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rust-toolchain.toml |
README.md
A Rust, Python and gRPC server for text generation inference. Used in production at HuggingFace to power LLMs api-inference widgets.
Table of contents
Features
- Token streaming using Server Side Events (SSE)
- Dynamic batching of incoming requests for increased total throughput
- Quantization with bitsandbytes
- Safetensors weight loading
- 45ms per token generation for BLOOM with 8xA100 80GB
- Logits warpers (temperature scaling, topk, repetition penalty ...)
- Stop sequences
- Log probabilities
Officially supported models
- BLOOM
- BLOOMZ
- MT0-XXL
Galactica(deactivated)- SantaCoder
- GPT-Neox 20B: use
--revision pr/13
Other models are supported on a best effort basis using:
AutoModelForCausalLM.from_pretrained(<model>, device_map="auto")
or
AutoModelForSeq2SeqLM.from_pretrained(<model>, device_map="auto")
Get started
Docker
The easiest way of getting started is using the official Docker container:
model=bigscience/bloom-560m
num_shard=2
volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
docker run --gpus all -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:latest --model-id $model --num-shard $num_shard
You can then query the model using either the /generate
or /generate_stream
routes:
curl 127.0.0.1:8080/generate \
-X POST \
-d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
-H 'Content-Type: application/json'
curl 127.0.0.1:8080/generate_stream \
-X POST \
-d '{"inputs":"Testing API","parameters":{"max_new_tokens":9}}' \
-H 'Content-Type: application/json'
To use GPUs, you will need to install the NVIDIA Container Toolkit.
API documentation
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.
Local install
You can also opt to install text-generation-inference
locally. You will need to have cargo and Python installed on your
machine
BUILD_EXTENSIONS=True make install # Install repository and HF/transformer fork with CUDA kernels
make run-bloom-560m
CUDA Kernels
The custom CUDA kernels are only tested on NVIDIA A100s. If you have any installation or runtime issues, you can remove
the kernels by using the BUILD_EXTENSIONS=False
environment variable.
Be aware that the official Docker image has them enabled by default.
Run BLOOM
Download
First you need to download the weights:
make download-bloom
Run
make run-bloom # Requires 8xA100 80GB
Quantization
You can also quantize the weights with bitsandbytes to reduce the VRAM requirement:
make run-bloom-quantize # Requires 8xA100 40GB
Develop
make server-dev
make router-dev
Testing
make python-tests
make integration-tests