jina-embeddings-v5-text-nano

by jinaai

Smallest high-quality multilingual text embedding at 239M parameters

451Kdl/month

239Mparams

HuggingFace Use in Pipeline

Identifiers

Model ID

jinaai/jina-embeddings-v5-text-nano

Feature URI

mixpeek://text_extractor@v1/jina_embeddings_v5_nano_v1

Overview

Jina Embeddings v5 Text Nano is a 239M-parameter multilingual text embedding model built on the EuroBERT-210M backbone. It achieves 71.0 on MTEB English v2 — remarkably close to the 677M v5-text-small (71.7) at one-third the size. Trained via embedding distillation from Qwen3-Embedding-4B with task-specific contrastive losses, it retains quality under aggressive dimension truncation and binary quantization.

On Mixpeek, jina-embeddings-v5-text-nano is the optimal choice for latency-critical and edge deployments where every millisecond counts. Its Matryoshka support (768 down to 32 dimensions) and robust quantization make it ideal for high-throughput text search at minimal compute cost.

Architecture

EuroBERT-210M backbone with last-token pooling. 239M parameters. Four task-specific LoRA adapters (retrieval, text-matching, clustering, classification). 8192-token context length. Matryoshka truncation from 768 to 32 dimensions.

Mixpeek SDK Integration

from mixpeek import Mixpeek

mixpeek = Mixpeek(api_key="YOUR_API_KEY")

mixpeek.ingest.documents(
    collection="knowledge_base",
    source={"type": "s3", "bucket": "docs"},
    pipeline={
        "embedding": {
            "model": "mixpeek://text_extractor@v1/jina_embeddings_v5_text_nano"
        }
    }
)