GGUF quantizations of jina-embeddings-v5-text-nano-retrieval using llama.cpp. A 239M parameter multilingual embedding model quantized for efficient inference.

Elastic Inference Service | ArXiv | Blog

[!IMPORTANT] We highly recommend to first read this blog post for more technical details and customized llama.cpp build.

Overview

jina-embeddings-v5-text-nano-retrieval is a task-specific embedding model for retrieval, part of the jina-embeddings-v5-text model family.

Feature	Value
Parameters	239M
Task	`retrieval`
Embedding Dimension	768
Matryoshka Dimensions	32, 64, 128, 256, 512, 768
Pooling Strategy	Last-token pooling
Base Model	jina-embeddings-v5-text-nano

Usage with llama.cpp

via Elastic Inference Service

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.

bash

PUT _inference/text_embedding/jina-v5
{
  "service": "elastic",
  "service_settings": {
    "model_id": "jina-embeddings-v5-text-nano"
  }
}

See the Elastic Inference Service documentation for setup details.

bash

# Build llama.cpp (upstream)
git clone https://github.com/ggml-org/llama.cpp
cd llama.cpp && cmake -B build && cmake --build build --config Release

# Run embedding
./build/bin/llama-embedding -m jina-embeddings-v5-text-nano-retrieval-Q8_0.gguf \
  --pooling last -p "Your text here"

License

CC-BY-NC-4.0. For commercial use, please contact us.

jinaai/jina-embeddings-v5-text-nano-retrieval-GGUF

jina-embeddings-v5-text-nano-retrieval-GGUF

Overview

Usage with llama.cpp

License

jinaai/
jina-embeddings-v5-text-nano-retrieval-GGUF