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### :section Basics
### :title Generate text in streaming
### :order 2
import asyncio
from tensorrt_llm import LLM, SamplingParams
def main():
# model could accept HF model name or a path to local HF model.
llm = LLM(model="TinyLlama/TinyLlama-1.1B-Chat-v1.0")
# Sample prompts.
prompts = [
"Hello, my name is",
"The capital of France is",
"The future of AI is",
]
# Create a sampling params.
sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
# Async based on Python coroutines
async def task(id: int, prompt: str):
# streaming=True is used to enable streaming generation.
async for output in llm.generate_async(prompt,
sampling_params,
streaming=True):
print(f"Generation for prompt-{id}: {output.outputs[0].text!r}")
async def main():
tasks = [task(id, prompt) for id, prompt in enumerate(prompts)]
await asyncio.gather(*tasks)
asyncio.run(main())
# Got output like follows:
# Generation for prompt-0: '\n'
# Generation for prompt-3: 'an'
# Generation for prompt-2: 'Paris'
# Generation for prompt-1: 'likely'
# Generation for prompt-0: '\n\n'
# Generation for prompt-3: 'an exc'
# Generation for prompt-2: 'Paris.'
# Generation for prompt-1: 'likely to'
# Generation for prompt-0: '\n\nJ'
# Generation for prompt-3: 'an exciting'
# Generation for prompt-2: 'Paris.'
# Generation for prompt-1: 'likely to nomin'
# Generation for prompt-0: '\n\nJane'
# Generation for prompt-3: 'an exciting time'
# Generation for prompt-1: 'likely to nominate'
# Generation for prompt-0: '\n\nJane Smith'
# Generation for prompt-3: 'an exciting time for'
# Generation for prompt-1: 'likely to nominate a'
# Generation for prompt-0: '\n\nJane Smith.'
# Generation for prompt-3: 'an exciting time for us'
# Generation for prompt-1: 'likely to nominate a new'
if __name__ == '__main__':
main()