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Preparing patch release. (#2186)
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Cargo.toml

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resolver = "2"
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[workspace.package]
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version = "2.1.1-dev0"
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version = "2.1.2-dev0"
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edition = "2021"
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authors = ["Olivier Dehaene"]
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homepage = "https://github.com/huggingface/text-generation-inference"

README.md

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volume=$PWD/data
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
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ghcr.io/huggingface/text-generation-inference:2.1.0 --model-id $model
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ghcr.io/huggingface/text-generation-inference:2.1.1 --model-id $model
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```
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And then you can make requests like
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**Note:** To use NVIDIA GPUs, you need to install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/install-guide.html). We also recommend using NVIDIA drivers with CUDA version 12.2 or higher. For running the Docker container on a machine with no GPUs or CUDA support, it is enough to remove the `--gpus all` flag and add `--disable-custom-kernels`, please note CPU is not the intended platform for this project, so performance might be subpar.
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**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.1.0-rocm --model-id $model` instead of the command above.
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**Note:** TGI supports AMD Instinct MI210 and MI250 GPUs. Details can be found in the [Supported Hardware documentation](https://huggingface.co/docs/text-generation-inference/supported_models#supported-hardware). To use AMD GPUs, please use `docker run --device /dev/kfd --device /dev/dri --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.1.1-rocm --model-id $model` instead of the command above.
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To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli):
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```

docs/source/installation_amd.md

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docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \
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--device=/dev/kfd --device=/dev/dri --group-add video \
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--ipc=host --shm-size 256g --net host -v $volume:/data \
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ghcr.io/huggingface/text-generation-inference:2.1.0-rocm \
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ghcr.io/huggingface/text-generation-inference:2.1.1-rocm \
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--model-id $model
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```
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docs/source/installation_nvidia.md

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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 --shm-size 64g -p 8080:80 -v $volume:/data \
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ghcr.io/huggingface/text-generation-inference:2.1.0 \
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ghcr.io/huggingface/text-generation-inference:2.1.1 \
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--model-id $model
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```
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docs/source/quicktour.md

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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 --shm-size 1g -p 8080:80 -v $volume:/data \
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ghcr.io/huggingface/text-generation-inference:2.1.0 \
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ghcr.io/huggingface/text-generation-inference:2.1.1 \
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--model-id $model
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```
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To see all possible deploy flags and options, you can use the `--help` flag. It's possible to configure the number of shards, quantization, generation parameters, and more.
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```bash
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docker run ghcr.io/huggingface/text-generation-inference:2.1.0 --help
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docker run ghcr.io/huggingface/text-generation-inference:2.1.1 --help
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```
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</Tip>

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