text-generation-inference/examples/README.md

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# TGI-Gaudi example
This example provide a simple way of usage of `tgi-gaudi` with continuous batching. It uses a small dataset [DIBT/10k_prompts_ranked](https://huggingface.co/datasets/DIBT/10k_prompts_ranked) and present basic performance numbers.
## Get started
### Install
```
pip install -r requirements.txt
```
### Setup TGI server
More details on runing the TGI server available [here](https://github.com/huggingface/tgi-gaudi/blob/habana-main/README.md#running-tgi-on-gaudi).
### Run benchmark
To run benchmark use below command:
```
python run_generation --model_id MODEL_ID
```
where `MODEL_ID` should be set to the same value as in the TGI server instance.
> For gated models such as [LLama](https://huggingface.co/meta-llama) or [StarCoder](https://huggingface.co/bigcode/starcoder), you will have to set environment variable `HUGGING_FACE_HUB_TOKEN=<token>` with a valid Hugging Face Hub read token.
All possible parameters are described in the below table:
<div align="left">
| Name | Default value | Description |
| ------------------------- | :---------------------------- | :------------------------------------------------------------ |
| SERVER_ADDRESS | http://localhost:8080 | The address and port at which the TGI server is available. |
| MODEL_ID | meta-llama/Llama-2-7b-chat-hf | Model ID used in the TGI server instance. |
| MAX_INPUT_LENGTH | 1024 | Maximum input length supported by the TGI server. |
| MAX_OUTPUT_LENGTH | 1024 | Maximum output length supported by the TGI server. |
| TOTAL_SAMPLE_COUNT | 2048 | Number of samples to run. |
| MAX_CONCURRENT_REQUESTS | 256 | The number of requests sent simultaneously to the TGI server. |
</div>