A Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power Hugging Chat, the Inference API and Inference Endpoint.
Text Generation Inference (TGI) is a toolkit for deploying and serving Large Language Models (LLMs). TGI enables high-performance text generation for the most popular open-source LLMs, including Llama, Falcon, StarCoder, BLOOM, GPT-NeoX, and more. TGI implements many features, such as:
- Simple launcher to serve most popular LLMs
- Production ready (distributed tracing with Open Telemetry, Prometheus metrics)
- Tensor Parallelism for faster inference on multiple GPUs
- Token streaming using Server-Sent Events (SSE)
- Continuous batching of incoming requests for increased total throughput
- Optimized transformers code for inference using Flash Attention and Paged Attention on the most popular architectures
- Quantization with :
- Safetensors weight loading
- Watermarking with A Watermark for Large Language Models
- Logits warper (temperature scaling, top-p, top-k, repetition penalty, more details see transformers.LogitsProcessor)
- Stop sequences
- Log probabilities
- Speculation ~2x latency
- Guidance/JSON. Specify output format to speed up inference and make sure the output is valid according to some specs..
- Custom Prompt Generation: Easily generate text by providing custom prompts to guide the model's output
- Fine-tuning Support: Utilize fine-tuned models for specific tasks to achieve higher accuracy and performance
- Nvidia
- AMD (-rocm)
- Inferentia
- Intel GPU
- Gaudi
For a detailed starting guide, please see the Quick Tour. The easiest way of getting started is using the official Docker container:
model=HuggingFaceH4/zephyr-7b-beta
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:2.0 --model-id $modelAnd then you can make requests like
curl 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
-H 'Content-Type: application/json'Note: To use NVIDIA GPUs, you need to install the NVIDIA Container Toolkit. 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. 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:2.0-rocm --model-id $model instead of the command above.
To see all options to serve your models (in the code or in the cli):
text-generation-launcher --help
You can consult the OpenAPI documentation of the text-generation-inference REST API using the /docs route.
The Swagger UI is also available at: https://huggingface.github.io/text-generation-inference.
You have the option to utilize the HUGGING_FACE_HUB_TOKEN environment variable for configuring the token employed by
text-generation-inference. This allows you to gain access to protected resources.
For example, if you want to serve the gated Llama V2 model variants:
- Go to https://huggingface.co/settings/tokens
- Copy your cli READ token
- Export
HUGGING_FACE_HUB_TOKEN=<your cli READ token>
or with Docker:
model=meta-llama/Llama-2-7b-chat-hf
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
token=<your cli READ token>
docker run --gpus all --shm-size 1g -e HUGGING_FACE_HUB_TOKEN=$token -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:2.0 --model-id $modelNCCL is a communication framework used by
PyTorch to do distributed training/inference. text-generation-inference make
use of NCCL to enable Tensor Parallelism to dramatically speed up inference for large language models.
In order to share data between the different devices of a NCCL group, NCCL might fall back to using the host memory if
peer-to-peer using NVLink or PCI is not possible.
To allow the container to use 1G of Shared Memory and support SHM sharing, we add --shm-size 1g on the above command.
If you are running text-generation-inference inside Kubernetes. You can also add Shared Memory to the container by
creating a volume with:
- name: shm
emptyDir:
medium: Memory
sizeLimit: 1Giand mounting it to /dev/shm.
Finally, you can also disable SHM sharing by using the NCCL_SHM_DISABLE=1 environment variable. However, note that
this will impact performance.
text-generation-inference is instrumented with distributed tracing using OpenTelemetry. You can use this feature
by setting the address to an OTLP collector with the --otlp-endpoint argument.
You can also opt to install text-generation-inference locally.
First install Rust and create a Python virtual environment with at least
Python 3.9, e.g. using conda:
curl --proto '=https' --tlsv1.2 -sSf https://sh.rustup.rs | sh
conda create -n text-generation-inference python=3.11
conda activate text-generation-inferenceYou may also need to install Protoc.
On Linux:
PROTOC_ZIP=protoc-21.12-linux-x86_64.zip
curl -OL https://github.com/protocolbuffers/protobuf/releases/download/v21.12/$PROTOC_ZIP
sudo unzip -o $PROTOC_ZIP -d /usr/local bin/protoc
sudo unzip -o $PROTOC_ZIP -d /usr/local 'include/*'
rm -f $PROTOC_ZIPOn MacOS, using Homebrew:
brew install protobufThen run:
BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2Note: on some machines, you may also need the OpenSSL libraries and gcc. On Linux machines, run:
sudo apt-get install libssl-dev gcc -yTGI works out of the box to serve optimized models for all modern models. They can be found in this list.
Other architectures are supported on a best-effort basis using:
AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")
or
AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
text-generation-launcher --model-id mistralai/Mistral-7B-Instruct-v0.2 --quantize4bit quantization is available using the NF4 and FP4 data types from bitsandbytes. It can be enabled by providing --quantize bitsandbytes-nf4 or --quantize bitsandbytes-fp4 as a command line argument to text-generation-launcher.
make server-dev
make router-dev# python
make python-server-tests
make python-client-tests
# or both server and client tests
make python-tests
# rust cargo tests
make rust-tests
# integration tests
make integration-tests
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