leideng's picture
download
raw
1.23 kB
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

Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.