Sold by: JetBrains s.r.o.
Deployed on AWS
Mellum All is JetBrains' large language model (LLM) optimized for code completion in all languages.
Overview
Mellum All is JetBrains' large language model (LLM) optimized for code completion in all 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
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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 GPU instance type and inference mode, billed per host hour. You choose between batch mode and real-time mode on the ml.g5.2xlarge, or real-time mode on the ml.g6.2xlarge, ml.g6e.xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, and ml.p5e.48xlarge. The g-series instances suit lighter workloads, while the p-series instances offer more powerful GPUs for demanding inference. Costs scale with the instance you select and the hours it runs.
Top-of-mind questions for buyers
One host hour is one hour that your chosen instance runs in your account. Charges accrue per active running hour of that instance. If you stop the instance, host-hour charges stop. Underlying AWS storage or other resource fees may still apply while stopped.
Both meter the same instance per host hour. Real-time mode keeps the endpoint running to respond to live requests with low latency. Batch mode processes queued inputs together, so the instance runs only during the job. Real-time suits interactive use; batch suits scheduled bulk processing.
Each running instance meters its own host hours independently. Running two instances at once bills two sets of host-hour charges for the same clock time. Your total is the sum of every active instance's hours at that instance type's rate. The model itself carries no software fee.
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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
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