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Signed-off-by: DarkLight1337 <tlleungac@connect.ust.hk> Signed-off-by: Harry Mellor <19981378+hmellor@users.noreply.github.com> Co-authored-by: Harry Mellor <19981378+hmellor@users.noreply.github.com>
48 lines
1.2 KiB
Python
48 lines
1.2 KiB
Python
# SPDX-License-Identifier: Apache-2.0
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# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
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from argparse import Namespace
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from vllm import LLM, EngineArgs
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from vllm.utils import FlexibleArgumentParser
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def parse_args():
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parser = FlexibleArgumentParser()
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parser = EngineArgs.add_cli_args(parser)
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# Set example specific arguments
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parser.set_defaults(
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model="BAAI/bge-reranker-v2-m3",
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runner="pooling",
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enforce_eager=True,
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)
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return parser.parse_args()
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def main(args: Namespace):
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# Sample prompts.
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text_1 = "What is the capital of France?"
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texts_2 = [
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"The capital of Brazil is Brasilia.",
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"The capital of France is Paris.",
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]
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# Create an LLM.
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# You should pass runner="pooling" for cross-encoder models
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llm = LLM(**vars(args))
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# Generate scores. The output is a list of ScoringRequestOutputs.
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outputs = llm.score(text_1, texts_2)
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# Print the outputs.
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print("\nGenerated Outputs:\n" + "-" * 60)
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for text_2, output in zip(texts_2, outputs):
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score = output.outputs.score
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print(f"Pair: {[text_1, text_2]!r} \nScore: {score}")
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print("-" * 60)
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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