advisors for personalized investment recommendations. Integrate with financial APIs to provide real-time market analysis and alerts. * Healthcare Solutions: Automate patient record management and data retrieval for healthcare providers. * E-commerce Enhancements: Create intelligent product recommendation systems based on user preferences and behavior. Automate inventory management and supply chain logistics.
- * Human Resources Automation: Automate candidate screening and ranking based on resume analysis and job requirements. Implement virtual onboarding assistants to guide new employees through the onboarding process. Analyze employee feedback and sentiment to inform HR policies and practices. * Legal Services Automation: Automate contract analysis and extraction of key legal terms and conditions. Implement AI-driven tools for legal research and case law summarization. Develop virtual legal assistants to provide preliminary legal advice and document drafting.
Details
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Delivery method
Latest version
Deployed on AWS
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AWS Marketplace now accepts line of credit payments through the PNC Vendor Finance program. This program is available to select AWS customers in the US, excluding NV, NC, ND, TN, & VT.
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 grouped by inference mode. Ten real-time options run on g5 and g6 GPU instances, sized from xlarge up to 16xlarge, and are billed per host hour for live requests. Two batch options run on p3 instances (8xlarge and 16xlarge), also billed per host hour, for processing groups of requests at once. Larger instance sizes carry more compute capacity. You pick the mode and instance size that fit your workload.
Top-of-mind questions for buyers
One host hour covers one hour of running one GPU instance hosting the model. Charges accrue only while the instance runs. Stopped or terminated instances stop the software meter. Underlying AWS storage or other resource fees may still apply separately, but host-hour metering counts running time only.
Real-time mode runs on g5 or g6 instances and stays on to serve live requests, so you pay for every host hour it runs. Batch mode runs on p3 instances to process grouped requests, then stops when the job finishes. Real-time suits continuous traffic; batch suits scheduled bulk jobs.
You pay the host-hour rate for whichever instance size you run. Larger sizes carry more compute capacity and a higher per-hour rate. The change is manual — you choose the instance when you deploy. Running more capacity than a workload needs raises cost without added benefit.
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Vendor refund policy
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
input.json
https://github.com/arcee-ai/aws-samples/blob/main/model_package_notebooks/sample-notebook-arcee-agent-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_notebooks/sample-notebook-arcee-agent-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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