Model reference · open weights

jina-code-embeddings

jina-code-embeddings is an open-weight embedding model from jinaai, listed in the AxForge catalogue. AxForge can bring it up on EU-owned hardware for you on request — with the licence handled where one is required.

Licence fee required Embeddings jinaai 2 variants 83k downloads/mo
Request a licence + hosting quote All served models Not on the shared API today — deployed on request.

About

What jina-code-embeddings is

Jina Code Embeddings: A Small but Performant Code Embedding Model Intended Usage & Model Info jina-code-embeddings is an embedding model for code retrieval. The model supports various types of code retrieval (text-to-code, code-to-code, code-to-text, code-to-completion) and technical question answering across 15+ programming languages. Built on Qwen/Qwen2.5-Coder-0.5B, jina-code-embeddings-0.5b features: - 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 896-dimensional by default but can be truncated to as low as 64 with minimal performance loss. Summary of features: Usage The following Python packages are required: - transformers=4.53.0 - torch=2.7.1 Optional / Recommended - flash-attention: Installing flash-attention is recommended for improved inference speed and efficiency, but not mandatory. - sentence-transformers: If you want to use the model via the sentence-transformers interface, install this package as well. Citation Please refer to our technical report of jina-code-embeddings for training details and benchmarks. If you find it useful in your research, please cite the following paper: Contact Join our Discord community and chat with other community members about ideas.

Summarised from the published model card. Read the full card on the HuggingFace links below.

Specifications

What it is

Makerjinaai
TypeEmbedding models
Parameters (lead)494M
Context32k tokens
Variants2
Runs withtransformers
Based onQwen/Qwen2.5-Coder-0.5B
Released2025-08-19
Popularity83k downloads / month
Likes52
LicenceCommercial licence needed

How it works

How embedding models work

Your textsentence / documentEncodermaps meaningVectorlist of numbersAn embedding model turns text into a vector, so similar meanings sit close together — the basis of search and RAG.

Variants

Sizes & precisions

Open weights ship in several sizes and precisions. One page, all the variants — pick the one that fits your GPU. VRAM figures are estimates from model size.

VariantParamsPrecisionVRAMFits 16 GBWeights
jina-code-embeddings-0.5b494MBF16~1.1 GBWeights ↗
jina-code-embeddings-1.5b1.5BBF16~3.6 GBWeights ↗

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-code-embeddings for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-code-embeddings below is illustrative; you get the exact model name on deployment.)

$ curl -sS https://api.axforge.ai/v1/embeddings \
  -H "Authorization: Bearer $AXFORGE_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"jina-code-embeddings","input":"text to embed"}'

Details

Languages, data & research

Tags

transformers safetensors qwen2 text-generation feature-extraction mteb sentence-transformers deploy:azure

Papers

Licence

Commercial licence needed

The weights are open but cc-by-nc-4.0 needs a commercial agreement for business use. AxForge can arrange that licence and host the model for you — you pay AxForge, we settle with the model’s maker. Ask us for a quote. Read the licence ↗

Sources

Weights & code

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