---
tags:
- sentence-transformers
- feature-extraction
- sentence-similarity
- transformers
license: mit
language:
- en
---


<h1 align="center">Infinity Embedding Model</h1>


More details please refer to the Github: [Infinity](https://github.com/michaelfeil/infinity).



## Usage 

### Usage for Embedding Model via infinity

Its also possible to deploy files with the [infinity_emb](https://github.com/michaelfeil/infinity) pip package.
Recommended is `device="cuda", engine="torch"` with flash attention on gpu, and `device="cpu", engine="optimum"` for onnx inference.

```python
import asyncio
from infinity_emb import AsyncEmbeddingEngine, EngineArgs

sentences = ["Embed this is sentence via Infinity.", "Paris is in France."]
engine = AsyncEmbeddingEngine.from_args(
    EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", device="cpu", engine="optimum" # or engine="torch"
))

async def main(): 
    async with engine:
        embeddings, usage = await engine.embed(sentences=sentences)
asyncio.run(main())
```


## Contact
If you have any question or suggestion related to this project, feel free to open an issue or pull request.
You also can email Michael Feil (infinity at michaelfeil.eu).


## Citation

If you find this repository useful, please consider giving a star :star: and citation

```
@software{Feil_Infinity_2023,
author = {Feil, Michael},
month = oct,
title = {{Infinity - To Embeddings and Beyond}},
url = {https://github.com/michaelfeil/infinity},
year = {2023}
}
```

## License
Infinity is licensed under the [MIT License](https://github.com/michaelfeil/infinity/blob/master/LICENSE).