Default to Qwen3 in README.md and docs/ examples

README.md

@@ -110,7 +110,7 @@ Below are some examples of the currently supported models:
 ### Docker
 
 ```shell
-model=BAAI/bge-large-en-v1.5
+model=Qwen/Qwen3-Embedding-0.6B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id $model
@@ -369,13 +369,13 @@ cd models
 
 # Make sure you have git-lfs installed (https://git-lfs.com)
 git lfs install
-git clone https://huggingface.co/Alibaba-NLP/gte-base-en-v1.5
+git clone https://huggingface.co/Qwen/Qwen3-Embedding-0.6B
 
 # Set the models directory as the volume path
 volume=$PWD
 
 # Mount the models directory inside the container with a volume and set the model ID
-docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id /data/gte-base-en-v1.5
+docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id /data/Qwen3-Embedding-0.6B
 ```
 
 ### Using Re-rankers models
@@ -458,7 +458,7 @@ found [here](https://github.com/huggingface/text-embeddings-inference/blob/main/
 You can use the gRPC API by adding the `-grpc` tag to any TEI Docker image. For example:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
+model=Qwen/Qwen3-Embedding-0.6B
 volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
 
 docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7-grpc --model-id $model
@@ -494,7 +494,7 @@ cargo install --path router -F metal
 You can now launch Text Embeddings Inference on CPU with:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
+model=Qwen/Qwen3-Embedding-0.6B
 
 text-embeddings-router --model-id $model --port 8080
 ```
@@ -532,7 +532,7 @@ cargo install --path router -F candle-cuda -F http --no-default-features
 You can now launch Text Embeddings Inference on GPU with:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
+model=Qwen/Qwen3-Embedding-0.6B
 
 text-embeddings-router --model-id $model --port 8080
 ```

docs/source/en/intel_container.md

@@ -35,7 +35,7 @@ docker build . -f Dockerfile-intel --build-arg PLATFORM=$platform -t tei_cpu_ipe
 To deploy your model on an Intel® CPU, use the following command:
 
 ```shell
-model='BAAI/bge-large-en-v1.5'
+model='Qwen/Qwen3-Embedding-0.6B'
 volume=$PWD/data
 
 docker run -p 8080:80 -v $volume:/data tei_cpu_ipex --model-id $model
@@ -58,7 +58,7 @@ docker build . -f Dockerfile-intel --build-arg PLATFORM=$platform -t tei_xpu_ipe
 To deploy your model on an Intel® XPU, use the following command:
 
 ```shell
-model='BAAI/bge-large-en-v1.5'
+model='Qwen/Qwen3-Embedding-0.6B'
 volume=$PWD/data
 
 docker run -p 8080:80 -v $volume:/data --device=/dev/dri -v /dev/dri/by-path:/dev/dri/by-path tei_xpu_ipex --model-id $model --dtype float16
@@ -81,7 +81,7 @@ docker build . -f Dockerfile-intel --build-arg PLATFORM=$platform -t tei_hpu
 To deploy your model on an Intel® HPU (Gaudi), use the following command:
 
 ```shell
-model='BAAI/bge-large-en-v1.5'
+model='Qwen/Qwen3-Embedding-0.6B'
 volume=$PWD/data
 
 docker run -p 8080:80 -v $volume:/data --runtime=habana -e HABANA_VISIBLE_DEVICES=all -e MAX_WARMUP_SEQUENCE_LENGTH=512 tei_hpu --model-id $model --dtype bfloat16

docs/source/en/local_cpu.md

@@ -47,10 +47,9 @@ cargo install --path router -F metal
 Once the installation is successfully complete, you can launch Text Embeddings Inference on CPU with the following command:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
+model=Qwen/Qwen3-Embedding-0.6B
 
-text-embeddings-router --model-id $model --revision $revision --port 8080
+text-embeddings-router --model-id $model --port 8080
 ```
 
 <Tip>

docs/source/en/local_gpu.md

@@ -58,8 +58,7 @@ cargo install --path router -F candle-cuda -F http --no-default-features
 You can now launch Text Embeddings Inference on GPU with:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
+model=Qwen/Qwen3-Embedding-0.6B
 
-text-embeddings-router --model-id $model --revision $revision --port 8080
+text-embeddings-router --model-id $model --dtype float16 --port 8080
 ```

docs/source/en/local_metal.md

@@ -38,10 +38,9 @@ cargo install --path router -F metal
 Once the installation is successfully complete, you can launch Text Embeddings Inference with Metal with the following command:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
-revision=refs/pr/5
+model=Qwen/Qwen3-Embedding-0.6B
 
-text-embeddings-router --model-id $model --revision $revision --port 8080
+text-embeddings-router --model-id $model --port 8080
 ```
 
 Now you are ready to use `text-embeddings-inference` locally on your machine.

docs/source/en/quick_tour.md

@@ -28,10 +28,10 @@ Next, install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/
 
 ## Deploy
 
-Next it's time to deploy your model. Let's say you want to use [`BAAI/bge-large-en-v1.5`](https://huggingface.co/BAAI/bge-large-en-v1.5). Here's how you can do this:
+Next it's time to deploy your model. Let's say you want to use [`Qwen/Qwen3-Embedding-0.6B`](https://huggingface.co/Qwen/Qwen3-Embedding-0.6B). Here's how you can do this:
 
 ```shell
-model=BAAI/bge-large-en-v1.5
+model=Qwen/Qwen3-Embedding-0.6B
 volume=$PWD/data
 
 docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.7 --model-id $model

docs/source/en/supported_models.md

@@ -21,21 +21,24 @@ We are continually expanding our support for other model types and plan to inclu
 ## Supported embeddings models
 
 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, and ModernBERT.
+model with Alibi positions and Mistral, Alibaba GTE, Qwen2 models with Rope positions, MPNet, ModernBERT, and Qwen3.
 
 Below are some examples of the currently supported models:
 
 | MTEB Rank | Model Size          | Model Type  | Model ID                                                                                         |
 |-----------|---------------------|-------------|--------------------------------------------------------------------------------------------------|
-| 3         | 7B (Very Expensive) | Qwen2       | [Alibaba-NLP/gte-Qwen2-7B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-7B-instruct)             |
-| 11        | 1.5B (Expensive)    | Qwen2       | [Alibaba-NLP/gte-Qwen2-1.5B-instruct](https://hf.co/Alibaba-NLP/gte-Qwen2-1.5B-instruct)         |
-| 14        | 7B (Very Expensive) | Mistral     | [Salesforce/SFR-Embedding-2_R](https://hf.co/Salesforce/SFR-Embedding-2_R)                       |
-| 20        | 0.3B                | Bert        | [WhereIsAI/UAE-Large-V1](https://hf.co/WhereIsAI/UAE-Large-V1)                                   |
-| 31        | 0.5B                | XLM-RoBERTa | [Snowflake/snowflake-arctic-embed-l-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-l-v2.0) |
-| 37        | 0.3B                | Alibaba GTE | [Snowflake/snowflake-arctic-embed-m-v2.0](https://hf.co/Snowflake/snowflake-arctic-embed-m-v2.0) |
-| 49        | 0.5B                | XLM-RoBERTa | [intfloat/multilingual-e5-large-instruct](https://hf.co/intfloat/multilingual-e5-large-instruct) |
+| 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       | 0.4B                | 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)             |
@@ -56,6 +59,7 @@ Below are some examples of the currently supported models:
 | Re-Ranking         | XLM-RoBERTa | [BAAI/bge-reranker-large](https://huggingface.co/BAAI/bge-reranker-large)                                       |
 | Re-Ranking         | XLM-RoBERTa | [BAAI/bge-reranker-base](https://huggingface.co/BAAI/bge-reranker-base)                                         |
 | Re-Ranking         | GTE         | [Alibaba-NLP/gte-multilingual-reranker-base](https://huggingface.co/Alibaba-NLP/gte-multilingual-reranker-base) |
+| Re-Ranking         | ModernBert  | [Alibaba-NLP/gte-reranker-modernbert-base](https://huggingface.co/Alibaba-NLP/gte-reranker-modernbert-base) |
 | Sentiment Analysis | RoBERTa     | [SamLowe/roberta-base-go_emotions](https://huggingface.co/SamLowe/roberta-base-go_emotions)                     |
 
 ## Supported hardware