Sold by: JetBrains Americas
Deployed on AWS
Mellum Kotlin is JetBrains' large language model (LLM) optimized for code completion in Kotlin and Java languages.
Overview
Mellum Kotlin is JetBrains' large language model (LLM) optimized for code completion in Kotlin and Java languages.
Highlights
- LLaMA-like architecture with ~4B parameters.
- Context-aware code completion with fill-in-the-middle.
- Top quality-to-size ratio.
Details
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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 license, so you pay only for the AWS compute you run it on. Pricing is organized by AWS instance type and inference mode, all billed per host hour (HostHrs). The ml.g5.2xlarge is offered in both batch and real-time modes. All other instances (ml.g6.2xlarge, ml.g6e.xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, and ml.p5e.48xlarge) run in real-time mode. Each dimension reflects the hardware behind your deployment. Larger or higher-capacity instances carry different hourly rates. You pick the instance that matches your workload and latency needs.
Top-of-mind questions for buyers
One host hour is one hour of a running inference instance. Real-time mode answers requests instantly, one at a time, for low-latency use. Batch mode processes grouped requests together, better for large jobs without instant response needs. The ml.g5.2xlarge supports both; the other instances run real-time only.
Charges accrue per running host hour. When you stop an instance, the software metering stops, so no host-hour charges apply while it is powered off. Underlying AWS storage or other resource fees may still apply separately from this listing, which itself is free to license.
Each dimension maps to a specific AWS GPU instance behind your deployment. The g5, g6, and g6e types use smaller GPU configurations. The p4d, p4de, p5, and p5e types use larger multi-GPU hardware. Pick the instance that matches your throughput, concurrency, and latency needs. Larger instances carry different hourly rates.
www.jetbrains.com
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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
First version available to enterprise customers.
Additional details
Inputs
- Summary
Model input should be application/json. See the input parameters and sample input data for more information.
- Input MIME type
- application/json
{"prefix": "import js", "suffix": "\n", "filepath": "main.py", "context": [], "max_length": 32, "stop_token": "\n\n", "use_control": "off"}
{"prefix": "import js", "suffix": "\n", "filepath": "main.py", "context": [], "max_length": 32, "stop_token": "\n\n", "use_control": "off"}
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
prefix | Code that appears before the cursor. | string | Yes |
suffix | Code that appears after the cursor. | string | Yes |
filepath | Relative path of the file being edited, including file name and extension (e.g. 'src/utils/helpers.ts'). | string | Yes |
context | Additional context items (such as neighbouring files, docs, etc.) | list | No |
max_length | Maximum number of BPE tokens that may be generated for the completion snippet. | integer | Yes |
stop_token | String at which generation should stop (exclusive). | string | No |
use_control | Selects the Cloud-Control mode. Use 'off' if unsure. | "on","off","silent" | Yes |
generate_indents | Whether the model should emit indentation characters. | boolean | No |
Support
Vendor support
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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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