Apps examples with GenerationExecutor / LLM API

Python chat

chat.py provides a small examples to play around with your model. Before running, install additional requirements with pip install -r ./requirements.txt. Then you can run it with

python3 ./chat.py --model <model_dir> --tokenizer <tokenizer_path> --tp_size <tp_size>

Please run python3 ./chat.py --help for more information on the arguments.

Note that, the model_dir could accept the following formats:

1. A path to a built TRT-LLM engine
2. A path to a local HuggingFace model
3. The name of a HuggingFace model such as "TinyLlama/TinyLlama-1.1B-Chat-v1.0"

FastAPI server

NOTE: This FastAPI-based server is only an example for demonstrating the usage of TensorRT LLM LLM API. It is not intended for production use. For production, use the trtllm-serve command. The server exposes OpenAI compatible API endpoints.

Install the additional requirements

pip install -r ./requirements.txt

Start the server

Start the server with:

python3 ./fastapi_server.py <model_dir>&

Note that, the model_dir could accept same formats as in the chat example. If you are using an engine build with trtllm-build, remember to pass the tokenizer path:

python3 ./fastapi_server.py <model_dir> --tokenizer <tokenizer_dir>&

To get more information on all the arguments, please run python3 ./fastapi_server.py --help.

Send requests

You can pass request arguments like "max_tokens", "top_p", "top_k" in your JSON dict:

curl http://localhost:8000/generate -d '{"prompt": "In this example,", "max_tokens": 8}'

You can also use the streaming interface with:

curl http://localhost:8000/generate -d '{"prompt": "In this example,", "max_tokens": 8, "streaming": true}'