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v0.3.0
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‎Cargo.lock

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‎Cargo.toml

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resolver = "2"
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[workspace.package]
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version = "0.2.1"
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version = "0.3.0"
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edition = "2021"
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authors = ["Olivier Dehaene"]
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homepage = "https://github.com/huggingface/text-embeddings-inference"

‎README.md

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### Supported Models
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You can use any BERT, CamemBERT or XLM-RoBERTa model with absolute positions in `text-embeddings-inference`.
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You can use any JinaBERT model with Alibi or absolute positions or any BERT, CamemBERT or XLM-RoBERTa model with
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absolute positions in `text-embeddings-inference`.
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**Support for other model types will be added in the future.**
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Examples of supported models:
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| MTEB Rank | Model Type | Model ID |
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|-----------|--------------|--------------------------------------------------------------------------------|
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| 1 | Bert | [BAAI/bge-large-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) |
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| 2 | | [BAAI/bge-base-en-v1.5](https://hf.co/BAAI/bge-base-en-v1.5) |
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| 3 | | [llmrails/ember-v1](https://hf.co/llmrails/ember-v1) |
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| 4 | | [thenlper/gte-large](https://hf.co/thenlper/gte-large) |
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| 5 | | [thenlper/gte-base](https://hf.co/thenlper/gte-base) |
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| 6 | | [intfloat/e5-large-v2](https://hf.co/intfloat/e5-large-v2) |
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| 7 | | [BAAI/bge-small-en-v1.5](https://hf.co/BAAI/bge-small-en-v1.5) |
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| 10 | | [intfloat/e5-base-v2](https://hf.co/intfloat/e5-base-v2) |
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| 11 | XLM-RoBERTa | [intfloat/multilingual-e5-large](https://hf.co/intfloat/multilingual-e5-large) |
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| MTEB Rank | Model Type | Model ID |
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|-----------|-------------|----------------------------------------------------------------------------------------|
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| 1 | Bert | [BAAI/bge-large-en-v1.5](https://hf.co/BAAI/bge-large-en-v1.5) |
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| 2 | | [BAAI/bge-base-en-v1.5](https://hf.co/BAAI/bge-base-en-v1.5) |
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| 3 | | [llmrails/ember-v1](https://hf.co/llmrails/ember-v1) |
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| 4 | | [thenlper/gte-large](https://hf.co/thenlper/gte-large) |
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| 5 | | [thenlper/gte-base](https://hf.co/thenlper/gte-base) |
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| 6 | | [intfloat/e5-large-v2](https://hf.co/intfloat/e5-large-v2) |
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| 7 | | [BAAI/bge-small-en-v1.5](https://hf.co/BAAI/bge-small-en-v1.5) |
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| 10 | | [intfloat/e5-base-v2](https://hf.co/intfloat/e5-base-v2) |
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| 11 | XLM-RoBERTa | [intfloat/multilingual-e5-large](https://hf.co/intfloat/multilingual-e5-large) |
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| N/A | JinaBERT | [jinaai/jina-embeddings-v2-base-en](https://hf.co/jinaai/jina-embeddings-v2-base-en) |
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| N/A | JinaBERT | [jinaai/jina-embeddings-v2-small-en](https://hf.co/jinaai/jina-embeddings-v2-small-en) |
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You can explore the list of best performing text embeddings models [here](https://huggingface.co/spaces/mteb/leaderboard).
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.2.2 --model-id $model --revision $revision
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.3.0 --model-id $model --revision $revision
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```
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And then you can make requests like
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Text Embeddings Inference ships with multiple Docker images that you can use to target a specific backend:
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| Architecture | Image |
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|-------------------------------------|------------------------------------------------------------|
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-0.2.2 |
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| Volta | NOT SUPPORTED |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-0.2.2 |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:0.2.2 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-0.2.2 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-0.2.2 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-0.2.2 |
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| Architecture | Image |
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|-------------------------------------|---------------------------------------------------------------------------|
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-0.3.0 |
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| Volta | NOT SUPPORTED |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-0.3.0 (experimental) |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:0.3.0 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-0.3.0 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-0.3.0 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-0.3.0 (experimental) |
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### API documentation
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docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.2.2 --model-id $model
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docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:0.3.0 --model-id $model
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```
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### Distributed Tracing

‎docs/openapi.json

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"license": {
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"name": "HFOIL"
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},
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"version": "0.2.1"
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"version": "0.3.0"
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},
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"paths": {
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"/embed": {

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