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vllm.model_executor.models.musicflamingo

MusicFlamingo model adapter.

MusicFlamingo shares the AudioFlamingo3 architecture, so we reuse the same implementation and multimodal processor, while accepting MusicFlamingo config and processor classes when available.

MusicFlamingoDummyInputsBuilder

Bases: AudioFlamingo3DummyInputsBuilder

Source code in vllm/model_executor/models/musicflamingo.py 
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class MusicFlamingoDummyInputsBuilder(AudioFlamingo3DummyInputsBuilder):
    pass

MusicFlamingoForConditionalGeneration

Bases: AudioFlamingo3ForConditionalGeneration

MusicFlamingo model for conditional generation.

Source code in vllm/model_executor/models/musicflamingo.py 
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@MULTIMODAL_REGISTRY.register_processor(
    AudioFlamingo3MultiModalProcessor,
    info=MusicFlamingoProcessingInfo,
    dummy_inputs=MusicFlamingoDummyInputsBuilder,
)
class MusicFlamingoForConditionalGeneration(AudioFlamingo3ForConditionalGeneration):
    """MusicFlamingo model for conditional generation."""

MusicFlamingoProcessingInfo

Bases: BaseProcessingInfo

Source code in vllm/model_executor/models/musicflamingo.py 
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class MusicFlamingoProcessingInfo(BaseProcessingInfo):
    def get_hf_config(self):
        if MusicFlamingoConfig is None:
            return self.ctx.get_hf_config(AudioFlamingo3Config)
        return self.ctx.get_hf_config((MusicFlamingoConfig, AudioFlamingo3Config))

    def get_hf_processor(self, **kwargs: object):
        if MusicFlamingoProcessor is None:
            return self.ctx.get_hf_processor(AudioFlamingo3Processor, **kwargs)
        # Tuple triggers AutoProcessor path and accepts either processor class.
        return self.ctx.get_hf_processor(
            (MusicFlamingoProcessor, AudioFlamingo3Processor), **kwargs
        )

    def get_feature_extractor(self, **kwargs: object):
        hf_processor = self.get_hf_processor(**kwargs)
        return hf_processor.feature_extractor

    def get_supported_mm_limits(self) -> Mapping[str, int | None]:
        return {"audio": None}

get_feature_extractor 

get_feature_extractor(**kwargs: object)
Source code in vllm/model_executor/models/musicflamingo.py 
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def get_feature_extractor(self, **kwargs: object):
    hf_processor = self.get_hf_processor(**kwargs)
    return hf_processor.feature_extractor

get_hf_config 

get_hf_config()
Source code in vllm/model_executor/models/musicflamingo.py 
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def get_hf_config(self):
    if MusicFlamingoConfig is None:
        return self.ctx.get_hf_config(AudioFlamingo3Config)
    return self.ctx.get_hf_config((MusicFlamingoConfig, AudioFlamingo3Config))

get_hf_processor 

get_hf_processor(**kwargs: object)
Source code in vllm/model_executor/models/musicflamingo.py 
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def get_hf_processor(self, **kwargs: object):
    if MusicFlamingoProcessor is None:
        return self.ctx.get_hf_processor(AudioFlamingo3Processor, **kwargs)
    # Tuple triggers AutoProcessor path and accepts either processor class.
    return self.ctx.get_hf_processor(
        (MusicFlamingoProcessor, AudioFlamingo3Processor), **kwargs
    )

get_supported_mm_limits 

get_supported_mm_limits() -> Mapping[str, int | None]
Source code in vllm/model_executor/models/musicflamingo.py 
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def get_supported_mm_limits(self) -> Mapping[str, int | None]:
    return {"audio": None}