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Qwen3-Embedding-0.6B

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Hello!

This pull request has been automatically generated from the export_static_quantized_openvino_model function from the Sentence Transformers library.

Config

OVQuantizationConfig(
    quant_method=<OVQuantizationMethod.DEFAULT: 'default'>
)

Tip:

Consider testing this pull request before merging by loading the model from this PR with the revision argument:

from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
    "Qwen/Qwen3-Embedding-0.6B",
    revision=f"refs/pr/{pr_number}",
    backend="openvino",
    model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)

Hello!

This pull request has been automatically generated from the export_static_quantized_openvino_model function from the Sentence Transformers library.

Config

OVQuantizationConfig(
    quant_method=<OVQuantizationMethod.DEFAULT: 'default'>
)

Tip:

Consider testing this pull request before merging by loading the model from this PR with the revision argument:

from sentence_transformers import SentenceTransformer

# TODO: Fill in the PR number
pr_number = 2
model = SentenceTransformer(
    "Qwen/Qwen3-Embedding-0.6B",
    revision=f"refs/pr/{pr_number}",
    backend="openvino",
    model_kwargs={"file_name": "openvino_model_qint8_quantized.xml"},
)

# Verify that everything works as expected
embeddings = model.encode(["The weather is lovely today.", "It's so sunny outside!", "He drove to the stadium."])
print(embeddings.shape)

similarities = model.similarity(embeddings, embeddings)
print(similarities)

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