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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.
| Feature | Value |
|---|---|
| Parameters | 677M |
| Supported Tasks | retrieval |
| Max Sequence Length | 32768 |
| Embedding Dimension | 1024 |
| Matryoshka Dimensions | 32, 64, 128, 256, 512, 768, 1024 |
| Pooling Strategy | Last-token pooling |
| Base Model | jinaai/jina-embeddings-v5-text-small |
For training details and evaluation results, see our technical report.
The following Python packages are required:
transformers>=5.1.0torch>=2.8.0peft>=0.15.2vllm>=0.15.1sentence-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.
PUT _inference/text_embedding/jina-v5
{
"service": "elastic",
"service_settings": {
"model_id": "jina-embeddings-v5-text-small"
}
}
See the Elastic Inference Service documentation for setup details.
from sentence_transformers import SentenceTransformer
import torch
model = SentenceTransformer(
"jinaai/jina-embeddings-v5-text-small-retrieval",
model_kwargs={"dtype": torch.bfloat16}, # Recommended for GPUs
config_kwargs={"_attn_implementation": "flash_attention_2"}, # Recommended but optional
)
# Optional: set truncate_dim in encode() to control embedding size
query = "Which planet is known as the Red Planet?"
documents = [
"Venus is often called Earth's twin because of its similar size and proximity.",
"Mars, known for its reddish appearance, is often referred to as the Red Planet.",
"Jupiter, the largest planet in our solar system, has a prominent red spot.",
"Saturn, famous for its rings, is sometimes mistaken for the Red Planet.",
]
# Encode query and documents
query_embeddings = model.encode(sentences=query, prompt_name="query")
document_embeddings = model.encode(sentences=documents, prompt_name="document")
print(query_embeddings.shape, document_embeddings.shape)
# (1024,) (4, 1024)
similarity = model.similarity(query_embeddings, document_embeddings)
print(similarity)
# tensor([[0.4860, 0.7611, 0.5914, 0.6188]])
from vllm import LLM
from vllm.config.pooler import PoolerConfig
# Initialize model
name = "jinaai/jina-embeddings-v5-text-small-retrieval"
model = LLM(
model=name,
dtype="float16",
runner="pooling",
pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True),
)
# Create text prompts
query = "Overview of climate change impacts on coastal cities"
query_prompt = f"Query: {query}"
document = "The impacts of climate change on coastal cities are significant.."
document_prompt = f"Document: {document}"
# Encode all prompts
prompts = [query_prompt, document_prompt]
outputs = model.encode(prompts, pooling_task="embed")
docker run -p 8080:80 \
ghcr.io/huggingface/text-embeddings-inference:cpu-1.9 \
--model-id jinaai/jina-embeddings-v5-text-small-retrieval \
--dtype float32 --pooling last-token
docker run --gpus all --shm-size 1g -p 8080:80 \
ghcr.io/huggingface/text-embeddings-inference:cuda-1.9 \
--model-id jinaai/jina-embeddings-v5-text-small-retrieval \
--dtype float16 --pooling last-token
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:
curl -X POST http://127.0.0.1:8080/v1/embeddings \
-H "Content-Type: application/json" \
-d '{
"model": "jinaai/jina-embeddings-v5-text-small-retrieval",
"input": [
"Query: Overview of climate change impacts on coastal cities",
"Document: The impacts of climate change on coastal cities are significant...",
]
}'
Or rather via the Text Embeddings Inference API specification instead, to prevent from manually formatting the inputs:
curl -X POST http://127.0.0.1:8080/embed \
-H "Content-Type: application/json" \
-d '{
"inputs": "Overview of climate change impacts on coastal cities",
"prompt_name": "query",
}'
llama-server -hf jinaai/jina-embeddings-v5-text-small-retrieval:F16 --embedding --pooling last -ub 32768
Client:
curl -X POST "http://127.0.0.1:8080/v1/embeddings" \
-H "Content-Type: application/json" \
-d '{
"input": [
"Query: A beautiful sunset over the beach",
"Query: Un beau coucher de soleil sur la plage",
"Document: 海滩上美丽的日落",
"Document: 浜辺に沈む美しい夕日",
"Document: Golden sunlight melts into the horizon, painting waves in warm amber and rose, while the sky whispers goodnight to the quiet, endless sea."
]
}'
The model is licensed under CC BY-NC 4.0. For commercial use, please contact us.
If you find jina-embeddings-v5-text-small-retrieval useful in your research, please cite the following paper:
@misc{akram2026jinaembeddingsv5texttasktargetedembeddingdistillation,
title={jina-embeddings-v5-text: Task-Targeted Embedding Distillation},
author={Mohammad Kalim Akram and Saba Sturua and Nastia Havriushenko and Quentin Herreros and Michael Günther and Maximilian Werk and Han Xiao},
year={2026},
eprint={2602.15547},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2602.15547},
}