---
license: cc-by-nc-4.0
language:
- multilingual
base_model:
- Qwen/Qwen3-0.6B-Base
tags:
- feature-extraction
- mteb
- sentence-transformers
library_name: transformers
---
<br><br>

<p align="center">
<img src="https://huggingface.co/datasets/jinaai/documentation-images/resolve/main/logo.webp" alt="Jina AI: Your Search Foundation, Supercharged!" width="150px">
</p>

### **jina-embeddings-v5-text-small-clustering**: Clustering-Targeted Embedding Distillation

[Blog](https://picsum.photos/200/300) | [Technical Report](https://picsum.photos/200/300) | [Jina API](https://jina.ai/embeddings) | [EIS](https://www.elastic.co/docs/explore-analyze/elastic-inference/eis)

### Model Overview

`jina-embeddings-v5-text-small-clustering` is a compact, high-performance text embedding model designed for clustering.

It is part of the **jina-embeddings-v5-text** model family, which also includes [jina-embeddings-v5-text-nano](https://huggingface.co/jinaai/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-clustering` outperforms existing state-of-the-art models of similar size across diverse embedding benchmarks.

| Feature | Value |
| --- | --- |
| Parameters | 677M |
| Supported Tasks | `clustering`|
| 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](https://picsum.photos/200/300).

### Usage

<details>
  <summary>Requirements</a></summary>
  
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](https://github.com/Dao-AILab/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-transformers` interface, install this package as well.

</details>

<details>
  <summary>via <a href="https://github.com/vllm-project/vllm">vLLM</a></summary>

```python
from vllm import LLM

# Initialize model
name = "jinaai/jina-embeddings-v5-text-small-clustering"
model = LLM(
    model=name,
    dtype="float16",
    runner="pooling",
    pooler_config=PoolerConfig(seq_pooling_type="LAST", normalize=True)
)

# Create text prompts
document1 = "Overview of climate change impacts on coastal cities"
document1_prompt = f"Document: {document1}"

document2 = "The impacts of climate change on large cities"
document2_prompt = f"Document: {document2}"

# Encode all prompts
prompts = [document1_prompt, document2_prompt]
outputs = model.encode(prompts, pooling_task="embed")


embed_document1 = outputs[0].outputs.data
embed_document2 = outputs[1].outputs.data
```

</details>

<details>
  <summary> via <a href="https://github.com/ggml-org/llama.cpp">llama.cpp (GGUF)</a></summary>
After installing <a href="https://github.com/ggml-org/llama.cpp">llama.cpp</a> one can run llama-server to host the embedding model as OpenAI API compatible HTTP server with the respective model version:

```sh
llama-server -hf jinaai/jina-embeddings-v5-text-small-clustering: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": [
      "Document: A beautiful sunset over the beach",
      "Document: 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."
    ]
  }'
```


</details>

### License

The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](sales@jina.ai).


### Citation

If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:

[will be published soon]