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Adding ColQwen2.5 support #83

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Adding ColQwen2.5 support #83

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24 changes: 23 additions & 1 deletion byaldi/colpali.py
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Original file line number Diff line number Diff line change
Expand Up @@ -7,7 +7,7 @@

import srsly
import torch
from colpali_engine.models import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor
from colpali_engine.models import ColPali, ColPaliProcessor, ColQwen2, ColQwen2Processor, ColQwen2_5, ColQwen2_5_Processor
from pdf2image import convert_from_path
from PIL import Image

Expand Down Expand Up @@ -35,6 +35,7 @@ def __init__(
if (
"colpali" not in pretrained_model_name_or_path.lower()
and "colqwen2" not in pretrained_model_name_or_path.lower()
and "colqwen2.5" not in pretrained_model_name_or_path.lower()
):
raise ValueError(
"This pre-release version of Byaldi only supports ColPali and ColQwen2 for now. Incorrect model name specified."
Expand Down Expand Up @@ -77,6 +78,18 @@ def __init__(
),
token=kwargs.get("hf_token", None) or os.environ.get("HF_TOKEN"),
)
elif "colqwen2.5" in pretrained_model_name_or_path.lower():
self.model = ColQwen2_5.from_pretrained(
self.pretrained_model_name_or_path,
torch_dtype=torch.bfloat16,
device_map=(
"cuda"
if device == "cuda"
or (isinstance(device, torch.device) and device.type == "cuda")
else None
),
token=kwargs.get("hf_token", None) or os.environ.get("HF_TOKEN"),
)
elif "colqwen2" in pretrained_model_name_or_path.lower():
self.model = ColQwen2.from_pretrained(
self.pretrained_model_name_or_path,
Expand All @@ -99,6 +112,14 @@ def __init__(
token=kwargs.get("hf_token", None) or os.environ.get("HF_TOKEN"),
),
)
elif "colqwen2.5" in pretrained_model_name_or_path.lower():
self.processor = cast(
ColQwen2_5_Processor,
ColQwen2_5_Processor.from_pretrained(
self.pretrained_model_name_or_path,
token=kwargs.get("hf_token", None) or os.environ.get("HF_TOKEN"),
),
)
elif "colqwen2" in pretrained_model_name_or_path.lower():
self.processor = cast(
ColQwen2Processor,
Expand Down Expand Up @@ -742,3 +763,4 @@ def encode_query(self, query: Union[str, List[str]]) -> torch.Tensor:

def get_doc_ids_to_file_names(self):
return self.doc_ids_to_file_names