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

jina-embeddings-v5-text-nano-classification-GGUF

GGUF quantizations of jina-embeddings-v5-text-nano-classification 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

<p align="center"> <img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5architecture1771470917.png" alt="jina-embeddings-v5-text Architecture" width="600px"> </p>

jina-embeddings-v5-text-nano-classification is a task-specific embedding model for classification, part of the jina-embeddings-v5-text model family. | Feature | Value | | --- | --- | | Parameters | 239M | | Task | classification | | Embedding Dimension | 768 | | Matryoshka Dimensions | 32, 64, 128, 256, 512, 768 | | Pooling Strategy | Last-token pooling | | Base Model | jina-embeddings-v5-text-nano |

<p align="center"> <img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_mmteb-4.png" alt="MMTEB Multilingual Benchmark" width="500px"> </p>

<p align="center"> <img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5mteben-4.png" alt="MTEB English Benchmark" width="500px"> </p>

<p align="center"> <img src="https://jina-ai-gmbh.ghost.io/content/images/2026/02/v5_retrieval-4.png" alt="Retrieval Benchmark Results" width="500px"> </p>

Usage with llama.cpp

<details open> <summary>via <a href="https://www.elastic.co/docs/explore-analyze/elastic-inference/eis">Elastic Inference Service</a></summary>

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.

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.

</details>

# 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-classification-Q8_0.gguf \
  --pooling last -p "Your text here"

License

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