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Merged
merged 3 commits into from
Sep 12, 2025
Merged

Add test_gemma3.rs for EmbeddingGemma #718

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b14e077
Add `test_gemma3.rs` for EmbeddingGemma
alvarobartt Sep 10, 2025
71b1f17
Update `README.md` and `supported_models.md`
alvarobartt Sep 10, 2025
cb36dc7
Add `HF_TOKEN` to `.github/workflows/test.yaml`
alvarobartt Sep 10, 2025
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5 changes: 3 additions & 2 deletions .github/workflows/test.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@ on:
workflow_dispatch:
push:
branches:
- 'main'
- "main"
pull_request:
paths:
- ".github/workflows/build.yaml"
Expand All @@ -17,7 +17,7 @@ on:
- "rust-toolchain.toml"
- "Dockerfile"
branches:
- 'main'
- "main"

jobs:
tests:
Expand All @@ -38,6 +38,7 @@ jobs:
env:
SCCACHE_GHA_ENABLED: "true"
RUSTC_WRAPPER: "sccache"
HF_TOKEN: ${{ secrets.HF_HUB_READ_TOKEN }}
run: |
sudo apt-get update && sudo apt-get install protobuf-compiler -y
cargo test --profile=release-debug
50 changes: 26 additions & 24 deletions README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@

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
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>
Expand Down Expand Up @@ -66,29 +66,31 @@ Ember, GTE and E5. TEI implements many features such as:
#### 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, and Qwen3.
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 | 8B (Very Expensive) | Qwen3 | [Qwen/Qwen3-Embedding-8B](https://hf.co/Qwen/Qwen3-Embedding-8B) |
| 4 | 0.6B | Qwen3 | [Qwen/Qwen3-Embedding-0.6B](https://hf.co/Qwen/Qwen3-Embedding-0.6B) |
| 6 | 7B (Very Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct) |
| 7 | 0.5B | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
| 14 | 1.5B (Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) |
| 17 | 7B (Very Expensive) | Mistral | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R) |
| 34 | 0.5B | XLM-RoBERTa | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
| 40 | 0.3B | Alibaba GTE | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
| 51 | 0.3B | Bert | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1) |
| N/A | 0.4B | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5) |
| N/A | 0.4B | ModernBERT | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large) |
| N/A | 0.3B | NomicBert | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe) |
| N/A | 0.1B | NomicBert | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1) |
| N/A | 0.1B | NomicBert | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5) |
| N/A | 0.1B | JinaBERT | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en) |
| N/A | 0.1B | JinaBERT | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code) |
| N/A | 0.1B | MPNet | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
| 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-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
| 8 | 308M | Gemma3 | [google/embeddinggemma-300m](https://hf.co/google/embeddinggemma-300m) (gated) |
| 15 | 1.78B (Expensive) | Qwen2 | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct) |
| 18 | 7.11B (Very Expensive) | Mistral | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R) |
| 35 | 568M | XLM-RoBERTa | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
| 41 | 305M | Alibaba GTE | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
| 52 | 335M | BERT | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1) |
| 58 | 137M | NomicBERT | [nomic-ai/nomic-embed-text-v1](https://hf.co/nomic-ai/nomic-embed-text-v1) |
| 79 | 137M | NomicBERT | [nomic-ai/nomic-embed-text-v1.5](https://hf.co/nomic-ai/nomic-embed-text-v1.5) |
| 103 | 109M | MPNet | [sentence-transformers/all-mpnet-base-v2](https://hf.co/sentence-transformers/all-mpnet-base-v2) |
| N/A | 475M-A305M | NomicBERT | [nomic-ai/nomic-embed-text-v2-moe](https://hf.co/nomic-ai/nomic-embed-text-v2-moe) |
| N/A | 434M | Alibaba GTE | [Alibaba-NLP/gte-large-en-v1.5](https://hf.co/Alibaba-NLP/gte-large-en-v1.5) |
| N/A | 396M | ModernBERT | [answerdotai/ModernBERT-large](https://hf.co/answerdotai/ModernBERT-large) |
| N/A | 137M | JinaBERT | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en) |
| N/A | 137M | JinaBERT | [jinaai/jina-embeddings-v2-base-code](https://hf.co/jinaai/jina-embeddings-v2-base-code) |

To explore the list of best performing text embeddings models, visit the
[Massive Text Embedding Benchmark (MTEB) Leaderboard](https://huggingface.co/spaces/mteb/leaderboard).
Expand Down Expand Up @@ -352,15 +354,15 @@ You have the option to utilize the `HF_TOKEN` environment variable for configuri
For example:

1. Go to https://huggingface.co/settings/tokens
2. Copy your cli READ token
3. Export `HF_TOKEN=<your cli READ token>`
2. Copy your CLI READ token
3. Export `HF_TOKEN=<your CLI READ token>`

or with Docker:

```shell
model=<your private model>
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
token=<your CLI READ token>

docker run --gpus all -e HF_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.8 --model-id $model
```
Expand Down
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