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71b1c8b667
Signed-off-by: Yeshwanth Surya <yeshsurya@gmail.com> Signed-off-by: Yeshwanth N <yeshsurya@gmail.com> Signed-off-by: yeshsurya <yeshsurya@gmail.com>
57 lines
1.5 KiB
Python
57 lines
1.5 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.argparse_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-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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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create an LLM.
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# You should pass runner="pooling" for embedding models
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llm = LLM(**vars(args))
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# Generate embedding. The output is a list of EmbeddingRequestOutputs.
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outputs = llm.embed(prompts)
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# Print the outputs.
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print("\nGenerated Outputs:\n" + "-" * 60)
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for prompt, output in zip(prompts, outputs):
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embeds = output.outputs.embedding
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print(len(embeds))
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# Generate embedding for each token. The output is a list of PoolingRequestOutput.
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outputs = llm.encode(prompts, pooling_task="token_embed")
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# Print the outputs.
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print("\nGenerated Outputs:\n" + "-" * 60)
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for prompt, output in zip(prompts, outputs):
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multi_vector = output.outputs.data
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print(multi_vector.shape)
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if __name__ == "__main__":
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args = parse_args()
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main(args)
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