Sold by: Jina AIÂ
Overview
jina-code-embeddings-1.5b is a 1.54 billion parameter model representing a significant advancement in code retrieval capabilities. Built on Qwen2.5-Coder-1.5B backbone with last-token pooling, it moves beyond traditional training on limited aligned data to leverage vast unaligned code and documentation corpora. The model implements comprehensive task-specific instructions across five categories: NL2Code, TechQA, Code2Code, Code2NL, and Code2Completion, each with distinct prefixes for queries and documents. Supports Matryoshka representation learning for flexible embedding truncation. Despite larger size, maintains practical deployment characteristics while achieving benchmark performance competitive with substantially larger alternatives.
Highlights
- Multilingual support (15+ programming languages) and compatibility with a wide range of domains, including web development, software development, machine learning, data science, and educational coding problems.
- Task-specific instruction prefixes for NL2Code, Code2Code, Code2NL, Code2Completion, and Technical QA, which can be selected at inference time.
- Flexible embedding size: dense embeddings are 1536-dimensional by default but can be truncated to as low as 128 with minimal performance loss.
Details
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Delivery method
Latest version
Deployed on AWS
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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.g5.xlarge Inference (Batch) Recommended | Model inference on the ml.g5.xlarge instance type, batch mode | $2.50 |
ml.g5.xlarge Inference (Real-Time) Recommended | Model inference on the ml.g5.xlarge instance type, real-time mode | $2.50 |
ml.p2.xlarge Inference (Batch) | Model inference on the ml.p2.xlarge instance type, batch mode | $2.30 |
ml.p2.8xlarge Inference (Batch) | Model inference on the ml.p2.8xlarge instance type, batch mode | $18.00 |
ml.p2.16xlarge Inference (Batch) | Model inference on the ml.p2.16xlarge instance type, batch mode | $35.00 |
ml.p3.2xlarge Inference (Batch) | Model inference on the ml.p3.2xlarge instance type, batch mode | $7.00 |
ml.p3.8xlarge Inference (Batch) | Model inference on the ml.p3.8xlarge instance type, batch mode | $25.00 |
ml.p3.16xlarge Inference (Batch) | Model inference on the ml.p3.16xlarge instance type, batch mode | $48.25 |
ml.g4dn.xlarge Inference (Batch) | Model inference on the ml.g4dn.xlarge instance type, batch mode | $1.50 |
ml.g4dn.2xlarge Inference (Batch) | Model inference on the ml.g4dn.2xlarge instance type, batch mode | $2.20 |
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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
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Additional details
Inputs
- Summary
The model accepts JSON inputs. Texts must be passed in the following format.
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-inference-code-1500m-input.jsonÂ
- Input MIME type
- text/csv
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-inference-code-1500m-input.json
https://github.com/jina-ai/jina-sagemaker/blob/main/examples/sample-batch-input.csv
Input data descriptions
The following table describes supported input data fields for real-time inference and batch transform.
Field name | Description | Constraints | Required |
|---|---|---|---|
data | Texts to embed | n/a | No |
parameters.task | Task type:
nl2code.query, nl2code.passage, code2code.query, code2code.passage,
code2nl.query, code2nl.passage, code2completion.query, code2completion.passage,
qa.query, qa.passage.
nl2code.query
Find the most relevant code snippet given the following query.
nl2code.passage
Candidate code snippet.
code2code.query
Find an equivalent code snippet given the following code snippet.
code2code.passage
Candidate code snippet.
code2nl.query
Find the most relevant comment given the following code snippet.
code2nl.passage
Candidate comment.
code2completion.query
Find the most relevant completion given the following start of code snippet.
code2completion.passage
Candidate completion
qa.query
Find the most relevant answer given the following question.
qa.passage
Candidate answer. | - | No |
Support
Vendor support
We provide support for this model package through our enterprise support channel:
AWS infrastructure support
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