---
language:
- en
- zh
- multilingual
license: apache-2.0
library_name: sentence-transformers
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- embedding
- text-embedding
- retrieval
pipeline_tag: sentence-similarity
base_model: Qwen/Qwen3-Embedding-0.6B
---

# Octen-Embedding-0.6B

Octen-Embedding-0.6B is a text embedding model designed for semantic search and retrieval tasks. This model is fine-tuned from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B) and supports multiple languages, providing high-quality embeddings for various applications.

## Model Details

- **Base Model**: [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B)
- **Model Size**: 0.6B parameters
- **Max Sequence Length**: 32,768 tokens
- **Embedding Dimension**: 1024
- **Languages**: English, Chinese, and multilingual support
- **Training Method**: LoRA fine-tuning

## Usage

### Using Sentence Transformers

```python
from sentence_transformers import SentenceTransformer

model = SentenceTransformer("Octen/Octen-Embedding-0.6B")

# Encode sentences
sentences = [
    "This is an example sentence",
    "Each sentence is converted to a vector"
]

embeddings = model.encode(sentences)
print(embeddings.shape)
# Output: (2, 1024)

# Compute similarity
from sentence_transformers.util import cos_sim
similarity = cos_sim(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")
```

### Using Transformers

```python
from transformers import AutoModel, AutoTokenizer
import torch
import torch.nn.functional as F

tokenizer = AutoTokenizer.from_pretrained("Octen/Octen-Embedding-0.6B", padding_side="left")
model = AutoModel.from_pretrained("Octen/Octen-Embedding-0.6B")
model.eval()

def encode(texts):
    inputs = tokenizer(texts, padding=True, truncation=True,
                      max_length=8192, return_tensors="pt")

    with torch.no_grad():
        outputs = model(**inputs)
        # Use last token embedding
        embeddings = outputs.last_hidden_state[:, -1, :]
        # Normalize embeddings
        embeddings = F.normalize(embeddings, p=2, dim=1)

    return embeddings

# Example usage
texts = ["Hello world", "你好世界"]
embeddings = encode(texts)
similarity = torch.matmul(embeddings[0], embeddings[1])
print(f"Similarity: {similarity.item():.4f}")
```

## Recommended Use Cases

- Semantic search and information retrieval
- Document similarity and clustering
- Question answering
- Cross-lingual retrieval
- Text classification with embeddings

## Limitations

- Performance may vary across different domains and languages
- Very long documents (>32K tokens) require truncation
- Optimized for retrieval tasks, not for text generation

## License

This model is licensed under the [Apache License 2.0](https://www.apache.org/licenses/LICENSE-2.0).

This model is derived from [Qwen/Qwen3-Embedding-0.6B](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B), which is also licensed under Apache License 2.0.

## Paper

For more details, please refer to our blog post: [Octen Series: Optimizing Embedding Models to #1 on RTEB Leaderboard](https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/)

## Citation

If you find our work helpful, please consider citing:

```bibtex
@misc{octen2025rteb,
  title={Octen Series: Optimizing Embedding Models to #1 on RTEB Leaderboard},
  author={Octen Team},
  year={2025},
  url={https://octen-team.github.io/octen_blog/posts/octen-rteb-first-place/}
}
```