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.