| from typing import List, Union | |
| from sglang.srt.models.mllama import MllamaForConditionalGeneration | |
| from sglang.srt.multimodal.processors.base_processor import ( | |
| BaseMultimodalProcessor, | |
| MultimodalSpecialTokens, | |
| ) | |
| class MllamaImageProcessor(BaseMultimodalProcessor): | |
| models = [MllamaForConditionalGeneration] | |
| def __init__(self, hf_config, server_args, _processor, *args, **kwargs): | |
| super().__init__(hf_config, server_args, _processor, *args, **kwargs) | |
| self.mm_tokens = MultimodalSpecialTokens( | |
| image_token=self._processor.image_token, | |
| image_token_id=self._processor.image_token_id, | |
| ).build(_processor) | |
| async def process_mm_data_async( | |
| self, image_data: List[Union[str, bytes]], input_text, *args, **kwargs | |
| ): | |
| base_out = self.load_mm_data( | |
| prompt=input_text, | |
| image_data=image_data, | |
| multimodal_tokens=self.mm_tokens, | |
| ) | |
| mm_items, input_ids, _ = self.process_and_combine_mm_data( | |
| base_out, self.mm_tokens | |
| ) | |
| return { | |
| "mm_items": mm_items, | |
| "input_ids": input_ids.tolist(), | |
| "im_token_id": self.mm_tokens.image_token_id, | |
| } | |
Xet Storage Details
- Size:
- 1.23 kB
- Xet hash:
- 9763fb34bf81e5afaa19268124d35c4abe3620ccbaaedc344a786fefe15a2fe9
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