95 lines
3.0 KiB
Markdown
95 lines
3.0 KiB
Markdown
# Quick Tour
|
|
|
|
The easiest way of getting started is using the official Docker container. Install Docker following [their installation instructions](https://docs.docker.com/get-docker/).
|
|
|
|
## Launching TGI
|
|
|
|
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:
|
|
|
|
```bash
|
|
model=teknium/OpenHermes-2.5-Mistral-7B
|
|
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:2.2.0 \
|
|
--model-id $model
|
|
```
|
|
|
|
### Supported hardware
|
|
|
|
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.
|
|
|
|
## Consuming TGI
|
|
|
|
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.
|
|
|
|
<inferencesnippet>
|
|
<python>
|
|
|
|
```python
|
|
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'}
|
|
```
|
|
</python>
|
|
<js>
|
|
|
|
```js
|
|
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"}
|
|
```
|
|
|
|
</js>
|
|
<curl>
|
|
|
|
```curl
|
|
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'
|
|
```
|
|
|
|
</curl>
|
|
</inferencesnippet>
|
|
|
|
<Tip>
|
|
|
|
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.
|
|
|
|
```bash
|
|
docker run ghcr.io/huggingface/text-generation-inference:2.2.0 --help
|
|
```
|
|
|
|
</Tip>
|