35 lines
1001 B
Python
35 lines
1001 B
Python
|
# python examples/generate.py --model='/path/models/Qwen2-7B-Instruct' --devices='npu:0'
|
||
|
|
# python generate.py --model='/path/models/Qwen2-7B-Instruct' --devices='npu:0,npu:1'
|
||
|
|
|
||
|
|
from xllm import ArgumentParser, LLM, SamplingParams
|
||
|
|
|
||
|
|
# Create an LLM.
|
||
|
|
parser = ArgumentParser()
|
||
|
|
llm = LLM(**vars(parser.parse_args()))
|
||
|
|
|
||
|
|
# Create sampling params.
|
||
|
|
sampling_params = SamplingParams(
|
||
|
|
temperature=0.8,
|
||
|
|
top_p=0.95,
|
||
|
|
max_tokens=10,
|
||
|
|
)
|
||
|
|
|
||
|
|
# Generate texts from the prompts. The output is a list of RequestOutput
|
||
|
|
# objects that contain the prompt, generated text, and other information.
|
||
|
|
prompts = [
|
||
|
|
"Hello, my name is",
|
||
|
|
"The president of the United States is",
|
||
|
|
"The capital of France is",
|
||
|
|
"The future of AI is",
|
||
|
|
]
|
||
|
|
|
||
|
|
outputs = llm.generate(prompts, sampling_params=sampling_params)
|
||
|
|
|
||
|
|
# Print the outputs.
|
||
|
|
for i, output in enumerate(outputs):
|
||
|
|
prompt = output.prompt
|
||
|
|
generated_text = output.outputs[0].text
|
||
|
|
print(f"Prompt: {prompt!r}, Generated text: {generated_text!r}")
|
||
|
|
|
||
|
|
llm.finish()
|