95 lines
3.0 KiB
Markdown
95 lines
3.0 KiB
Markdown
# Quick Tour
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The easiest way of getting started is using the official Docker container. Install Docker following [their installation instructions](https://docs.docker.com/get-docker/).
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## Launching TGI
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Let's say you want to deploy [teknium/OpenHermes-2.5-Mistral-7B](https://huggingface.co/teknium/OpenHermes-2.5-Mistral-7B) model with TGI on an Nvidia GPU. Here is an example on how to do that:
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```bash
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model=teknium/OpenHermes-2.5-Mistral-7B
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volume=$PWD/data # share a volume with the Docker container to avoid downloading weights every run
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docker run --gpus all --shm-size 1g -p 8080:80 -v $volume:/data \
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ghcr.io/huggingface/text-generation-inference:2.1.1 \
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--model-id $model
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```
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### Supported hardware
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TGI supports various hardware. Make sure to check the [Using TGI with Nvidia GPUs](./installation_nvidia), [Using TGI with AMD GPUs](./installation_amd), [Using TGI with Intel GPUs](./installation_intel), [Using TGI with Gaudi](./installation_gaudi), [Using TGI with Inferentia](./installation_inferentia) guides depending on which hardware you would like to deploy TGI on.
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## Consuming TGI
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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](./basic_tutorials/consuming_tgi) section, where we show examples with utility libraries and UIs. Below you can see a simple snippet to query the endpoint.
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<inferencesnippet>
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<python>
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```python
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import requests
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headers = {
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"Content-Type": "application/json",
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}
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data = {
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'inputs': 'What is Deep Learning?',
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'parameters': {
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'max_new_tokens': 20,
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},
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}
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response = requests.post('http://127.0.0.1:8080/generate', headers=headers, json=data)
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print(response.json())
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# {'generated_text': '\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can'}
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```
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</python>
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<js>
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```js
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async function query() {
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const response = await fetch(
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'http://127.0.0.1:8080/generate',
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{
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method: 'POST',
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headers: { 'Content-Type': 'application/json'},
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body: JSON.stringify({
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'inputs': 'What is Deep Learning?',
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'parameters': {
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'max_new_tokens': 20
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}
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})
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}
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);
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}
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query().then((response) => {
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console.log(JSON.stringify(response));
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});
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/// {"generated_text":"\n\nDeep Learning is a subset of Machine Learning that is concerned with the development of algorithms that can"}
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```
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</js>
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<curl>
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```curl
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curl 127.0.0.1:8080/generate \
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-X POST \
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-d '{"inputs":"What is Deep Learning?","parameters":{"max_new_tokens":20}}' \
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-H 'Content-Type: application/json'
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```
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</curl>
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</inferencesnippet>
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<Tip>
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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.
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```bash
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docker run ghcr.io/huggingface/text-generation-inference:2.1.1 --help
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```
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</Tip>
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