From f01862c0c02bb6617c80e805ad94e5ab1c03116e Mon Sep 17 00:00:00 2001 From: Nicolas Patry Date: Thu, 30 Jan 2025 20:49:08 +0100 Subject: [PATCH] Prepare for release 3.1.0 --- Cargo.lock | 14 +++++++------- Cargo.toml | 2 +- README.md | 6 +++--- docs/openapi.json | 2 +- docs/source/basic_tutorials/gated_model_access.md | 2 +- docs/source/conceptual/quantization.md | 6 +++--- docs/source/installation_amd.md | 2 +- docs/source/installation_intel.md | 4 ++-- docs/source/installation_nvidia.md | 2 +- docs/source/quicktour.md | 4 ++-- docs/source/reference/api_reference.md | 2 +- 11 files changed, 23 insertions(+), 23 deletions(-) diff --git a/Cargo.lock b/Cargo.lock index af3e1902..d6883f9d 100644 --- a/Cargo.lock +++ b/Cargo.lock @@ -4423,7 +4423,7 @@ dependencies = [ [[package]] name = "text-generation-backends-trtllm" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "async-trait", "clap 4.5.21", @@ -4444,7 +4444,7 @@ dependencies = [ [[package]] name = "text-generation-benchmark" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "average", "clap 4.5.21", @@ -4464,7 +4464,7 @@ dependencies = [ [[package]] name = "text-generation-client" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "async-trait", "base64 0.22.1", @@ -4482,7 +4482,7 @@ dependencies = [ [[package]] name = "text-generation-launcher" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "clap 4.5.21", "ctrlc", @@ -4503,7 +4503,7 @@ dependencies = [ [[package]] name = "text-generation-router" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "anyhow", "async-stream", @@ -4554,7 +4554,7 @@ dependencies = [ [[package]] name = "text-generation-router-v2" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "async-stream", "async-trait", @@ -4603,7 +4603,7 @@ dependencies = [ [[package]] name = "text-generation-router-v3" -version = "3.0.2-dev0" +version = "3.1.1-dev0" dependencies = [ "async-stream", "async-trait", diff --git a/Cargo.toml b/Cargo.toml index 9f49c9ab..6fd4b51d 100644 --- a/Cargo.toml +++ b/Cargo.toml @@ -20,7 +20,7 @@ default-members = [ resolver = "2" [workspace.package] -version = "3.0.2-dev0" +version = "3.1.1-dev0" edition = "2021" authors = ["Olivier Dehaene"] homepage = "https://github.com/huggingface/text-generation-inference" diff --git a/README.md b/README.md index 6072c9bd..e4c4486f 100644 --- a/README.md +++ b/README.md @@ -84,7 +84,7 @@ model=HuggingFaceH4/zephyr-7b-beta volume=$PWD/data docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model + ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id $model ``` And then you can make requests like @@ -121,7 +121,7 @@ curl localhost:8080/v1/chat/completions \ **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. -**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/installation_amd#using-tgi-with-amd-gpus). 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:3.0.2-rocm --model-id $model` instead of the command above. +**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/installation_amd#using-tgi-with-amd-gpus). 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:3.1.0-rocm --model-id $model` instead of the command above. To see all options to serve your models (in the [code](https://github.com/huggingface/text-generation-inference/blob/main/launcher/src/main.rs) or in the cli): ``` @@ -152,7 +152,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading token= docker run --gpus all --shm-size 1g -e HF_TOKEN=$token -p 8080:80 -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model + ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id $model ``` ### A note on Shared Memory (shm) diff --git a/docs/openapi.json b/docs/openapi.json index 48120f77..1f1419a9 100644 --- a/docs/openapi.json +++ b/docs/openapi.json @@ -10,7 +10,7 @@ "name": "Apache 2.0", "url": "https://www.apache.org/licenses/LICENSE-2.0" }, - "version": "3.0.2-dev0" + "version": "3.1.0-dev0" }, "paths": { "/": { diff --git a/docs/source/basic_tutorials/gated_model_access.md b/docs/source/basic_tutorials/gated_model_access.md index 6520a6ab..949aab56 100644 --- a/docs/source/basic_tutorials/gated_model_access.md +++ b/docs/source/basic_tutorials/gated_model_access.md @@ -19,6 +19,6 @@ docker run --gpus all \ --shm-size 1g \ -e HF_TOKEN=$token \ -p 8080:80 \ - -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 \ + -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.1.0 \ --model-id $model ``` diff --git a/docs/source/conceptual/quantization.md b/docs/source/conceptual/quantization.md index a0eb0c51..449cc79b 100644 --- a/docs/source/conceptual/quantization.md +++ b/docs/source/conceptual/quantization.md @@ -19,7 +19,7 @@ bitsandbytes is a library used to apply 8-bit and 4-bit quantization to models. In TGI, you can use 8-bit quantization by adding `--quantize bitsandbytes` like below 👇 ```bash -docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize bitsandbytes +docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id $model --quantize bitsandbytes ``` 4-bit quantization is also possible with bitsandbytes. You can choose one of the following 4-bit data types: 4-bit float (`fp4`), or 4-bit `NormalFloat` (`nf4`). These data types were introduced in the context of parameter-efficient fine-tuning, but you can apply them for inference by automatically converting the model weights on load. @@ -27,7 +27,7 @@ docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingf In TGI, you can use 4-bit quantization by adding `--quantize bitsandbytes-nf4` or `--quantize bitsandbytes-fp4` like below 👇 ```bash -docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize bitsandbytes-nf4 +docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id $model --quantize bitsandbytes-nf4 ``` You can get more information about 8-bit quantization by reading this [blog post](https://huggingface.co/blog/hf-bitsandbytes-integration), and 4-bit quantization by reading [this blog post](https://huggingface.co/blog/4bit-transformers-bitsandbytes). @@ -48,7 +48,7 @@ $$({\hat{W}_{l}}^{*} = argmin_{\hat{W_{l}}} ||W_{l}X-\hat{W}_{l}X||^{2}_{2})$$ TGI allows you to both run an already GPTQ quantized model (see available models [here](https://huggingface.co/models?search=gptq)) or quantize a model of your choice using quantization script. You can run a quantized model by simply passing --quantize like below 👇 ```bash -docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.0.2 --model-id $model --quantize gptq +docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:3.1.0 --model-id $model --quantize gptq ``` Note that TGI's GPTQ implementation doesn't use [AutoGPTQ](https://github.com/PanQiWei/AutoGPTQ) under the hood. However, models quantized using AutoGPTQ or Optimum can still be served by TGI. diff --git a/docs/source/installation_amd.md b/docs/source/installation_amd.md index 797f0a5d..100bc2a9 100644 --- a/docs/source/installation_amd.md +++ b/docs/source/installation_amd.md @@ -11,7 +11,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading docker run --rm -it --cap-add=SYS_PTRACE --security-opt seccomp=unconfined \ --device=/dev/kfd --device=/dev/dri --group-add video \ --ipc=host --shm-size 256g --net host -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2-rocm \ + ghcr.io/huggingface/text-generation-inference:3.1.0-rocm \ --model-id $model ``` diff --git a/docs/source/installation_intel.md b/docs/source/installation_intel.md index 165d1105..b2279bb4 100644 --- a/docs/source/installation_intel.md +++ b/docs/source/installation_intel.md @@ -12,7 +12,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading docker run --rm --privileged --cap-add=sys_nice \ --device=/dev/dri \ --ipc=host --shm-size 1g --net host -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2-intel-xpu \ + ghcr.io/huggingface/text-generation-inference:3.1.0-intel-xpu \ --model-id $model --cuda-graphs 0 ``` @@ -29,7 +29,7 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading docker run --rm --privileged --cap-add=sys_nice \ --device=/dev/dri \ --ipc=host --shm-size 1g --net host -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2-intel-cpu \ + ghcr.io/huggingface/text-generation-inference:3.1.0-intel-cpu \ --model-id $model --cuda-graphs 0 ``` diff --git a/docs/source/installation_nvidia.md b/docs/source/installation_nvidia.md index 0092f5ef..8c4bdaee 100644 --- a/docs/source/installation_nvidia.md +++ b/docs/source/installation_nvidia.md @@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B volume=$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 \ + ghcr.io/huggingface/text-generation-inference:3.1.0 \ --model-id $model ``` diff --git a/docs/source/quicktour.md b/docs/source/quicktour.md index 4ffb9728..bd4956a0 100644 --- a/docs/source/quicktour.md +++ b/docs/source/quicktour.md @@ -11,7 +11,7 @@ model=teknium/OpenHermes-2.5-Mistral-7B volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \ - ghcr.io/huggingface/text-generation-inference:3.0.2 \ + ghcr.io/huggingface/text-generation-inference:3.1.0 \ --model-id $model ``` @@ -96,7 +96,7 @@ curl 127.0.0.1:8080/generate \ 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. ```bash -docker run ghcr.io/huggingface/text-generation-inference:3.0.2 --help +docker run ghcr.io/huggingface/text-generation-inference:3.1.0 --help ``` diff --git a/docs/source/reference/api_reference.md b/docs/source/reference/api_reference.md index f7723187..ee34d587 100644 --- a/docs/source/reference/api_reference.md +++ b/docs/source/reference/api_reference.md @@ -163,7 +163,7 @@ hub = { # create Hugging Face Model Class huggingface_model = HuggingFaceModel( - image_uri=get_huggingface_llm_image_uri("huggingface",version="3.0.2"), + image_uri=get_huggingface_llm_image_uri("huggingface",version="3.1.0"), env=hub, role=role, )