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OlivierDehaeneOlivierDehaene
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fix: docs
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‎README.md

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@@ -97,7 +97,7 @@ model=BAAI/bge-large-en-v1.5
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revision=refs/pr/5
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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:1.0 --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:1.1 --model-id $model --revision $revision
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```
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And then you can make requests like
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| Architecture | Image |
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|-------------------------------------|-------------------------------------------------------------------------|
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-1.0 |
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-1.1 |
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| Volta | NOT SUPPORTED |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-1.0 (experimental) |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:1.0 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-1.0 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.0 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-1.0 (experimental) |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-1.1 (experimental) |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:1.1 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-1.1 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.1 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-1.1 (experimental) |
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**Warning**: Flash Attention is turned off by default for the Turing image as it suffers from precision issues.
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You can turn Flash Attention v1 ON by using the `USE_FLASH_ATTENTION=True` environment variable.
@@ -280,7 +280,7 @@ model=<your private model>
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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token=<your cli READ token>
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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:1.0 --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:1.1 --model-id $model
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```
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### Using Re-rankers models
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revision=refs/pr/4
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volume=$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:1.0 --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:1.1 --model-id $model --revision $revision
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```
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And then you can rank the similarity between a query and a list of texts with:
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model=SamLowe/roberta-base-go_emotions
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volume=$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:1.0 --model-id $model
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.1 --model-id $model
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```
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Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:
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revision=refs/pr/5
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volume=$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:1.0-grpc --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:1.1-grpc --model-id $model --revision $revision
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```
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```shell

‎docs/source/en/private_models.md

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volume=$PWD/data
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token=<your cli Hugging Face Hub token>
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docker run --gpus all -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.0 --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:1.1 --model-id $model
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```

‎docs/source/en/quick_tour.md

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volume=$PWD/data
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.0 --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:1.1 --model-id $model --revision $revision
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```
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<Tip>
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volume=$PWD/data
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.0 --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:1.1 --model-id $model --revision $revision
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```
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Once you have deployed a model you can use the `rerank` endpoint to rank the similarity between a query and a list
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model=SamLowe/roberta-base-go_emotions
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volume=$PWD/data
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.0 --model-id $model
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docker run --gpus all -p 8080:80 -v $volume:/data --pull always ghcr.io/huggingface/text-embeddings-inference:1.1 --model-id $model
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```
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Once you have deployed the model you can use the `predict` endpoint to get the emotions most associated with an input:

‎docs/source/en/supported_models.md

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| Architecture | Image |
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|-------------------------------------|--------------------------------------------------------------------------|
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-1.0 |
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| CPU | ghcr.io/huggingface/text-embeddings-inference:cpu-1.1 |
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| Volta | NOT SUPPORTED |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-1.0 (experimental) |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:1.0 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-1.0 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.0 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-1.0 (experimental) |
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| Turing (T4, RTX 2000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:turing-1.1 (experimental) |
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| Ampere 80 (A100, A30) | ghcr.io/huggingface/text-embeddings-inference:1.1 |
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| Ampere 86 (A10, A40, ...) | ghcr.io/huggingface/text-embeddings-inference:86-1.1 |
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| Ada Lovelace (RTX 4000 series, ...) | ghcr.io/huggingface/text-embeddings-inference:89-1.1 |
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| Hopper (H100) | ghcr.io/huggingface/text-embeddings-inference:hopper-1.1 (experimental) |
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**Warning**: Flash Attention is turned off by default for the Turing image as it suffers from precision issues.
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You can turn Flash Attention v1 ON by using the `USE_FLASH_ATTENTION=True` environment variable.

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