hf_text-generation-inference/docs/source/quicktour.md

Quick Tour

The easiest way of getting started is using the official Docker container. Install Docker following their installation instructions.

Let's say you want to deploy Falcon-7B Instruct model with TGI. Here is an example on how to do that:

model=tiiuae/falcon-7b-instruct
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:1.4 --model-id $model

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.

TGI also supports ROCm-enabled AMD GPUs (only MI210 and MI250 are tested), details are available in the Supported Hardware section and AMD documentation. To launch TGI on ROCm GPUs, please use instead:

docker run --cap-add=SYS_PTRACE --security-opt seccomp=unconfined --device=/dev/kfd --device=/dev/dri --group-add video --ipc=host --shm-size 1g -p 8080:80 -v $volume:/data ghcr.io/huggingface/text-generation-inference:1.4-rocm --model-id $model

Once TGI is running, you can use the generate endpoint by doing requests. To learn more about how to query the endpoints, check the Consuming TGI section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.

import requests

headers = {
    "Content-Type": "application/json",
}

data = {
    'inputs': 'What is Deep Learning?',
    'parameters': {
        'max_new_tokens': 20,
    },
}

response = requests.post('http://127.0.0.1:8080/generate', headers=headers, json=data)
print(response.json())
# {'generated_text': '\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can'}

async function query() {
    const response = await fetch(
        'http://127.0.0.1:8080/generate',
        {
            method: 'POST',
            headers: { 'Content-Type': 'application/json'},
            body: JSON.stringify({
                'inputs': 'What is Deep Learning?',
                'parameters': {
                    'max_new_tokens': 20
                }
            })
        }
    );
}

query().then((response) => {
    console.log(JSON.stringify(response));
});
/// {"generated_text":"\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can"}

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'

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.

docker run ghcr.io/huggingface/text-generation-inference:1.4 --help