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https://github.com/huggingface/text-generation-inference.git
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29a0893b67
* Upgrade the version number. * Remove modifications in Lock. * Tmp branch to test transformers backend with 2.5.1 and TP>1 * Fixing the transformers backend. inference_mode forces the use of `aten.matmul` instead of `aten.mm` the former doesn't have sharding support crashing the transformers TP support. `lm_head.forward` also crashes because it skips the hook that cast/decast the DTensor. Torch 2.5.1 is required for sharding support. * Put back the attention impl. * Revert the flashinfer (this will fails). * Building AOT. * Using 2.5 kernels. * Remove the archlist, it's defined in the docker anyway.
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Using TGI with Nvidia GPUs
TGI optimized models are supported on NVIDIA H100, A100, A10G and T4 GPUs with CUDA 12.2+. Note that you have to install NVIDIA Container Toolkit to use it.
For other NVIDIA GPUs, continuous batching will still apply, but some operations like flash attention and paged attention will not be executed.
TGI can be used on NVIDIA GPUs through its official docker image:
model=teknium/OpenHermes-2.5-Mistral-7B
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all --shm-size 64g -p 8080:80 -v $volume:/data \
ghcr.io/huggingface/text-generation-inference:3.0.2 \
--model-id $model
The launched TGI server can then be queried from clients, make sure to check out the Consuming TGI guide.