jina-embeddings-v5-text-nano-retrieval-mlx
MLX port of jina-embeddings-v5-text-nano-retrieval for efficient inference on Apple Silicon. For the full-size model, see jina-embeddings-v5-text-small-retrieval-mlx.
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Model Overview
| Feature | Value |
|---|---|
| Parameters | 239M |
| Supported Task | retrieval |
| Max Sequence Length | 8192 |
| Embedding Dimension | 768 |
| Pooling Strategy | Last-token pooling |
| Base Model | EuroBERT/EuroBERT-210m |
| Format | MLX float16 safetensors |
For training details and evaluation results, see our technical report.
Requirements
pip install mlx tokenizers
Usage
git clone https://huggingface.co/jinaai/jina-embeddings-v5-text-nano-retrieval-mlx
cd jina-embeddings-v5-text-nano-retrieval-mlx
import mlx.core as mx
from tokenizers import Tokenizer
from model import JinaEmbeddingModel
import json
with open("config.json") as f:
config = json.load(f)
model = JinaEmbeddingModel(config)
weights = mx.load("model.safetensors")
model.load_weights(list(weights.items()))
tokenizer = Tokenizer.from_file("tokenizer.json")
# Encode query
query_embeddings = model.encode(
["Overview of climate change impacts on coastal cities"],
tokenizer,
task_type="retrieval.query",
)
# Encode document
document_embeddings = model.encode(
["Climate change has led to rising sea levels, increased frequency of extreme weather events..."],
tokenizer,
task_type="retrieval.passage",
)
License
jina-embeddings-v5-text-nano is licensed under CC BY-NC 4.0. For commercial use, please contact sales@jina.ai.
Citation
If you find jina-embeddings-v5-text-nano useful in your research, please cite the following paper:
@article{akram2026jina,
title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation},
author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael G{\"u}nther and Maximilian Werk and Han Xiao},
journal={arXiv preprint arXiv:2602.15547},
year={2026}
}