jina-embeddings-text-small-retrieval

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What jina-embeddings-text-small-retrieval is

jina-embeddings-v5-text-small-retrieval: Retrieval-Targeted Embedding Distillation Elastic Inference Service | ArXiv | Release Note | Blog Model Overview jina-embeddings-v5-text-small-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-nano, a smaller model for more resource-constrained use cases. Trained using a novel approach that combines distillation with task-specific contrastive losses, jina-embeddings-v5-text-small-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. - Via Docker on CPU: - Via Docker on NVIDIA GPU (Turing, Ampere, Ada Lovelace, Hopper or Blackwell): Alternatively, you can also run with cargo, more information can be found in the Text Embeddings Inference documentation. Send a request to /v1/embeddings to generate embeddings via the OpenAI Embeddings API: Or rather via the Text Embeddings Inference API specification instead, to prevent from manually formatting the inputs: After installing <a href="https://github.com/ggml-org/llama.cpp"llama.cpp</a one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version: Client: After installing <a href="https://github.com/ggml-org/llama.cpp"llama.cpp</a one can run llama-server to host the embedding

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

Details

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Papers

Licence

Commercial licence needed

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