Create README.md

README.md

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+---
+base_model:
+- Qwen/Qwen3-0.6B
+license: cc
+---
+<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) | [API](https://jina.ai/embeddings)
+
+### 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>=4.57.0`
+- `torch>=2.8.0`
+- `peft>=0.15.2`
+  
+### 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, task="embed", dtype="bfloat16")
+
+# 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>
+
+### License
+
+The model is licensed under CC BY-NC 4.0. For commercial use, please [contact us](link).
+
+
+### Citation
+
+If you find `jina-embeddings-v5-text-small-clustering` useful in your research, please cite the following paper:
+
+[TODO]