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](https://huggingface.co/docs/text-generation-inference/supported_models). TGI implements many features, such as:
- Serve the most popular Large Language Models with a simple launcher
- 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](https://github.com/huggingface/text-generation-inference/tree/main/router) for increased total throughput
- Optimized transformers code for inference using [flash-attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
- Continuous batching of incoming requests for increased total throughput
- Optimized transformers code for inference using [Flash Attention](https://github.com/HazyResearch/flash-attention) and [Paged Attention](https://github.com/vllm-project/vllm) on the most popular architectures
- Quantization with [bitsandbytes](https://github.com/TimDettmers/bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323)
The easiest way of getting started is using the official Docker container:
For a detailed starting guide, please see the [Quick Tour](https://huggingface.co/docs/text-generation-inference/quicktour). The easiest way of getting started is using the official Docker container:
```shell
model=tiiuae/falcon-7b-instruct
...
...
@@ -90,46 +62,21 @@ volume=$PWD/data # share a volume with the Docker container to avoid downloading
docker run --gpus all --shm-size 1g -p 8080:80 -v$volume:/data ghcr.io/huggingface/text-generation-inference:1.1.0 --model-id$model
```
**Note:** To use 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 11.8 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.
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):
```
text-generation-launcher --help
```
You can then query the model using either the `/generate` or `/generate_stream` routes:
And then you can make requests like
```shell
```bash
curl 127.0.0.1:8080/generate \
-X POST \
-d'{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}'\
-H'Content-Type: application/json'
```
```shell
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'
```
or from Python:
**Note:** To use 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 11.8 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.
```shell
pip install text-generation
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):
```
```python
fromtext_generationimportClient
client=Client("http://127.0.0.1:8080")
print(client.generate("What is Deep Learning?",max_new_tokens=20).generated_text)
text=""
forresponseinclient.generate_stream("What is Deep Learning?",max_new_tokens=20):
ifnotresponse.token.special:
text+=response.token.text
print(text)
text-generation-launcher --help
```
### API documentation
...
...
@@ -241,6 +188,20 @@ the kernels by using the `DISABLE_CUSTOM_KERNELS=True` environment variable.
Be aware that the official Docker image has them enabled by default.
## Optimized architectures
TGI works out of the box to serve optimized models in [this list](https://huggingface.co/docs/text-generation-inference/supported_models).
Other architectures are supported on a best-effort basis using:
TGI is also supported on the following AI hardware accelerators:
-*Habana first-gen Gaudi and Gaudi2:* checkout [here](https://github.com/huggingface/optimum-habana/tree/main/text-generation-inference) how to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index)
@@ -45,4 +45,3 @@ TGI is also supported on the following AI hardware accelerators:
-*Habana first-gen Gaudi and Gaudi2:* check out this [example](https://github.com/huggingface/optimum-habana/tree/main/text-generation-inference) how to serve models with TGI on Gaudi and Gaudi2 with [Optimum Habana](https://huggingface.co/docs/optimum/habana/index)
