README.md

---
tags:
- unsloth
- sentence-transformers
- sentence-similarity
- feature-extraction
- dense
base_model: unsloth/Qwen3-Embedding-4B
pipeline_tag: sentence-similarity
library_name: sentence-transformers
---

# SentenceTransformer

This model was finetuned with [Unsloth](https://github.com/unslothai/unsloth).

[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
 based on unsloth/Qwen3-Embedding-4B

This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [unsloth/Qwen3-Embedding-4B](https://huggingface.co/unsloth/Qwen3-Embedding-4B). It maps sentences & paragraphs to a 2560-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.

## Model Details

### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [unsloth/Qwen3-Embedding-4B](https://huggingface.co/unsloth/Qwen3-Embedding-4B) <!-- at revision 8edd3dda8d779908e835f772b7290680593edc7d -->
- **Maximum Sequence Length:** 40960 tokens
- **Output Dimensionality:** 2560 dimensions
- **Similarity Function:** Cosine Similarity
<!-- - **Training Dataset:** Unknown -->
<!-- - **Language:** Unknown -->
<!-- - **License:** Unknown -->

### Model Sources

- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/huggingface/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)

### Full Model Architecture

```
SentenceTransformer(
  (0): Transformer({'max_seq_length': 40960, 'do_lower_case': False, 'architecture': 'PeftModelForFeatureExtraction'})
  (1): Pooling({'word_embedding_dimension': 2560, 'pooling_mode_cls_token': False, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': True, 'include_prompt': True})
  (2): Normalize()
)
```

## Usage

### Direct Usage (Sentence Transformers)

First install the Sentence Transformers library:

```bash
pip install -U sentence-transformers
```

Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer

# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
    'The weather is lovely today.',
    "It's so sunny outside!",
    'He drove to the stadium.',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 2560]

# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities)
# tensor([[1.0010, 0.6558, 0.1455],
#         [0.6558, 1.0010, 0.1655],
#         [0.1455, 0.1655, 1.0000]], dtype=torch.float16)
```

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## Training Details

### Framework Versions
- Python: 3.12.12
- Sentence Transformers: 5.2.2
- Transformers: 4.56.2
- PyTorch: 2.9.0+cu126
- Accelerate: 1.12.0
- Datasets: 4.3.0
- Tokenizers: 0.22.2

## Citation

### BibTeX

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