Generate text asynchronously#
Source NVIDIA/TensorRT-LLM.
1import asyncio
2
3from tensorrt_llm import LLM, SamplingParams
4
5
6def main():
7 # model could accept HF model name or a path to local HF model.
8 llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
9
10 # Sample prompts.
11 prompts = [
12 "Hello, my name is",
13 "The president of the United States is",
14 "The capital of France is",
15 "The future of AI is",
16 ]
17
18 # Create a sampling params.
19 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
20
21 # Async based on Python coroutines
22 async def task(prompt: str):
23 output = await llm.generate_async(prompt, sampling_params)
24 print(
25 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
26 )
27
28 async def main():
29 tasks = [task(prompt) for prompt in prompts]
30 await asyncio.gather(*tasks)
31
32 asyncio.run(main())
33
34 # Got output like follows:
35 # Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming'
36 # Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the'
37 # Prompt: 'The capital of France is', Generated text: 'Paris.'
38 # Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are'
39
40
41if __name__ == '__main__':
42 main()