6d7cb14a24
### What this PR does / why we need it? Fix lint ### Does this PR introduce _any_ user-facing change? No ### How was this patch tested? CI passed Signed-off-by: Yikun Jiang <yikunkero@gmail.com>
54 lines
2.0 KiB
Python
54 lines
2.0 KiB
Python
#
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# Copyright 2023 The vLLM team.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# This file is a part of the vllm-ascend project.
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# Adapted from https://www.modelscope.cn/models/Qwen/Qwen3-Embedding-0.6B
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#
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import os
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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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import torch
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from vllm import LLM
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def get_detailed_instruct(task_description: str, query: str) -> str:
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return f'Instruct: {task_description}\nQuery:{query}'
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# Each query must come with a one-sentence instruction that describes the task
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task = 'Given a web search query, retrieve relevant passages that answer the query'
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queries = [
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get_detailed_instruct(task, 'What is the capital of China?'),
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get_detailed_instruct(task, 'Explain gravity')
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]
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# No need to add instruction for retrieval documents
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documents = [
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"The capital of China is Beijing.",
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"Gravity is a force that attracts two bodies towards each other. It gives weight to physical objects and is responsible for the movement of planets around the sun."
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]
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input_texts = queries + documents
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model = LLM(model="Qwen/Qwen3-Embedding-0.6B", task="embed")
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outputs = model.embed(input_texts)
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embeddings = torch.tensor([o.outputs.embedding for o in outputs])
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# Calculate the similarity scores between the first two queries and the last two documents
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scores = (embeddings[:2] @ embeddings[2:].T)
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print(scores.tolist())
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# [[0.7620252966880798, 0.14078938961029053], [0.1358368694782257, 0.6013815999031067]]
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