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jinaai / jina-embeddings-v5-text-small-retrieval
Model's Last Updated: April 15 2026
sentence-similarity
Introduction of jina-embeddings-v5-text-small-retrieval
Model Details of jina-embeddings-v5-text-small-retrieval
jina-embeddings-v5-text-small-retrieval : Retrieval-Targeted Embedding Distillation
Blog | Technical Report | Jina API | EIS
Model Overview
jina-embeddings-v5-text-small-retrieval
is a compact, high-performance text embedding model designed for information retrieval.
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 | Qwen/Qwen3-0.6B-Base |
Training and Evaluation
For training details and evaluation results, see our technical report .
Usage
Requirements
The following Python packages are required:
-
transformers>=5.1.0 -
torch>=2.8.0 -
peft>=0.15.2 -
vllm>=0.15.1
Optional / Recommended
- flash-attention : Installing flash-attention is recommended for improved inference speed and efficiency, but not mandatory.
-
sentence-transformers
: If you want to use the model via the
sentence-transformersinterface, install this package as well.
via vLLM
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")
via llama.cpp (GGUF)
After installing llama.cpp one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version: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."
]
}'
License
The model is licensed under CC BY-NC 4.0. For commercial use, please contact us .
Citation
If you find
jina-embeddings-v5-text-small-retrieval
useful in your research, please cite the following paper:
[will be published soon]
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