Sold by: JetBrains s.r.o.
Deployed on AWS
Mellum Python is JetBrains' large language model (LLM) optimized for code completion in python.
Overview
Mellum Python is JetBrains' large language model (LLM) optimized for code completion in python.
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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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; you pay only for the AWS compute that runs the model. Pricing is organized by GPU instance type and inference mode. One option runs on the ml.g5.2xlarge in batch mode, processing requests in groups. The rest run in real-time mode for live responses, spanning the ml.g5.2xlarge, ml.g6.2xlarge, ml.g6e.xlarge, ml.p4d.24xlarge, ml.p4de.24xlarge, ml.p5.48xlarge, and ml.p5e.48xlarge. All are billed by host hours, so cost scales with how long each instance runs. You choose the instance size that fits your workload.
Top-of-mind questions for buyers
A host hour is one hour that a chosen GPU instance runs. Billing meters the time the instance stays active, from launch until you stop it. Cost scales with running hours per instance. Idle or stopped instances stop accruing host-hour charges, though underlying AWS storage fees may still apply.
Both bill by host hours on the same instance. Batch mode processes requests in groups, so it suits scheduled or bulk workloads. Real-time mode handles live requests as they arrive, suiting interactive use. You pick the mode that matches your response-time needs; the metering unit stays the same.
The model itself carries no software charge. You pay only for the AWS GPU compute that runs it, billed per host hour on your chosen instance. Your bill depends on which instance type you select and how long it runs, not on the model software.
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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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