jina-embeddings-text-nano-retrieval

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What jina-embeddings-text-nano-retrieval is

jina-embeddings-v5-text: Task-Targeted Embedding Distillation Elastic Inference Service | ArXiv | Release Note | Blog Model Overview jina-embeddings-v5-text-nano-retrieval is a compact, high-performance text embedding model designed for information retrieval. It is part of the jina-embeddings-v5-text model family, which also includes jina-embeddings-v5-text-small, for better performance at a bigger size. Trained using a novel approach that combines distillation with task-specific contrastive losses, jina-embeddings-v5-text-nano-retrieval outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks. Training and Evaluation For training details and evaluation results, see our technical report. Usage The following Python packages are required: - transformers=5.1.0 - torch=2.8.0 - peft=0.15.2 - vllm==0.15.1 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. The fastest way to use v5-text in production. Elastic Inference Service (EIS) provides managed embedding inference with built-in scaling, so you can generate embeddings directly within your Elastic deployment. See the Elastic Inference Service documentation for setup details. Since our nano model is based on jinaai/jina-embeddings-v5-text-nano, which is not yet supported by llama.cpp, we provide our own branch of llama.cpp, which implements the necessary changes to support it for now. To start the OpenAI API compatible HTTP server, run with the respective model version: Client: Note: For the retrieval variant, add Query: or Document: prefix in front of your input as shown above. You can run the ONNX-optimized version of the model locally using Hugging Face's optimum library. Make sure you have the required dependencies installed (e.g., pip install optimum[onnxruntime] transformers torch): License The model is licensed under CC BY-NC 4.0. For commercial use, please contact us. Citation If you find jina-embeddings-v5-text-nano-retrieval useful in your research,

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

Specifications

What it is

How it works

How embedding models work

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.

Using it via the API

Call it like any OpenAI endpoint

Once AxForge deploys jina-embeddings-text-nano-retrieval for you, it answers on the OpenAI-compatible API — the same base URL and keys as every other model. (jina-embeddings-text-nano-retrieval 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-embeddings-text-nano-retrieval","input":"text to embed"}'

Details

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Commercial licence needed

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