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Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
A blazing fast inference solution for text embeddings models
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Today
+1 stars today
This week
+7 stars this week
This month
+37 stars this month
Momentum
71.0
growth rate 0.14%/day
<div align="center">
# Text Embeddings Inference
<a href="https://github.com/huggingface/text-embeddings-inference">
<img alt="GitHub Repo stars" src="https://img.shields.io/github/stars/huggingface/text-embeddings-inference?style=social">
</a>
<a href="https://huggingface.github.io/text-embeddings-inference">
<img alt="Swagger API documentation" src="https://img.shields.io/badge/API-Swagger-informational">
</a>
A blazing fast inference solution for text embeddings models.
Benchmark for [BAAI/bge-base-en-v1.5](https://huggingface.co/BAAI/bge-base-en-v1.5) on an NVIDIA A10 with a sequence
length of 512 tokens:
<p>
<img src="assets/bs1-lat.png" width="400" />
<img src="assets/bs1-tp.png" width="400" />
</p>
<p>
<img src="assets/bs32-lat.png" width="400" />
<img src="assets/bs32-tp.png" width="400" />
</p>
</div>
## Table of contents
- [Get Started](#get-started)
- [Supported Models](#supported-models)
- [Docker](#docker)
- [Docker Images](#docker-images)
- [API Documentation](#api-documentation)
- [Using a private or gated model](#using-a-private-or-gated-model)
- [Air gapped deployment](#air-gapped-deployment)
- [Using Re-rankers models](#using-re-rankers-models)
- [Using Sequence Classification models](#using-sequence-classification-models)
- [Using SPLADE pooling](#using-splade-pooling)
- [Distributed Tracing](#distributed-tracing)
- [gRPC](#grpc)
- [Local Install](#local-install)
- [Apple Silicon (Homebrew)](#apple-silicon-homebrew)
- [Docker Build](#docker-build)
- [ARM64 / aarch64](#arm64--aarch64)
- [AMD Instinct GPUs (ROCm)](#amd-instinct-gpus-rocm)
- [Examples](#examples)
Text Embeddings Inference (TEI) is a toolkit for deploying and serving open source text embeddings and sequence
classification models. TEI enables high-performance extraction for the most popular models, including FlagEmbedding,
Ember, GTE and E5. TEI implements many features such as:
* No model graph compilation step
* Metal support for local execution on Macs
* Small docker images and fast boot times. Get ready for true serverless!
* Token based dynamic batching
* Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention),
[Candle](https://github.com/huggingface/candle)
and [cuBLASLt](https://docs.nvidia.com/cuda/cublas/#using-the-cublaslt-api)
* [Safetensors](https://github.com/huggingface/safetensors) weight loading
* [ONNX](https://github.com/onnx/onnx) weight loading
* Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
## Get Started
### Supported Models
#### Text Embeddings
Text Embeddings Inference currently supports Nomic, BERT, CamemBERT, XLM-RoBERTa models with absolute positions, JinaBERT
model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, Qwen3, and Gemma3.
Below are some examples of the currently supported models:
| MTEB Rank | Model Size | Model Type | Model ID |
|-----------|------------------------|----------------|--------------------------------------------------------------------------------------------------|
| 2 | 7.57B (Very Expensive) | Qwen3 | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B) |
| 3 | 4.02B (Very Expensive) | Qwen3 | [Qwen/Qwen3-Embedding-4B](https://hf.co/Qwen/Qwen3-Embedding-4B) |
| 4 | 509M | Qwen3 | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B) |
| 6 | 7.61B (Very Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct) |
| 7 | 560M | XLM-RoBERTa | [intfloat/multilingual-e5-large-instructExcerpt of 28,564 characters
Read on GitHub
@apple · France
133
Alvaro Bartolome · @huggingface · Spain
63
Nicolas Patry · @huggingface
31
kaixuanliu · intel · China
22
Hyeongchan Kim · South Korea
22
Michael Feil · @basetenlabs · United States
10
Vinay R Damodaran · @cisco · United States
7
Adrien · @huggingface · France
5
Maria Khalusova · Unstructured.io · Canada
4
Guillaume LEGENDRE
3
3
HuggingFace · Belgium
2
Salman Chishti · GitHub, ex-Microsoft · United Kingdom
2
BADAOUI Abdennacer · HuggingFace · France
2
drbh · drbh
2
2
2
Pauline Bailly-Masson
1
1
Tai An
1
Would you bet a product on this? Bounded 0–100 and slow moving.
matched fp:df7e1a74ca9ae81c, topic:embeddings, name:text embeddings, desc:text embeddings
matched fp:df7e1a74ca9ae81c, topic:llm