# infinity **Repository Path**: devin-alan/infinity ## Basic Information - **Project Name**: infinity - **Description**: Infinity is a high-throughput, low-latency serving engine for text-embeddings, reranking models, clip, clap and colpali - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: main - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2025-08-26 - **Last Updated**: 2025-08-26 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README [![Contributors][contributors-shield]][contributors-url] [![Forks][forks-shield]][forks-url] [![Stargazers][stars-shield]][stars-url] [![Issues][issues-shield]][issues-url] [![MIT License][license-shield]][license-url] # Infinity ♾️ [![codecov][codecov-shield]][codecov-url] [![ci][ci-shield]][ci-url] [![Downloads][pepa-shield]][pepa-url] [![DOI](https://zenodo.org/badge/703686617.svg)](https://zenodo.org/doi/10.5281/zenodo.11406462) ![Docker pulls](https://img.shields.io/docker/pulls/michaelf34/infinity) Infinity is a high-throughput, low-latency REST API for serving text-embeddings, reranking models, clip, clap and colpali. Infinity is developed under [MIT License](https://github.com/michaelfeil/infinity/blob/main/LICENSE). ## Why Infinity * **Deploy any model from HuggingFace**: deploy any embedding, reranking, clip and sentence-transformer model from [HuggingFace]( https://huggingface.co/models?other=text-embeddings-inference&sort=trending) * **Fast inference backends**: The inference server is built on top of [PyTorch](https://github.com/pytorch/pytorch), [optimum (ONNX/TensorRT)](https://huggingface.co/docs/optimum/index) and [CTranslate2](https://github.com/OpenNMT/CTranslate2), using FlashAttention to get the most out of your **NVIDIA CUDA**, **AMD ROCM**, **CPU**, **AWS INF2** or **APPLE MPS** accelerator. Infinity uses dynamic batching and tokenization dedicated in worker threads. * **Multi-modal and multi-model**: Mix-and-match multiple models. Infinity orchestrates them. * **Tested implementation**: Unit and end-to-end tested. Embeddings via infinity are correctly embedded. Lets API users create embeddings till infinity and beyond. * **Easy to use**: Built on [FastAPI](https://fastapi.tiangolo.com/). Infinity CLI v2 allows launching of all arguments via Environment variable or argument. OpenAPI aligned to [OpenAI's API specs](https://platform.openai.com/docs/guides/embeddings/what-are-embeddings). View the docs at [https://michaelfeil.github.io/infinity](https://michaelfeil.github.io/infinity/) on how to get started.

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### Latest News πŸ”₯ - [2025/07] Blackwell support - [2024/11] AMD, CPU, ONNX docker images - [2024/10] `pip install infinity_client` - [2024/07] Inference deployment example via [Modal](./infra/modal/README.md) and a [free GPU deployment](https://infinity.modal.michaelfeil.eu/) - [2024/06] Support for multi-modal: clip, text-classification & launch all arguments from env variables - [2024/05] launch multiple models using the `v2` cli, including `--api-key` - [2024/03] infinity supports experimental int8 (cpu/cuda) and fp8 (H100/MI300) support - [2024/03] Docs are online: https://michaelfeil.github.io/infinity/latest/ - [2024/02] Community meetup at the [Run:AI Infra Club](https://discord.gg/7D4fbEgWjv) - [2024/01] TensorRT / ONNX inference - [2023/10] Initial release ## Getting started ### Launch the cli via pip install ```bash pip install infinity-emb[all] ``` After your pip install, with your venv active, you can run the CLI directly. ```bash infinity_emb v2 --model-id BAAI/bge-small-en-v1.5 ``` Check the `v2 --help` command to get a description for all parameters. ```bash infinity_emb v2 --help ``` ### Launch the CLI using a pre-built docker container (recommended) Instead of installing the CLI via pip, you may also use docker to run `michaelf34/infinity`. Make sure you mount your accelerator ( i.e. install `nvidia-docker` and activate with `--gpus all`). ```bash port=7997 model1=michaelfeil/bge-small-en-v1.5 model2=mixedbread-ai/mxbai-rerank-xsmall-v1 volume=$PWD/data docker run -it --gpus all \ -v $volume:/app/.cache \ -p $port:$port \ michaelf34/infinity:latest \ v2 \ --model-id $model1 \ --model-id $model2 \ --port $port ``` The cache path inside the docker container is set by the environment variable `HF_HOME`. #### Specialized docker images
Docker container for CPU Use the `latest-cpu` image or `x.x.x-cpu` for slimer image. Run like any other cpu-only docker image. Optimum/Onnx is often the prefered engine. ``` docker run -it \ -v $volume:/app/.cache \ -p $port:$port \ michaelf34/infinity:latest-cpu \ v2 \ --engine optimum \ --model-id $model1 \ --model-id $model2 \ --port $port ```
Docker Container for ROCm (MI200 Series and MI300 Series) Use the `latest-rocm` image or `x.x.x-rocm` for rocm compatible inference. **This image is currently not build via CI/CD (to large), consider pinning to exact version.** Make sure you have ROCm is correctly installed and ready to use with Docker. Visit [Docs](https://michaelfeil.github.io/infinity) for more info.
Docker Container for Onnx-GPU, Cuda Extensions, TensorRT Use the `latest-trt-onnx` image or `x.x.x-trt-onnx` for nvidia compatible inference. **This image is currently not build via CI/CD (to large), consider pinning to exact version.** This image has support for: - ONNX-Cuda "CudaExecutionProvider" - ONNX-TensorRT "TensorRTExecutionProvider" (may not always work due to version mismatch with ORT) - CudaExtensions and packages, e.g. Tri-Dao's `pip install flash-attn` package when using Pytorch. - nvcc compiler support ``` docker run -it \ -v $volume:/app/.cache \ -p $port:$port \ michaelf34/infinity:latest-trt-onnx \ v2 \ --engine optimum \ --device cuda \ --model-id $model1 \ --port $port ```
#### Using local models with Docker container In order to deploy a local model with a docker container, you need to mount the model inside the container and specify the path in the container to the launch command. Example: ```bash git lfs install cd /tmp mkdir models && cd models && git clone https://huggingface.co/BAAI/bge-small-en-v1.5 docker run -it -v /tmp/models:/models -p 8081:8081 michaelf34/infinity:latest v2 --model-id "/models/bge-small-en-v1.5" --port 8081 ``` #### Advanced CLI usage
Launching multiple models at once Since `infinity_emb>=0.0.34`, you can use cli `v2` method to launch multiple models at the same time. Checkout `infinity_emb v2 --help` for all args and validation. Multiple Model CLI Playbook: - 1. cli options can be repeated e.g. `v2 --model-id model/id1 --model-id model/id2 --batch-size 8 --batch-size 4`. This will create two models `model/id1` and `model/id2` - 2. or adapt the defaults by setting ENV Variables separated by `;`: `INFINITY_MODEL_ID="model/id1;model/id2;" && INFINITY_BATCH_SIZE="8;4;"` - 3. single items are broadcasted to `--model-id` length, `v2 --model-id model/id1 --model-id/id2 --batch-size 8` making both models have batch-size 8. - 4. Everything is broadcasted to the number of `--model-id` + API requests are routed to the `--served-model-name/--model-id`
Using environment variables instead of the cli All CLI arguments are also launchable via environment variables. Environment variables start with `INFINITY_{UPPER_CASE_SNAKE_CASE}` and often match the `--{lower-case-kebab-case}` cli arguments. The following two are equivalent: - CLI `infinity_emb v2 --model-id BAAI/bge-base-en-v1.5` - ENV-CLI: `export INFINITY_MODEL_ID="BAAI/bge-base-en-v1.5" && infinity_emb v2` Multiple arguments can be used via `;` syntax: `INFINITY_MODEL_ID="model/id1;model/id2;"`
API Key Supply an `--api-key secret123` via CLI or ENV INFINITY_API_KEY="secret123".
Chosing the fastest engine With the command `--engine torch` the model must be compatible with https://github.com/UKPLab/sentence-transformers/ and AutoModel With the command `--engine optimum`, there must be an onnx file. Models from https://huggingface.co/Xenova are recommended. With the command `--engine ctranslate2` - only `BERT` models are supported.
Telemetry opt-out See which telemetry is collected: https://michaelfeil.eu/infinity/main/telemetry/ ``` # Disable export INFINITY_ANONYMOUS_USAGE_STATS="0" ```
### Supported Tasks and Models by Infinity Infinity aims to be the inference server supporting most functionality for embeddings, reranking and related RAG tasks. The following Infinity tests 15+ architectures and all of the below cases in the Github CI. Click on the sections below to find tasks and **validated example models**.
Text Embeddings Text embeddings measure the relatedness of text strings. Embeddings are used for search, clustering, recommendations. Think about a private deployed version of openai's text embeddings. https://platform.openai.com/docs/guides/embeddings Tested embedding models: - [mixedbread-ai/mxbai-embed-large-v1](https://huggingface.co/mixedbread-ai/mxbai-embed-large-v1) - [WhereIsAI/UAE-Large-V1](https://huggingface.co/WhereIsAI/UAE-Large-V1) - [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) - [Alibaba-NLP/gte-large-en-v1.5](https://huggingface.co/Alibaba-NLP/gte-large-en-v1.5) - [jinaai/jina-embeddings-v2-base-code](https://huggingface.co/jinaai/jina-embeddings-v2-base-code) - [sentence-transformers/all-MiniLM-L6-v2](https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2) - [intfloat/multilingual-e5-large-instruct](https://huggingface.co/intfloat/multilingual-e5-large-instruct) - [intfloat/multilingual-e5-small](https://huggingface.co/intfloat/multilingual-e5-small) - [jinaai/jina-embeddings-v3](nomic-ai/nomic-embed-text-v1.5) - [BAAI/bge-m3, no sparse](https://huggingface.co/BAAI/bge-m3) - decoder-based models. Keep in mind that they are ~20-100x larger (&slower) than bert-small models: - [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct/discussions/20) - [Salesforce/SFR-Embedding-2_R](https://huggingface.co/Salesforce/SFR-Embedding-2_R/discussions/6) - [Alibaba-NLP/gte-Qwen2-7B-instruct](https://huggingface.co/Alibaba-NLP/gte-Qwen2-7B-instruct/discussions/39) Other models: - Most embedding model are likely supported: https://huggingface.co/models?pipeline_tag=feature-extraction&other=text-embeddings-inference&sort=trending - Check MTEB leaderboard for models https://huggingface.co/spaces/mteb/leaderboard.
Reranking Given a query and a list of documents, Reranking indexes the documents from most to least semantically relevant to the query. Think like a locally deployed version of https://docs.cohere.com/reference/rerank Tested reranking models: - [mixedbread-ai/mxbai-rerank-xsmall-v1](https://huggingface.co/mixedbread-ai/mxbai-rerank-xsmall-v1) - [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) - [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base) - [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large) - [BAAI/bge-reranker-v2-m3](https://huggingface.co/BAAI/bge-reranker-v2-m3) - [jinaai/jina-reranker-v1-turbo-en](https://huggingface.co/jinaai/jina-reranker-v1-turbo-en) Other reranking models: - Reranking Models supported by infinity are bert-style classification Models with one category. - Most reranking model are likely supported: https://huggingface.co/models?pipeline_tag=text-classification&other=text-embeddings-inference&sort=trending - https://huggingface.co/models?pipeline_tag=text-classification&sort=trending&search=rerank
Multi-modal and cross-modal - image and audio embeddings Specialized embedding models that allow for image<->text or image<->audio search. Typically, these models allow for text<->text, text<->other and other<->other search, with accuracy tradeoffs when going cross-modal. Image<->text models can be used for e.g. photo-gallery search, where users can type in keywords to find photos, or use a photo to find related images. Audio<->text models are less popular, and can be e.g. used to find music songs based on a text description or related music songs. Tested image<->text models: - [wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M](https://huggingface.co/wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M) - [jinaai/jina-clip-v1](https://huggingface.co/jinaai/jina-clip-v1) - [google/siglip-so400m-patch14-384](https://huggingface.co/google/siglip-so400m-patch14-384) - Models of type: ClipModel / SiglipModel in `config.json` Tested audio<->text models: - [Clap Models from LAION](https://huggingface.co/collections/laion/clap-contrastive-language-audio-pretraining-65415c0b18373b607262a490) - limited number open source organizations training these models - * Note: The sampling rate of the audio data needs to match the model * Not supported: - Plain vision models e.g. nomic-ai/nomic-embed-vision-v1.5
ColBert-style late-interaction Embeddings ColBert Embeddings don't perform any special Pooling methods, but return the raw **token embeddings**. The **token embeddings** are then to be scored with the MaxSim Metric in a VectorDB (Qdrant / Vespa) For usage via the RestAPI, late-interaction embeddings may best be transported via `base64` encoding. Example notebook: https://colab.research.google.com/drive/14FqLc0N_z92_VgL_zygWV5pJZkaskyk7?usp=sharing Tested colbert models: - [colbert-ir/colbertv2.0](https://huggingface.co/colbert-ir/colbertv2.0) - [jinaai/jina-colbert-v2](https://huggingface.co/jinaai/jina-colbert-v2) - [mixedbread-ai/mxbai-colbert-large-v1](https://huggingface.co/mixedbread-ai/mxbai-colbert-large-v1) - [answerai-colbert-small-v1 - click link for instructions](https://huggingface.co/answerdotai/answerai-colbert-small-v1/discussions/14)
ColPali-style late-interaction Image<->Text Embeddings Similar usage to ColBert, but scanning over an image<->text instead of only text. For usage via the RestAPI, late-interaction embeddings may best be transported via `base64` encoding. Example notebook: https://colab.research.google.com/drive/14FqLc0N_z92_VgL_zygWV5pJZkaskyk7?usp=sharing Tested ColPali/ColQwen models: - [vidore/colpali-v1.2-merged](https://huggingface.co/michaelfeil/colpali-v1.2-merged) - [michaelfeil/colqwen2-v0.1](https://huggingface.co/michaelfeil/colqwen2-v0.1) - No lora adapters supported, only "merged" models.
Text classification A bert-style multi-label text classification. Classifies it into distinct categories. Tested models: - [ProsusAI/finbert](https://huggingface.co/ProsusAI/finbert), financial news classification - [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions), text to emotion categories. - bert-style text-classifcation models with more than >1 label in `config.json`
### Infinity usage via the Python API Instead of the cli & RestAPI use infinity's interface via the Python API. This gives you most flexibility. The Python API builds on `asyncio` with its `await/async` features, to allow concurrent processing of requests. Arguments of the CLI are also available via Python. #### Embeddings ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine sentences = ["Embed this is sentence via Infinity.", "Paris is in France."] array = AsyncEngineArray.from_args([ EngineArgs(model_name_or_path = "BAAI/bge-small-en-v1.5", engine="torch", embedding_dtype="float32", dtype="auto") ]) async def embed_text(engine: AsyncEmbeddingEngine): async with engine: embeddings, usage = await engine.embed(sentences=sentences) # or handle the async start / stop yourself. await engine.astart() embeddings, usage = await engine.embed(sentences=sentences) await engine.astop() asyncio.run(embed_text(array[0])) ``` #### Reranking Reranking gives you a score for similarity between a query and multiple documents. Use it in conjunction with a VectorDB+Embeddings, or as standalone for small amount of documents. Please select a model from huggingface that is a AutoModelForSequenceClassification compatible model with one class classification. ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine query = "What is the python package infinity_emb?" docs = ["This is a document not related to the python package infinity_emb, hence...", "Paris is in France!", "infinity_emb is a package for sentence embeddings and rerankings using transformer models in Python!"] array = AsyncEmbeddingEngine.from_args( [EngineArgs(model_name_or_path = "mixedbread-ai/mxbai-rerank-xsmall-v1", engine="torch")] ) async def rerank(engine: AsyncEmbeddingEngine): async with engine: ranking, usage = await engine.rerank(query=query, docs=docs) print(list(zip(ranking, docs))) # or handle the async start / stop yourself. await engine.astart() ranking, usage = await engine.rerank(query=query, docs=docs) await engine.astop() asyncio.run(rerank(array[0])) ``` When using the CLI, use this command to launch rerankers: ```bash infinity_emb v2 --model-id mixedbread-ai/mxbai-rerank-xsmall-v1 ``` #### Image-Embeddings: CLIP models CLIP models are able to encode images and text at the same time. ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine sentences = ["This is awesome.", "I am bored."] images = ["http://images.cocodataset.org/val2017/000000039769.jpg"] engine_args = EngineArgs( model_name_or_path = "wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M", engine="torch" ) array = AsyncEngineArray.from_args([engine_args]) async def embed(engine: AsyncEmbeddingEngine): await engine.astart() embeddings, usage = await engine.embed(sentences=sentences) embeddings_image, _ = await engine.image_embed(images=images) await engine.astop() asyncio.run(embed(array["wkcn/TinyCLIP-ViT-8M-16-Text-3M-YFCC15M"])) ``` #### Audio-Embeddings: CLAP models CLAP models are able to encode audio and text at the same time. ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine import requests import soundfile as sf import io sentences = ["This is awesome.", "I am bored."] url = "https://bigsoundbank.com/UPLOAD/wav/2380.wav" raw_bytes = requests.get(url, stream=True).content audios = [raw_bytes] engine_args = EngineArgs( model_name_or_path = "laion/clap-htsat-unfused", dtype="float32", engine="torch" ) array = AsyncEngineArray.from_args([engine_args]) async def embed(engine: AsyncEmbeddingEngine): await engine.astart() embeddings, usage = await engine.embed(sentences=sentences) embedding_audios = await engine.audio_embed(audios=audios) await engine.astop() asyncio.run(embed(array["laion/clap-htsat-unfused"])) ``` #### Text Classification Use text classification with Infinity's `classify` feature, which allows for sentiment analysis, emotion detection, and more classification tasks. ```python import asyncio from infinity_emb import AsyncEngineArray, EngineArgs, AsyncEmbeddingEngine sentences = ["This is awesome.", "I am bored."] engine_args = EngineArgs( model_name_or_path = "SamLowe/roberta-base-go_emotions", engine="torch", model_warmup=True) array = AsyncEngineArray.from_args([engine_args]) async def classifier(engine: AsyncEmbeddingEngine): async with engine: predictions, usage = await engine.classify(sentences=sentences) # or handle the async start / stop yourself. await engine.astart() predictions, usage = await engine.classify(sentences=sentences) await engine.astop() asyncio.run(classifier(array["SamLowe/roberta-base-go_emotions"])) ``` ### Infinity usage via the Python Client Infinity has a generated client code for RestAPI client side usage. If you want to call a remote infinity instance via RestAPI, install the following package locally: ```bash pip install infinity_client ``` For more information, check out the Client Readme https://github.com/michaelfeil/infinity/tree/main/libs/client_infinity/infinity_client ## Integrations: - [Serverless deployments at Runpod](https://github.com/runpod-workers/worker-infinity-embedding) - [Truefoundry Cognita](https://github.com/truefoundry/cognita) - [Langchain example](https://github.com/langchain-ai/langchain) - [imitater - A unified language model server built upon vllm and infinity.](https://github.com/the-seeds/imitater) - [Dwarves Foundation: Deployment examples using Modal.com](https://github.com/dwarvesf/llm-hosting) - [infiniflow/Ragflow](https://github.com/infiniflow/ragflow) - [SAP Core AI](https://github.com/SAP-samples/btp-generative-ai-hub-use-cases/tree/main/10-byom-oss-llm-ai-core) - [gpt_server - gpt_server is an open-source framework designed for production-level deployment of LLMs (Large Language Models) or Embeddings.](https://github.com/shell-nlp/gpt_server) - [KubeAI: Kubernetes AI Operator for inferencing](https://github.com/substratusai/kubeai) - [LangChain](https://python.langchain.com/docs/integrations/text_embedding/infinity) - [Batched, modification of the Batching algoritm in Infinity](https://github.com/mixedbread-ai/batched) ## Documentation View the docs at [https:///michaelfeil.github.io/infinity](https://michaelfeil.github.io/infinity) on how to get started. After startup, the Swagger Ui will be available under `{url}:{port}/docs`, in this case `http://localhost:7997/docs`. You can also find a interactive preview here: https://infinity.modal.michaelfeil.eu/docs (and https://michaelfeil-infinity.hf.space/docs) ## Contribute and Develop Install via Poetry 1.8.1, Python3.11 on Ubuntu 22.04 ```bash cd libs/infinity_emb poetry install --extras all --with lint,test ``` To pass the CI: ```bash cd libs/infinity_emb make precommit ``` All contributions must be made in a way to be compatible with the MIT License of this repo. ### Citation ``` @software{feil_2023_11630143, author = {Feil, Michael}, title = {Infinity - To Embeddings and Beyond}, month = oct, year = 2023, publisher = {Zenodo}, doi = {10.5281/zenodo.11630143}, url = {https://doi.org/10.5281/zenodo.11630143} } ``` ### πŸ’š Current contributors [contributors-shield]: https://img.shields.io/github/contributors/michaelfeil/infinity.svg?style=for-the-badge [contributors-url]: https://github.com/michaelfeil/infinity/graphs/contributors [forks-shield]: https://img.shields.io/github/forks/michaelfeil/infinity.svg?style=for-the-badge [forks-url]: https://github.com/michaelfeil/infinity/network/members [stars-shield]: https://img.shields.io/github/stars/michaelfeil/infinity.svg?style=for-the-badge [stars-url]: https://github.com/michaelfeil/infinity/stargazers [issues-shield]: https://img.shields.io/github/issues/michaelfeil/infinity.svg?style=for-the-badge [issues-url]: https://github.com/michaelfeil/infinity/issues [license-shield]: https://img.shields.io/github/license/michaelfeil/infinity.svg?style=for-the-badge [license-url]: https://github.com/michaelfeil/infinity/blob/main/LICENSE [pepa-shield]: https://static.pepy.tech/badge/infinity-emb [pepa-url]: https://www.pepy.tech/projects/infinity-emb [codecov-shield]: https://codecov.io/gh/michaelfeil/infinity/branch/main/graph/badge.svg?token=NMVQY5QOFQ [codecov-url]: https://codecov.io/gh/michaelfeil/infinity/branch/main [ci-shield]: https://github.com/michaelfeil/infinity/actions/workflows/ci.yaml/badge.svg [ci-url]: https://github.com/michaelfeil/infinity/actions