Sold by: Arcee AIÂ
Deployed on AWS
Coder Small is a 14-billion parameter coding model developed by Arcee AI.
Overview
The Coder models are purpose-built models for developers and excel in both benchmark performance and real-world applications. They bring you the same quality as much larger models in a more compact form, ideal for organizations looking for both performance and cost efficiency.
Coder Small is a 14-billion parameter model based on the Qwen 2.5 architecture. Compared to Coder Large, Coder Small gives you a lightweight option for faster, simpler coding workflows and autocomplete tasks.
IMPORTANT INFORMATION: Once you have subscribed to the model, you may deploy it with our sample notebook at https://github.com/arcee-ai/aws-samples .
Highlights
- Tasks: * Writing and Generating Code: Creating code snippets, scripts, and full programs in various programming languages (e.g., Python, Java, C++, JavaScript). * Debugging Code: Identifying and fixing errors in existing code. * Code Optimization: Improving the performance and efficiency of code. * Algorithm Design: Developing algorithms to solve specific problems. * Learning Resources: Recommending tutorials, documentation, and learning materials for different programming languages and frameworks.
- Use cases: * Software Development: Writing and generating code for custom software solutions tailored to business needs. * API Integration: Assisting with the integration of third-party APIs to enhance functionality and data flow. * Data Analysis: Using code to perform data analysis and generate insights for business decision-making. * DevOps Implementation: Helping with setting up CI/CD pipelines, containerization, and cloud deployments to streamline development processes. * Security Assurance: Providing code reviews and security best practices.
Details
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Delivery method
Latest version
Deployed on AWS
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Features and programs
Financing for AWS Marketplace purchases
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
Pricing is based on actual usage, with charges varying according to how much you consume. 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.
Dimension | Description | Cost/host/hour |
|---|---|---|
ml.p3.8xlarge Inference (Batch) Recommended | Model inference on the ml.p3.8xlarge instance type, batch mode | $0.00 |
ml.g5.12xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.12xlarge instance type, real-time mode | $1.83 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $0.00 |
ml.g6e.12xlarge Inference (Real-Time) | Model inference on the ml.g6e.12xlarge instance type, real-time mode | $2.84 |
ml.g6.12xlarge Inference (Real-Time) | Model inference on the ml.g6.12xlarge instance type, real-time mode | $1.18 |
ml.p4de.24xlarge Inference (Real-Time) | Model inference on the ml.p4de.24xlarge instance type, real-time mode | $4.38 |
ml.g6e.24xlarge Inference (Real-Time) | Model inference on the ml.g6e.24xlarge instance type, real-time mode | $2.84 |
ml.g6.24xlarge Inference (Real-Time) | Model inference on the ml.g6.24xlarge instance type, real-time mode | $1.18 |
ml.p4d.24xlarge Inference (Real-Time) | Model inference on the ml.p4d.24xlarge instance type, real-time mode | $4.38 |
ml.g6.48xlarge Inference (Real-Time) | Model inference on the ml.g6.48xlarge instance type, real-time mode | $1.83 |
Vendor refund policy
No refund is available.
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)Â .
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 GPU instances of the g5, g6, g6e, p4d, p4de, and p5 families. Context size is 32K 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-coder-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
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-virtuoso-on-sagemaker.ipynb . This is the best way to guarantee proper configuration.
Contact: julien@arcee.aiÂ
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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