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FEAT: [model] support jina-reranker-v3 #4156

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merged 2 commits into from
Oct 22, 2025
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FEAT: [model] support jina-reranker-v3 #4156

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4a77549
feat(reranker): sup jina-reranker-v3
llyycchhee Oct 21, 2025
f1efa35
Merge remote-tracking branch 'origin/main' into feat-sup-jina-reranke…
llyycchhee Oct 22, 2025
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8 changes: 7 additions & 1 deletion doc/source/models/model_abilities/embed.rst
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Expand Up @@ -123,4 +123,10 @@ Does Embeddings API provides integration method for LangChain?
-----------------------------------------------------------------------------------

Yes, you can refer to the related sections in LangChain's respective official Xinference documentation.
Here is the link: `Text Embedding Models: Xinference <https://python.langchain.com/docs/integrations/text_embedding/xinference>`_
Here is the link: `Text Embedding Models: Xinference <https://python.langchain.com/docs/integrations/text_embedding/xinference>`_


Does Embeddings API support hrbrid model?
-----------------------------------------------------------------------------------

Yes, you can use ``flag`` as the engine to deploy the model and call Embeddings API by setting the extra parameter ``return_parse=True`` which will return sparse vectors.
12 changes: 10 additions & 2 deletions xinference/model/rerank/sentence_transformers/core.py
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Expand Up @@ -81,6 +81,7 @@ def load(self):
if (
self.model_family.type == "normal"
and "qwen3" not in self.model_family.model_name.lower()
and "jina-reranker-v3" not in self.model_family.model_name.lower()
):
try:
import sentence_transformers
Expand Down Expand Up @@ -109,7 +110,10 @@ def load(self):
)
if self._use_fp16:
self._model.model.half()
elif "qwen3" in self.model_family.model_name.lower():
elif (
"qwen3" in self.model_family.model_name.lower()
or "jina-reranker-v3" in self.model_family.model_name.lower()
):
# qwen3-reranker
# now we use transformers
# TODO: support engines for rerank models
Expand Down Expand Up @@ -225,6 +229,7 @@ def rerank(
if (
self.model_family.type == "normal"
and "qwen3" not in self.model_family.model_name.lower()
and "jina-reranker-v3" not in self.model_family.model_name.lower()
):
logger.debug("Passing processed sentences: %s", sentence_combinations)
similarity_scores = self._model.predict(
Expand All @@ -235,7 +240,10 @@ def rerank(
).cpu()
if similarity_scores.dtype == torch.bfloat16:
similarity_scores = similarity_scores.float()
elif "qwen3" in self.model_family.model_name.lower():
elif (
"qwen3" in self.model_family.model_name.lower()
or "jina-reranker-v3" in self.model_family.model_name.lower()
):

def format_instruction(instruction, query, doc):
if instruction is None:
Expand Down
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