README.md

metadata

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

Infinity Embedding Model

This is the stable default model for infinity.

pip install infinity_emb[all]

More details about the infinity inference project please refer to the Github: Infinity.

Usage for Embedding Model via infinity in Python

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="cuda",
        # or device="cpu"
        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())

CLI interface

The same args

pip install infinity_emb
infinity_emb --model-name-or-path michaelfeil/bge-small-en-v1.5 --port 7997

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.