README.md

metadata

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

Infinity Embedding Model

More details please refer to the Github: Infinity.

Usage for Embedding Model via infinity

To deploy files with the infinity_emb pip package. Recommended is device="cuda", engine="torch" with flash attention on gpu, and device="cpu", engine="optimum" for onnx inference.

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 = "michaelfeil/bge-small-en-v1.5",
        device="cpu",
        # or device="cuda"
        engine="torch"
        # or engine="optimum"
        compile=True # enable torch.compile
))

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.