new: gpu package (#224)

When HF_HUB_OFFLINE is set to a truthy value (1, true, yes, on),
download_model() should treat local_files_only=True to avoid any
network calls. Currently, even with the local-cache-first pass (which
may fail due to missing metadata), the retry loop still calls
download_files_from_huggingface() without local_files_only, which
triggers model_info() — a network API call that immediately fails in
offline mode. This causes an unnecessary fallback to GCS download from
storage.googleapis.com.

By setting local_files_only=True when HF_HUB_OFFLINE is enabled:

1. The HF local cache pass works if the model is cached
2. The retry loop skips the network-dependent HF path entirely
3. retrieve_model_gcs() only checks for local fast-* directories
4. No network calls are attempted at all

The truthy value check aligns with huggingface_hub's own parsing of
HF_HUB_OFFLINE, which accepts "1", "true", "yes", "on" (case-insensitive).

This is critical for air-gapped / restricted environments where both
HuggingFace and Google Cloud Storage are unreachable.

Made-with: Cursor

.github/workflows/python-publish.yml

@@ -25,7 +25,7 @@ jobs:
     - name: Set up Python
       uses: actions/setup-python@v2
       with:
-        python-version: '3.9.x'
+        python-version: '3.10.x'
     - name: Install dependencies
       run: |
         python -m pip install poetry

.github/workflows/python-tests.yml

@@ -1,10 +1,11 @@
 name: Tests
+run-name: Tests (gpu)
 
 on:
   pull_request:
     branches: [ master, main, gpu ]
   workflow_dispatch:
-  
+
 
 env:
   CARGO_TERM_COLOR: always
@@ -15,29 +16,12 @@ jobs:
     strategy:
       matrix:
         python-version:
-          - '3.9.x'
           - '3.10.x'
           - '3.11.x'
           - '3.12.x'
           - '3.13.x'
         os:
           - ubuntu-latest
-          - macos-latest
-          - windows-latest
-        exclude:
-          # Exclude 3.10–3.12 for macOS and Windows
-          - os: macos-latest
-            python-version: '3.10.x'
-          - os: macos-latest
-            python-version: '3.11.x'
-          - os: macos-latest
-            python-version: '3.12.x'
-          - os: windows-latest
-            python-version: '3.10.x'
-          - os: windows-latest
-            python-version: '3.11.x'
-          - os: windows-latest
-            python-version: '3.12.x'
 
     runs-on: ${{ matrix.os }}
 

.github/workflows/type-checkers.yml

@@ -8,7 +8,7 @@ jobs:
     strategy:
       fail-fast: true
       matrix:
-        python-version: ["3.9", "3.10", "3.11", "3.12", "3.13"]
+        python-version: ["3.10", "3.11", "3.12", "3.13"]
         os: [ubuntu-latest]
 
     name: Python ${{ matrix.python-version }} test

fastembed/common/model_description.py

@@ -1,12 +1,12 @@
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Optional, Any
+from typing import Any
 
 
 @dataclass(frozen=True)
 class ModelSource:
-    hf: Optional[str] = None
-    url: Optional[str] = None
+    hf: str | None = None
+    url: str | None = None
     _deprecated_tar_struct: bool = False
 
     @property
@@ -33,8 +33,8 @@ class BaseModelDescription:
 
 @dataclass(frozen=True)
 class DenseModelDescription(BaseModelDescription):
-    dim: Optional[int] = None
-    tasks: Optional[dict[str, Any]] = field(default_factory=dict)
+    dim: int | None = None
+    tasks: dict[str, Any] | None = field(default_factory=dict)
 
     def __post_init__(self) -> None:
         assert self.dim is not None, "dim is required for dense model description"
@@ -42,8 +42,8 @@ class DenseModelDescription(BaseModelDescription):
 
 @dataclass(frozen=True)
 class SparseModelDescription(BaseModelDescription):
-    requires_idf: Optional[bool] = None
-    vocab_size: Optional[int] = None
+    requires_idf: bool | None = None
+    vocab_size: int | None = None
 
 
 class PoolingType(str, Enum):

fastembed/common/model_management.py

@@ -5,7 +5,7 @@ import shutil
 import tarfile
 from copy import deepcopy
 from pathlib import Path
-from typing import Any, Optional, Union, TypeVar, Generic
+from typing import Any, TypeVar, Generic
 
 import requests
 from huggingface_hub import snapshot_download, model_info, list_repo_tree
@@ -180,8 +180,8 @@ class ModelManagement(Generic[T]):
 
         def _collect_file_metadata(
             model_dir: Path, repo_files: list[RepoFile]
-        ) -> dict[str, dict[str, Union[int, str]]]:
-            meta: dict[str, dict[str, Union[int, str]]] = {}
+        ) -> dict[str, dict[str, int | str]]:
+            meta: dict[str, dict[str, int | str]] = {}
             file_info_map = {f.path: f for f in repo_files}
             for file_path in model_dir.rglob("*"):
                 if file_path.is_file() and file_path.name != cls.METADATA_FILE:
@@ -193,9 +193,7 @@ class ModelManagement(Generic[T]):
                         }
             return meta
 
-        def _save_file_metadata(
-            model_dir: Path, meta: dict[str, dict[str, Union[int, str]]]
-        ) -> None:
+        def _save_file_metadata(model_dir: Path, meta: dict[str, dict[str, int | str]]) -> None:
             try:
                 if not model_dir.exists():
                     model_dir.mkdir(parents=True, exist_ok=True)
@@ -397,7 +395,11 @@ class ModelManagement(Generic[T]):
             Path: The path to the downloaded model directory.
         """
         local_files_only = kwargs.get("local_files_only", False)
-        specific_model_path: Optional[str] = kwargs.pop("specific_model_path", None)
+        hf_offline = os.environ.get("HF_HUB_OFFLINE", "").strip().upper()
+        if not local_files_only and hf_offline in {"1", "TRUE", "YES", "ON"}:
+            local_files_only = True
+            kwargs["local_files_only"] = True
+        specific_model_path: str | None = kwargs.pop("specific_model_path", None)
         if specific_model_path:
             return Path(specific_model_path)
         retries = 1 if local_files_only else retries

fastembed/common/onnx_model.py

@@ -1,7 +1,7 @@
 import warnings
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Any, Generic, Iterable, Optional, Sequence, Type, TypeVar
+from typing import Any, Generic, Iterable, Sequence, Type, TypeVar
 
 import numpy as np
 import onnxruntime as ort
@@ -9,7 +9,7 @@ import onnxruntime as ort
 from numpy.typing import NDArray
 from tokenizers import Tokenizer
 
-from fastembed.common.types import OnnxProvider, NumpyArray
+from fastembed.common.types import OnnxProvider, NumpyArray, Device
 from fastembed.parallel_processor import Worker
 
 # Holds type of the embedding result
@@ -19,8 +19,9 @@ T = TypeVar("T")
 @dataclass
 class OnnxOutputContext:
     model_output: NumpyArray
-    attention_mask: Optional[NDArray[np.int64]] = None
-    input_ids: Optional[NDArray[np.int64]] = None
+    attention_mask: NDArray[np.int64] | None = None
+    input_ids: NDArray[np.int64] | None = None
+    metadata: dict[str, Any] | None = None
 
 
 class OnnxModel(Generic[T]):
@@ -43,8 +44,8 @@ class OnnxModel(Generic[T]):
         raise NotImplementedError("Subclasses must implement this method")
 
     def __init__(self) -> None:
-        self.model: Optional[ort.InferenceSession] = None
-        self.tokenizer: Optional[Tokenizer] = None
+        self.model: ort.InferenceSession | None = None
+        self.tokenizer: Tokenizer | None = None
 
     def _preprocess_onnx_input(
         self, onnx_input: dict[str, NumpyArray], **kwargs: Any
@@ -58,25 +59,30 @@ class OnnxModel(Generic[T]):
         self,
         model_dir: Path,
         model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        threads: int | None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_id: int | None = None,
+        extra_session_options: dict[str, Any] | None = None,
     ) -> None:
         model_path = model_dir / model_file
         # List of Execution Providers: https://onnxruntime.ai/docs/execution-providers
+        available_providers = ort.get_available_providers()
+        cuda_available = "CUDAExecutionProvider" in available_providers
+        explicit_cuda = cuda is True or cuda == Device.CUDA
 
-        if cuda and providers is not None:
+        if explicit_cuda and providers is not None:
             warnings.warn(
-                f"`cuda` and `providers` are mutually exclusive parameters, cuda: {cuda}, providers: {providers}",
+                f"`cuda` and `providers` are mutually exclusive parameters, "
+                f"cuda: {cuda}, providers: {providers}. If you'd like to use providers, cuda should be one of "
+                f"[False, Device.CPU, Device.AUTO].",
                 category=UserWarning,
                 stacklevel=6,
             )
 
         if providers is not None:
             onnx_providers = list(providers)
-        elif cuda:
+        elif explicit_cuda or (cuda == Device.AUTO and cuda_available):
             if device_id is None:
                 onnx_providers = ["CUDAExecutionProvider"]
             else:
@@ -84,7 +90,6 @@ class OnnxModel(Generic[T]):
         else:
             onnx_providers = ["CPUExecutionProvider"]
 
-        available_providers = ort.get_available_providers()
         requested_provider_names: list[str] = []
         for provider in onnx_providers:
             # check providers available

fastembed/common/preprocessor_utils.py

@@ -50,9 +50,10 @@ def load_tokenizer(model_dir: Path) -> tuple[Tokenizer, dict[str, int]]:
 
     tokenizer = Tokenizer.from_file(str(tokenizer_path))
     tokenizer.enable_truncation(max_length=max_context)
-    tokenizer.enable_padding(
-        pad_id=config.get("pad_token_id", 0), pad_token=tokenizer_config["pad_token"]
-    )
+    if not tokenizer.padding:
+        tokenizer.enable_padding(
+            pad_id=config.get("pad_token_id", 0), pad_token=tokenizer_config["pad_token"]
+        )
 
     for token in tokens_map.values():
         if isinstance(token, str):

fastembed/common/types.py

@@ -1,25 +1,27 @@
+from enum import Enum
 from pathlib import Path
-import sys
-from PIL import Image
-from typing import Any, Union
+from typing import Any, TypeAlias
+
 import numpy as np
 from numpy.typing import NDArray
-
-if sys.version_info >= (3, 10):
-    from typing import TypeAlias
-else:
-    from typing_extensions import TypeAlias
+from PIL import Image
 
 
-PathInput: TypeAlias = Union[str, Path]
-ImageInput: TypeAlias = Union[PathInput, Image.Image]
+class Device(str, Enum):
+    CPU = "cpu"
+    CUDA = "cuda"
+    AUTO = "auto"
 
-OnnxProvider: TypeAlias = Union[str, tuple[str, dict[Any, Any]]]
-NumpyArray = Union[
-    NDArray[np.float64],
-    NDArray[np.float32],
-    NDArray[np.float16],
-    NDArray[np.int8],
-    NDArray[np.int64],
-    NDArray[np.int32],
-]
+
+PathInput: TypeAlias = str | Path
+ImageInput: TypeAlias = PathInput | Image.Image
+
+OnnxProvider: TypeAlias = str | tuple[str, dict[Any, Any]]
+NumpyArray: TypeAlias = (
+    NDArray[np.float64]
+    | NDArray[np.float32]
+    | NDArray[np.float16]
+    | NDArray[np.int8]
+    | NDArray[np.int64]
+    | NDArray[np.int32]
+)

fastembed/common/utils.py

@@ -5,7 +5,7 @@ import tempfile
 import unicodedata
 from pathlib import Path
 from itertools import islice
-from typing import Iterable, Optional, TypeVar
+from typing import Iterable, TypeVar
 
 import numpy as np
 from numpy.typing import NDArray
@@ -45,7 +45,7 @@ def iter_batch(iterable: Iterable[T], size: int) -> Iterable[list[T]]:
         yield b
 
 
-def define_cache_dir(cache_dir: Optional[str] = None) -> Path:
+def define_cache_dir(cache_dir: str | None = None) -> Path:
     """
     Define the cache directory for fastembed
     """

fastembed/embedding.py

@@ -1,4 +1,4 @@
-from typing import Optional, Any
+from typing import Any
 
 from loguru import logger
 
@@ -17,8 +17,8 @@ class JinaEmbedding(TextEmbedding):
     def __init__(
         self,
         model_name: str = "jinaai/jina-embeddings-v2-base-en",
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, threads, **kwargs)

fastembed/image/image_embedding.py

@@ -1,7 +1,7 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.image.image_embedding_base import ImageEmbeddingBase
 from fastembed.image.onnx_embedding import OnnxImageEmbedding
@@ -48,11 +48,11 @@ class ImageEmbedding(ImageEmbeddingBase):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -98,7 +98,7 @@ class ImageEmbedding(ImageEmbeddingBase):
             ValueError: If the model name is not found in the supported models.
         """
         descriptions = cls._list_supported_models()
-        embedding_size: Optional[int] = None
+        embedding_size: int | None = None
         for description in descriptions:
             if description.model.lower() == model_name.lower():
                 embedding_size = description.dim
@@ -113,9 +113,9 @@ class ImageEmbedding(ImageEmbeddingBase):
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """

fastembed/image/image_embedding_base.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Any, Union
+from typing import Iterable, Any
 
 from fastembed.common.model_description import DenseModelDescription
 from fastembed.common.types import NumpyArray
@@ -10,21 +10,21 @@ class ImageEmbeddingBase(ModelManagement[DenseModelDescription]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
         self.threads = threads
         self._local_files_only = kwargs.pop("local_files_only", False)
-        self._embedding_size: Optional[int] = None
+        self._embedding_size: int | None = None
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """

fastembed/image/onnx_embedding.py

@@ -1,7 +1,7 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
@@ -63,14 +63,15 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -82,10 +83,11 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -105,7 +107,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -150,9 +152,9 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
 
     def embed(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """

fastembed/image/onnx_image_model.py

@@ -2,13 +2,13 @@ import contextlib
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from PIL import Image
 
 from fastembed.image.transform.operators import Compose
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common import ImageInput, OnnxProvider
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
 from fastembed.common.preprocessor_utils import load_preprocessor
@@ -37,7 +37,7 @@ class OnnxImageModel(OnnxModel[T]):
 
     def __init__(self) -> None:
         super().__init__()
-        self.processor: Optional[Compose] = None
+        self.processor: Compose | None = None
 
     def _preprocess_onnx_input(
         self, onnx_input: dict[str, NumpyArray], **kwargs: Any
@@ -51,11 +51,11 @@ class OnnxImageModel(OnnxModel[T]):
         self,
         model_dir: Path,
         model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        threads: int | None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_id: int | None = None,
+        extra_session_options: dict[str, Any] | None = None,
     ) -> None:
         super()._load_onnx_model(
             model_dir=model_dir,
@@ -76,9 +76,11 @@ class OnnxImageModel(OnnxModel[T]):
         return {input_name: encoded}
 
     def onnx_embed(self, images: list[ImageInput], **kwargs: Any) -> OnnxOutputContext:
-        with contextlib.ExitStack():
+        with contextlib.ExitStack() as stack:
             image_files = [
-                Image.open(image) if not isinstance(image, Image.Image) else image
+                stack.enter_context(Image.open(image))
+                if not isinstance(image, Image.Image)
+                else image
                 for image in images
             ]
             assert self.processor is not None, "Processor is not initialized"
@@ -93,15 +95,15 @@ class OnnxImageModel(OnnxModel[T]):
         self,
         model_name: str,
         cache_dir: str,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        parallel: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        specific_model_path: str | None = None,
+        extra_session_options: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False

fastembed/image/transform/functional.py

@@ -1,5 +1,3 @@
-from typing import Union
-
 import numpy as np
 from PIL import Image
 
@@ -15,7 +13,7 @@ def convert_to_rgb(image: Image.Image) -> Image.Image:
 
 
 def center_crop(
-    image: Union[Image.Image, NumpyArray],
+    image: Image.Image | NumpyArray,
     size: tuple[int, int],
 ) -> NumpyArray:
     if isinstance(image, np.ndarray):
@@ -64,8 +62,8 @@ def center_crop(
 
 def normalize(
     image: NumpyArray,
-    mean: Union[float, list[float]],
-    std: Union[float, list[float]],
+    mean: float | list[float],
+    std: float | list[float],
 ) -> NumpyArray:
     num_channels = image.shape[1] if len(image.shape) == 4 else image.shape[0]
 
@@ -96,8 +94,8 @@ def normalize(
 
 def resize(
     image: Image.Image,
-    size: Union[int, tuple[int, int]],
-    resample: Union[int, Image.Resampling] = Image.Resampling.BILINEAR,
+    size: int | tuple[int, int],
+    resample: int | Image.Resampling = Image.Resampling.BILINEAR,
 ) -> Image.Image:
     if isinstance(size, tuple):
         return image.resize(size, resample)
@@ -117,7 +115,7 @@ def rescale(image: NumpyArray, scale: float, dtype: type = np.float32) -> NumpyA
     return (image * scale).astype(dtype)
 
 
-def pil2ndarray(image: Union[Image.Image, NumpyArray]) -> NumpyArray:
+def pil2ndarray(image: Image.Image | NumpyArray) -> NumpyArray:
     if isinstance(image, Image.Image):
         return np.asarray(image).transpose((2, 0, 1))
     return image
@@ -126,7 +124,7 @@ def pil2ndarray(image: Union[Image.Image, NumpyArray]) -> NumpyArray:
 def pad2square(
     image: Image.Image,
     size: int,
-    fill_color: Union[str, int, tuple[int, ...]] = 0,
+    fill_color: str | int | tuple[int, ...] = 0,
 ) -> Image.Image:
     height, width = image.height, image.width
 
@@ -147,3 +145,77 @@ def pad2square(
     new_image = Image.new(mode="RGB", size=(size, size), color=fill_color)
     new_image.paste(image.crop((left, top, right, bottom)) if crop_required else image)
     return new_image
+
+
+def resize_longest_edge(
+    image: Image.Image,
+    max_size: int,
+    resample: int | Image.Resampling = Image.Resampling.LANCZOS,
+) -> Image.Image:
+    height, width = image.height, image.width
+    aspect_ratio = width / height
+
+    if width >= height:
+        # Width is longer
+        new_width = max_size
+        new_height = int(new_width / aspect_ratio)
+    else:
+        # Height is longer
+        new_height = max_size
+        new_width = int(new_height * aspect_ratio)
+
+    # Ensure even dimensions
+    if new_height % 2 != 0:
+        new_height += 1
+    if new_width % 2 != 0:
+        new_width += 1
+
+    return image.resize((new_width, new_height), resample)
+
+
+def crop_ndarray(
+    image: NumpyArray,
+    x1: int,
+    y1: int,
+    x2: int,
+    y2: int,
+    channel_first: bool = True,
+) -> NumpyArray:
+    if channel_first:
+        # (C, H, W) format
+        return image[:, y1:y2, x1:x2]
+    else:
+        # (H, W, C) format
+        return image[y1:y2, x1:x2, :]
+
+
+def resize_ndarray(
+    image: NumpyArray,
+    size: tuple[int, int],
+    resample: int | Image.Resampling = Image.Resampling.LANCZOS,
+    channel_first: bool = True,
+) -> NumpyArray:
+    # Convert to PIL-friendly format (H, W, C)
+    if channel_first:
+        img_hwc = image.transpose((1, 2, 0))
+    else:
+        img_hwc = image
+
+    # Handle different dtypes
+    if img_hwc.dtype == np.float32 or img_hwc.dtype == np.float64:
+        # Assume normalized, scale to 0-255 for PIL
+        img_hwc_scaled = (img_hwc * 255).astype(np.uint8)
+        pil_img = Image.fromarray(img_hwc_scaled, mode="RGB")
+        resized = pil_img.resize(size, resample)
+        result = np.array(resized).astype(np.float32) / 255.0
+    else:
+        # uint8 or similar
+        pil_img = Image.fromarray(img_hwc.astype(np.uint8), mode="RGB")
+        resized = pil_img.resize(size, resample)
+        result = np.array(resized)
+
+    # Convert back to original format
+    if channel_first:
+        result = result.transpose((2, 0, 1))
+
+    return result

fastembed/image/transform/operators.py

@@ -1,4 +1,5 @@
-from typing import Any, Union, Optional
+from typing import Any
+import math
 
 from PIL import Image
 
@@ -6,16 +7,19 @@ from fastembed.common.types import NumpyArray
 from fastembed.image.transform.functional import (
     center_crop,
     convert_to_rgb,
+    crop_ndarray,
     normalize,
     pil2ndarray,
     rescale,
     resize,
+    resize_longest_edge,
+    resize_ndarray,
     pad2square,
 )
 
 
 class Transform:
-    def __call__(self, images: list[Any]) -> Union[list[Image.Image], list[NumpyArray]]:
+    def __call__(self, images: list[Any]) -> list[Image.Image] | list[NumpyArray]:
         raise NotImplementedError("Subclasses must implement this method")
 
 
@@ -33,18 +37,28 @@ class CenterCrop(Transform):
 
 
 class Normalize(Transform):
-    def __init__(self, mean: Union[float, list[float]], std: Union[float, list[float]]):
+    def __init__(self, mean: float | list[float], std: float | list[float]):
         self.mean = mean
         self.std = std
 
-    def __call__(self, images: list[NumpyArray]) -> list[NumpyArray]:
-        return [normalize(image, mean=self.mean, std=self.std) for image in images]
+    def __call__(  # type: ignore[override]
+        self, images: list[NumpyArray] | list[list[NumpyArray]]
+    ) -> list[NumpyArray] | list[list[NumpyArray]]:
+        if images and isinstance(images[0], list):
+            # Nested structure from ImageSplitter
+            return [
+                [normalize(image, mean=self.mean, std=self.std) for image in img_patches]  # type: ignore[arg-type]
+                for img_patches in images
+            ]
+        else:
+            # Flat structure (backward compatibility)
+            return [normalize(image, mean=self.mean, std=self.std) for image in images]  # type: ignore[arg-type]
 
 
 class Resize(Transform):
     def __init__(
         self,
-        size: Union[int, tuple[int, int]],
+        size: int | tuple[int, int],
         resample: Image.Resampling = Image.Resampling.BICUBIC,
     ):
         self.size = size
@@ -58,12 +72,22 @@ class Rescale(Transform):
     def __init__(self, scale: float = 1 / 255):
         self.scale = scale
 
-    def __call__(self, images: list[NumpyArray]) -> list[NumpyArray]:
-        return [rescale(image, scale=self.scale) for image in images]
+    def __call__(  # type: ignore[override]
+        self, images: list[NumpyArray] | list[list[NumpyArray]]
+    ) -> list[NumpyArray] | list[list[NumpyArray]]:
+        if images and isinstance(images[0], list):
+            # Nested structure from ImageSplitter
+            return [
+                [rescale(image, scale=self.scale) for image in img_patches]  # type: ignore[arg-type]
+                for img_patches in images
+            ]
+        else:
+            # Flat structure (backward compatibility)
+            return [rescale(image, scale=self.scale) for image in images]  # type: ignore[arg-type]
 
 
 class PILtoNDarray(Transform):
-    def __call__(self, images: list[Union[Image.Image, NumpyArray]]) -> list[NumpyArray]:
+    def __call__(self, images: list[Image.Image | NumpyArray]) -> list[NumpyArray]:
         return [pil2ndarray(image) for image in images]
 
 
@@ -71,7 +95,7 @@ class PadtoSquare(Transform):
     def __init__(
         self,
         size: int,
-        fill_color: Union[str, int, tuple[int, ...]],
+        fill_color: str | int | tuple[int, ...],
     ):
         self.size = size
         self.fill_color = fill_color
@@ -82,13 +106,174 @@ class PadtoSquare(Transform):
         ]
 
 
+class ResizeLongestEdge(Transform):
+    """Resize images so the longest edge equals target size, preserving aspect ratio."""
+
+    def __init__(
+        self,
+        size: int,
+        resample: Image.Resampling = Image.Resampling.LANCZOS,
+    ):
+        self.size = size
+        self.resample = resample
+
+    def __call__(self, images: list[Image.Image]) -> list[Image.Image]:
+        return [resize_longest_edge(image, self.size, self.resample) for image in images]
+
+
+class ResizeForVisionEncoder(Transform):
+    """
+    Resize both dimensions to be multiples of vision_encoder_max_size.
+    Preserves aspect ratio approximately.
+    Works on numpy arrays in (C, H, W) format.
+    """
+
+    def __init__(
+        self,
+        max_size: int,
+        resample: Image.Resampling = Image.Resampling.LANCZOS,
+    ):
+        self.max_size = max_size
+        self.resample = resample
+
+    def __call__(self, images: list[NumpyArray]) -> list[NumpyArray]:
+        result = []
+        for image in images:
+            # Assume (C, H, W) format
+            _, height, width = image.shape
+
+            aspect_ratio = width / height
+
+            if width >= height:
+                # Calculate new width as multiple of max_size
+                new_width = math.ceil(width / self.max_size) * self.max_size
+                new_height = int(new_width / aspect_ratio)
+                new_height = math.ceil(new_height / self.max_size) * self.max_size
+            else:
+                # Calculate new height as multiple of max_size
+                new_height = math.ceil(height / self.max_size) * self.max_size
+                new_width = int(new_height * aspect_ratio)
+                new_width = math.ceil(new_width / self.max_size) * self.max_size
+
+            # Resize using the ndarray resize function
+            resized = resize_ndarray(
+                image,
+                size=(new_width, new_height),  # PIL expects (width, height)
+                resample=self.resample,
+                channel_first=True,
+            )
+            result.append(resized)
+
+        return result
+
+
+class ImageSplitter(Transform):
+    """
+    Split images into grid of patches plus a global view.
+
+    If image dimensions exceed max_size:
+    - Divide into ceil(H/max_size) x ceil(W/max_size) patches
+    - Each patch is cropped from the image
+    - Add a global view (original resized to max_size x max_size)
+
+    If image is smaller than max_size:
+    - Return single image unchanged
+
+    Works on numpy arrays in (C, H, W) format.
+    """
+
+    def __init__(
+        self,
+        max_size: int,
+        resample: Image.Resampling = Image.Resampling.LANCZOS,
+    ):
+        self.max_size = max_size
+        self.resample = resample
+
+    def __call__(self, images: list[NumpyArray]) -> list[list[NumpyArray]]:  # type: ignore[override]
+        result = []
+
+        for image in images:
+            # Assume (C, H, W) format
+            _, height, width = image.shape
+            max_height = max_width = self.max_size
+
+            frames = []
+
+            if height > max_height or width > max_width:
+                # Calculate the number of splits needed
+                num_splits_h = math.ceil(height / max_height)
+                num_splits_w = math.ceil(width / max_width)
+
+                # Calculate optimal patch dimensions
+                optimal_height = math.ceil(height / num_splits_h)
+                optimal_width = math.ceil(width / num_splits_w)
+
+                # Generate patches in grid order (row by row)
+                for r in range(num_splits_h):
+                    for c in range(num_splits_w):
+                        # Calculate crop coordinates
+                        start_x = c * optimal_width
+                        start_y = r * optimal_height
+                        end_x = min(start_x + optimal_width, width)
+                        end_y = min(start_y + optimal_height, height)
+
+                        # Crop the patch
+                        cropped = crop_ndarray(
+                            image, x1=start_x, y1=start_y, x2=end_x, y2=end_y, channel_first=True
+                        )
+                        frames.append(cropped)
+
+                # Add global view (resized to max_size x max_size)
+                global_view = resize_ndarray(
+                    image,
+                    size=(max_width, max_height),  # PIL expects (width, height)
+                    resample=self.resample,
+                    channel_first=True,
+                )
+                frames.append(global_view)
+            else:
+                # Image is small enough, no splitting needed
+                frames.append(image)
+
+            # Append (not extend) to preserve per-image grouping
+            result.append(frames)
+
+        return result
+
+
+class SquareResize(Transform):
+    """
+    Resize images to square dimensions (max_size x max_size).
+    Works on numpy arrays in (C, H, W) format.
+    """
+
+    def __init__(
+        self,
+        size: int,
+        resample: Image.Resampling = Image.Resampling.LANCZOS,
+    ):
+        self.size = size
+        self.resample = resample
+
+    def __call__(self, images: list[NumpyArray]) -> list[list[NumpyArray]]:  # type: ignore[override]
+        return [
+            [
+                resize_ndarray(
+                    image, size=(self.size, self.size), resample=self.resample, channel_first=True
+                )
+            ]
+            for image in images
+        ]
+
+
 class Compose:
     def __init__(self, transforms: list[Transform]):
         self.transforms = transforms
 
     def __call__(
-        self, images: Union[list[Image.Image], list[NumpyArray]]
-    ) -> Union[list[NumpyArray], list[Image.Image]]:
+        self, images: list[Image.Image] | list[NumpyArray]
+    ) -> list[NumpyArray] | list[Image.Image]:
         for transform in self.transforms:
             images = transform(images)
         return images
@@ -118,6 +303,7 @@ class Compose:
                 Valid size keys (nested):
                     - {"height", "width"}
                     - {"shortest_edge"}
+                    - {"longest_edge"}
 
         Returns:
             Compose: Image processor.
@@ -128,6 +314,7 @@ class Compose:
         cls._get_pad2square(transforms, config)
         cls._get_center_crop(transforms, config)
         cls._get_pil2ndarray(transforms, config)
+        cls._get_image_splitting(transforms, config)
         cls._get_rescale(transforms, config)
         cls._get_normalize(transforms, config)
         return cls(transforms=transforms)
@@ -196,6 +383,25 @@ class Compose:
                             resample=resample,
                         )
                     )
+        elif mode == "Idefics3ImageProcessor":
+            if config.get("do_resize", False):
+                size = config.get("size", {})
+                if "longest_edge" not in size:
+                    raise ValueError(
+                        "Size dictionary must contain 'longest_edge' key for Idefics3ImageProcessor"
+                    )
+
+                # Handle resample parameter - can be int enum or PIL.Image.Resampling
+                resample = config.get("resample", Image.Resampling.LANCZOS)
+                if isinstance(resample, int):
+                    resample = Image.Resampling(resample)
+
+                transforms.append(
+                    ResizeLongestEdge(
+                        size=size["longest_edge"],
+                        resample=resample,
+                    )
+                )
         else:
             raise ValueError(f"Preprocessor {mode} is not supported")
 
@@ -217,6 +423,8 @@ class Compose:
             pass
         elif mode == "JinaCLIPImageProcessor":
             pass
+        elif mode == "Idefics3ImageProcessor":
+            pass
         else:
             raise ValueError(f"Preprocessor {mode} is not supported")
 
@@ -224,6 +432,28 @@ class Compose:
     def _get_pil2ndarray(transforms: list[Transform], config: dict[str, Any]) -> None:
         transforms.append(PILtoNDarray())
 
+    @classmethod
+    def _get_image_splitting(cls, transforms: list[Transform], config: dict[str, Any]) -> None:
+        """
+        Add image splitting transforms for Idefics3.
+        Handles conditional logic: splitting vs square resize.
+        Must be called AFTER PILtoNDarray.
+        """
+        mode = config.get("image_processor_type", "CLIPImageProcessor")
+
+        if mode == "Idefics3ImageProcessor":
+            do_splitting = config.get("do_image_splitting", False)
+            max_size = config.get("max_image_size", {}).get("longest_edge", 512)
+            resample = config.get("resample", Image.Resampling.LANCZOS)
+            if isinstance(resample, int):
+                resample = Image.Resampling(resample)
+
+            if do_splitting:
+                transforms.append(ResizeForVisionEncoder(max_size, resample))
+                transforms.append(ImageSplitter(max_size, resample))
+            else:
+                transforms.append(SquareResize(max_size, resample))
+
     @staticmethod
     def _get_rescale(transforms: list[Transform], config: dict[str, Any]) -> None:
         if config.get("do_rescale", True):
@@ -253,7 +483,7 @@ class Compose:
             )
 
     @staticmethod
-    def _interpolation_resolver(resample: Optional[str] = None) -> Image.Resampling:
+    def _interpolation_resolver(resample: str | None = None) -> Image.Resampling:
         interpolation_map = {
             "nearest": Image.Resampling.NEAREST,
             "lanczos": Image.Resampling.LANCZOS,

fastembed/late_interaction/colbert.py

@@ -1,11 +1,11 @@
 import string
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from tokenizers import Encoding, Tokenizer
 
 from fastembed.common.preprocessor_utils import load_tokenizer
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, iter_batch
@@ -19,7 +19,7 @@ supported_colbert_models: list[DenseModelDescription] = [
     DenseModelDescription(
         model="colbert-ir/colbertv2.0",
         dim=128,
-        description="Late interaction model",
+        description="Text embeddings, Unimodal (text), English, 512 input tokens truncation, 2023 year",
         license="mit",
         size_in_GB=0.44,
         sources=ModelSource(hf="colbert-ir/colbertv2.0"),
@@ -28,7 +28,7 @@ supported_colbert_models: list[DenseModelDescription] = [
     DenseModelDescription(
         model="answerdotai/answerai-colbert-small-v1",
         dim=96,
-        description="Text embeddings, Unimodal (text), Multilingual (~100 languages), 512 input tokens truncation, 2024 year",
+        description="Text embeddings, Unimodal (text), English, 512 input tokens truncation, 2024 year",
         license="apache-2.0",
         size_in_GB=0.13,
         sources=ModelSource(hf="answerdotai/answerai-colbert-small-v1"),
@@ -98,7 +98,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         batch_size: int = 1024,
         is_doc: bool = True,
         include_extension: bool = False,
@@ -140,14 +140,14 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -159,10 +159,11 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -182,7 +183,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -198,11 +199,11 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             local_files_only=self._local_files_only,
             specific_model_path=self._specific_model_path,
         )
-        self.mask_token_id: Optional[int] = None
-        self.pad_token_id: Optional[int] = None
+        self.mask_token_id: int | None = None
+        self.pad_token_id: int | None = None
         self.skip_list: set[int] = set()
 
-        self.query_tokenizer: Optional[Tokenizer] = None
+        self.query_tokenizer: Tokenizer | None = None
 
         if not self.lazy_load:
             self.load_onnx_model()
@@ -238,9 +239,9 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -273,7 +274,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
             **kwargs,
         )
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         if isinstance(query, str):
             query = [query]
 

fastembed/late_interaction/late_interaction_embedding_base.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Any
 
 from fastembed.common.model_description import DenseModelDescription
 from fastembed.common.types import NumpyArray
@@ -9,21 +9,21 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
         self.threads = threads
         self._local_files_only = kwargs.pop("local_files_only", False)
-        self._embedding_size: Optional[int] = None
+        self._embedding_size: int | None = None
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         raise NotImplementedError()
@@ -43,7 +43,7 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         """
         Embeds queries
 
@@ -72,7 +72,7 @@ class LateInteractionTextEmbeddingBase(ModelManagement[DenseModelDescription]):
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         batch_size: int = 1024,
         **kwargs: Any,
     ) -> int:

fastembed/late_interaction/late_interaction_text_embedding.py

@@ -1,8 +1,8 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
 from fastembed.common.model_description import DenseModelDescription
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common import OnnxProvider
 from fastembed.late_interaction.colbert import Colbert
 from fastembed.late_interaction.jina_colbert import JinaColbert
@@ -51,11 +51,11 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -101,7 +101,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
             ValueError: If the model name is not found in the supported models.
         """
         descriptions = cls._list_supported_models()
-        embedding_size: Optional[int] = None
+        embedding_size: int | None = None
         for description in descriptions:
             if description.model.lower() == model_name.lower():
                 embedding_size = description.dim
@@ -116,9 +116,9 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -138,7 +138,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         """
         Embeds queries
 
@@ -154,7 +154,7 @@ class LateInteractionTextEmbedding(LateInteractionTextEmbeddingBase):
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         batch_size: int = 1024,
         is_doc: bool = True,
         include_extension: bool = False,

fastembed/late_interaction/token_embeddings.py

@@ -1,5 +1,5 @@
 from dataclasses import asdict
-from typing import Union, Iterable, Optional, Any, Type
+from typing import Iterable, Any, Type
 
 from fastembed.common.model_description import DenseModelDescription, ModelSource
 from fastembed.common.onnx_model import OnnxOutputContext
@@ -63,9 +63,9 @@ class TokenEmbeddingsModel(OnnxTextEmbedding, LateInteractionTextEmbeddingBase):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         yield from super().embed(documents, batch_size=batch_size, parallel=parallel, **kwargs)

fastembed/late_interaction_multimodal/colmodernvbert.py

@@ -0,0 +1,532 @@
+import contextlib
+from typing import Any, Iterable, Type, Optional, Sequence
+import json
+
+import numpy as np
+from tokenizers import Encoding
+from PIL import Image
+
+from fastembed.common import ImageInput
+from fastembed.common.model_description import DenseModelDescription, ModelSource
+from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.types import NumpyArray, OnnxProvider
+from fastembed.common.utils import define_cache_dir, iter_batch
+from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding_base import (
+    LateInteractionMultimodalEmbeddingBase,
+)
+from fastembed.late_interaction_multimodal.onnx_multimodal_model import (
+    OnnxMultimodalModel,
+    TextEmbeddingWorker,
+    ImageEmbeddingWorker,
+)
+
+supported_colmodernvbert_models: list[DenseModelDescription] = [
+    DenseModelDescription(
+        model="Qdrant/colmodernvbert",
+        dim=128,
+        description="The late-interaction version of ModernVBERT, CPU friendly, English, 2025.",
+        license="mit",
+        size_in_GB=1.0,
+        sources=ModelSource(hf="Qdrant/colmodernvbert"),
+        additional_files=["processor_config.json"],
+        model_file="model.onnx",
+    ),
+]
+
+
+class ColModernVBERT(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyArray]):
+    """
+    The ModernVBERT/colmodernvbert model implementation. This model uses
+    bidirectional attention, which proves to work better for retrieval.
+
+    See: https://huggingface.co/ModernVBERT/colmodernvbert
+    """
+
+    VISUAL_PROMPT_PREFIX = (
+        "<|begin_of_text|>User:<image>Describe the image.<end_of_utterance>\nAssistant:"
+    )
+    QUERY_AUGMENTATION_TOKEN = "<end_of_utterance>"
+
+    def __init__(
+        self,
+        model_name: str,
+        cache_dir: Optional[str] = None,
+        threads: Optional[int] = None,
+        providers: Optional[Sequence[OnnxProvider]] = None,
+        cuda: bool = False,
+        device_ids: Optional[list[int]] = None,
+        lazy_load: bool = False,
+        device_id: Optional[int] = None,
+        specific_model_path: Optional[str] = None,
+        **kwargs: Any,
+    ):
+        """
+        Args:
+            model_name (str): The name of the model to use.
+            cache_dir (str, optional): The path to the cache directory.
+                                       Can be set using the `FASTEMBED_CACHE_PATH` env variable.
+                                       Defaults to `fastembed_cache` in the system's temp directory.
+            threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
+            providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
+                Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
+            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to False.
+            device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
+                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+            lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
+                Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
+            device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
+
+        Raises:
+            ValueError: If the model_name is not in the format <org>/<model> e.g. BAAI/bge-base-en.
+        """
+        super().__init__(model_name, cache_dir, threads, **kwargs)
+        self.providers = providers
+        self.lazy_load = lazy_load
+        self._extra_session_options = self._select_exposed_session_options(kwargs)
+
+        # List of device ids, that can be used for data parallel processing in workers
+        self.device_ids = device_ids
+        self.cuda = cuda
+
+        # This device_id will be used if we need to load model in current process
+        self.device_id: Optional[int] = None
+        if device_id is not None:
+            self.device_id = device_id
+        elif self.device_ids is not None:
+            self.device_id = self.device_ids[0]
+
+        self.model_description = self._get_model_description(model_name)
+        self.cache_dir = str(define_cache_dir(cache_dir))
+
+        self._specific_model_path = specific_model_path
+        self._model_dir = self.download_model(
+            self.model_description,
+            self.cache_dir,
+            local_files_only=self._local_files_only,
+            specific_model_path=self._specific_model_path,
+        )
+        self.mask_token_id = None
+        self.pad_token_id = None
+        self.image_seq_len: Optional[int] = None
+        self.max_image_size: Optional[int] = None
+        self.image_size: Optional[int] = None
+
+        if not self.lazy_load:
+            self.load_onnx_model()
+
+    @classmethod
+    def _list_supported_models(cls) -> list[DenseModelDescription]:
+        """Lists the supported models.
+
+        Returns:
+            list[DenseModelDescription]: A list of DenseModelDescription objects containing the model information.
+        """
+        return supported_colmodernvbert_models
+
+    def load_onnx_model(self) -> None:
+        self._load_onnx_model(
+            model_dir=self._model_dir,
+            model_file=self.model_description.model_file,
+            threads=self.threads,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_id=self.device_id,
+            extra_session_options=self._extra_session_options,
+        )
+
+        # Load image processing configuration
+        processor_config_path = self._model_dir / "processor_config.json"
+        with open(processor_config_path) as f:
+            processor_config = json.load(f)
+            self.image_seq_len = processor_config.get("image_seq_len", 64)
+
+        preprocessor_config_path = self._model_dir / "preprocessor_config.json"
+        with open(preprocessor_config_path) as f:
+            preprocessor_config = json.load(f)
+            self.max_image_size = preprocessor_config.get("max_image_size", {}).get(
+                "longest_edge", 512
+            )
+
+        # Load model configuration
+        config_path = self._model_dir / "config.json"
+        with open(config_path) as f:
+            model_config = json.load(f)
+            vision_config = model_config.get("vision_config", {})
+            self.image_size = vision_config.get("image_size", 512)
+
+    def _preprocess_onnx_text_input(
+        self, onnx_input: dict[str, NumpyArray], **kwargs: Any
+    ) -> dict[str, NumpyArray]:
+        """
+        Post-process the ONNX model output to convert it into a usable format.
+
+        Args:
+            output (OnnxOutputContext): The raw output from the ONNX model.
+
+        Returns:
+            Iterable[NumpyArray]: Post-processed output as NumPy arrays.
+        """
+        batch_size, seq_length = onnx_input["input_ids"].shape
+        empty_image_placeholder: NumpyArray = np.zeros(
+            (batch_size, seq_length, 3, self.image_size, self.image_size),
+            dtype=np.float32,  # type: ignore[type-var,arg-type,assignment]
+        )
+        onnx_input["pixel_values"] = empty_image_placeholder
+        return onnx_input
+
+    def _post_process_onnx_text_output(
+        self,
+        output: OnnxOutputContext,
+    ) -> Iterable[NumpyArray]:
+        """
+        Post-process the ONNX model output to convert it into a usable format.
+
+        Args:
+            output (OnnxOutputContext): The raw output from the ONNX model.
+
+        Returns:
+            Iterable[NumpyArray]: Post-processed output as NumPy arrays.
+        """
+        return output.model_output
+
+    def tokenize(self, documents: list[str], **kwargs: Any) -> list[Encoding]:
+        # Add query augmentation tokens (matching process_queries logic from colpali-engine)
+        augmented_queries = [doc + self.QUERY_AUGMENTATION_TOKEN * 10 for doc in documents]
+        encoded = self.tokenizer.encode_batch(augmented_queries)  # type: ignore[union-attr]
+        return encoded
+
+    def token_count(
+        self,
+        texts: str | Iterable[str],
+        batch_size: int = 1024,
+        include_extension: bool = False,
+        **kwargs: Any,
+    ) -> int:
+        if not hasattr(self, "model") or self.model is None:
+            self.load_onnx_model()  # loads the tokenizer as well
+        token_num = 0
+        texts = [texts] if isinstance(texts, str) else texts
+        assert self.tokenizer is not None
+        tokenize_func = self.tokenize if include_extension else self.tokenizer.encode_batch
+        for batch in iter_batch(texts, batch_size):
+            token_num += sum([sum(encoding.attention_mask) for encoding in tokenize_func(batch)])
+        return token_num
+
+    def onnx_embed_image(self, images: list[ImageInput], **kwargs: Any) -> OnnxOutputContext:
+        with contextlib.ExitStack() as stack:
+            image_files = [
+                stack.enter_context(Image.open(image))
+                if not isinstance(image, Image.Image)
+                else image
+                for image in images
+            ]
+            assert self.processor is not None, "Processor is not initialized"
+            processed = self.processor(image_files)
+            encoded, attention_mask, metadata = self._process_nested_patches(processed)  # type: ignore[arg-type]
+
+        onnx_input = {"pixel_values": encoded, "attention_mask": attention_mask}
+        onnx_input = self._preprocess_onnx_image_input(onnx_input, **kwargs)
+        model_output = self.model.run(None, onnx_input)  # type: ignore[union-attr]
+
+        return OnnxOutputContext(
+            model_output=model_output[0],
+            attention_mask=attention_mask,  # type: ignore[arg-type]
+            metadata=metadata,
+        )
+
+    @staticmethod
+    def _process_nested_patches(
+        processed: list[list[NumpyArray]],
+    ) -> tuple[NumpyArray, NumpyArray, dict[str, Any]]:
+        """
+        Process nested image patches (from ImageSplitter).
+
+        Args:
+            processed: List of patch lists, one per image [[img1_patches], [img2_patches], ...]
+
+        Returns:
+            tuple: (encoded array, attention_mask, metadata)
+                - encoded: (batch_size, max_patches, C, H, W)
+                - attention_mask: (batch_size, max_patches) with 1 for real patches, 0 for padding
+                - metadata: Dict with 'patch_counts' key
+        """
+        patch_counts = [len(patches) for patches in processed]
+        max_patches = max(patch_counts)
+
+        # Get dimensions from first patch
+        channels, height, width = processed[0][0].shape
+        batch_size = len(processed)
+
+        # Create padded array
+        encoded = np.zeros(
+            (batch_size, max_patches, channels, height, width), dtype=processed[0][0].dtype
+        )
+
+        # Create attention mask (1 for real patches, 0 for padding)
+        attention_mask = np.zeros((batch_size, max_patches), dtype=np.int64)
+
+        # Fill in patches and attention mask
+        for i, patches in enumerate(processed):
+            for j, patch in enumerate(patches):
+                encoded[i, j] = patch
+                attention_mask[i, j] = 1
+
+        metadata = {"patch_counts": patch_counts}
+        return encoded, attention_mask, metadata  # type: ignore[return-value]
+
+    def _preprocess_onnx_image_input(
+        self, onnx_input: dict[str, np.ndarray], **kwargs: Any
+    ) -> dict[str, NumpyArray]:
+        """
+        Add text input placeholders for image data, following Idefics3 processing logic.
+
+        Constructs input_ids dynamically based on the actual number of image patches,
+        using the same token expansion logic as Idefics3Processor.
+
+        Args:
+            onnx_input: Dict with 'pixel_values' (batch, num_patches, C, H, W)
+                        and 'attention_mask' (batch, num_patches) indicating real patches
+            **kwargs: Additional arguments
+
+        Returns:
+            Updated onnx_input with 'input_ids' and updated 'attention_mask' for token sequence
+        """
+        # The attention_mask in onnx_input has a shape of (batch_size, num_patches),
+        # and should be used to create an attention mask matching the input_ids shape.
+        patch_attention_mask = onnx_input["attention_mask"]
+        pixel_values = onnx_input["pixel_values"]
+
+        batch_size = pixel_values.shape[0]
+        batch_input_ids = []
+
+        # Build input_ids for each image based on its actual patch count
+        for i in range(batch_size):
+            # Count real patches (non-padded) from attention mask
+            patch_count = int(np.sum(patch_attention_mask[i]))
+
+            # Compute rows/cols from patch count
+            rows, cols = self._compute_rows_cols_from_patches(patch_count)
+
+            # Build input_ids for this image
+            input_ids = self._build_input_ids_for_image(rows, cols)
+            batch_input_ids.append(input_ids)
+
+        # Pad sequences to max length in batch
+        max_len = max(len(ids) for ids in batch_input_ids)
+
+        # Get padding config from tokenizer
+        padding_direction = self.tokenizer.padding["direction"]  # type: ignore[index,union-attr]
+        pad_token_id = self.tokenizer.padding["pad_id"]  # type: ignore[index,union-attr]
+
+        # Initialize with pad token
+        padded_input_ids = np.full((batch_size, max_len), pad_token_id, dtype=np.int64)
+        attention_mask = np.zeros((batch_size, max_len), dtype=np.int64)
+
+        for i, input_ids in enumerate(batch_input_ids):
+            seq_len = len(input_ids)
+            if padding_direction == "left":
+                # Left padding: place tokens at the END of the array
+                start_idx = max_len - seq_len
+                padded_input_ids[i, start_idx:] = input_ids
+                attention_mask[i, start_idx:] = 1
+            else:
+                # Right padding: place tokens at the START of the array
+                padded_input_ids[i, :seq_len] = input_ids
+                attention_mask[i, :seq_len] = 1
+
+        onnx_input["input_ids"] = padded_input_ids
+        # Update attention_mask with token-level data
+        onnx_input["attention_mask"] = attention_mask
+        return onnx_input
+
+    @staticmethod
+    def _compute_rows_cols_from_patches(patch_count: int) -> tuple[int, int]:
+        if patch_count <= 1:
+            return 0, 0
+
+        # Subtract 1 for the global image
+        grid_patches = patch_count - 1
+
+        # Find rows and cols (assume square or near-square grid)
+        rows = int(grid_patches**0.5)
+        cols = grid_patches // rows
+
+        # Verify the calculation
+        if rows * cols + 1 != patch_count:
+            # Handle non-square grids
+            for r in range(1, grid_patches + 1):
+                if grid_patches % r == 0:
+                    c = grid_patches // r
+                    if r * c + 1 == patch_count:
+                        return r, c
+            # Fallback: treat as unsplit
+            return 0, 0
+
+        return rows, cols
+
+    def _create_single_image_prompt_string(self) -> str:
+        return (
+            "<fake_token_around_image>"
+            + "<global-img>"
+            + "<image>" * self.image_seq_len  # type: ignore[operator]
+            + "<fake_token_around_image>"
+        )
+
+    def _create_split_image_prompt_string(self, rows: int, cols: int) -> str:
+        text_split_images = ""
+
+        # Add tokens for each patch in the grid
+        for n_h in range(rows):
+            for n_w in range(cols):
+                text_split_images += (
+                    "<fake_token_around_image>"
+                    + f"<row_{n_h + 1}_col_{n_w + 1}>"
+                    + "<image>" * self.image_seq_len  # type: ignore[operator]
+                )
+            text_split_images += "\n"
+
+        # Add global image at the end
+        text_split_images += (
+            "\n<fake_token_around_image>"
+            + "<global-img>"
+            + "<image>" * self.image_seq_len  # type: ignore[operator]
+            + "<fake_token_around_image>"
+        )
+
+        return text_split_images
+
+    def _build_input_ids_for_image(self, rows: int, cols: int) -> np.ndarray:
+        # Create the appropriate image prompt string
+        if rows == 0 and cols == 0:
+            image_prompt_tokens = self._create_single_image_prompt_string()
+        else:
+            image_prompt_tokens = self._create_split_image_prompt_string(rows, cols)
+
+        # Replace <image> in visual prompt with expanded tokens
+        # The visual prompt is: "<|begin_of_text|>User:<image>Describe the image.<end_of_utterance>\nAssistant:"
+        expanded_prompt = self.VISUAL_PROMPT_PREFIX.replace("<image>", image_prompt_tokens)
+
+        # Tokenize the complete prompt
+        encoded = self.tokenizer.encode(expanded_prompt)  # type: ignore[union-attr]
+
+        # Convert to numpy array
+        return np.array(encoded.ids, dtype=np.int64)
+
+    def _post_process_onnx_image_output(
+        self,
+        output: OnnxOutputContext,
+    ) -> Iterable[NumpyArray]:
+        """
+        Post-process the ONNX model output to convert it into a usable format.
+
+        Args:
+            output (OnnxOutputContext): The raw output from the ONNX model.
+
+        Returns:
+            Iterable[NumpyArray]: Post-processed output as NumPy arrays.
+        """
+        assert self.model_description.dim is not None, "Model dim is not defined"
+        return output.model_output.reshape(
+            output.model_output.shape[0], -1, self.model_description.dim
+        )
+
+    def embed_text(
+        self,
+        documents: str | Iterable[str],
+        batch_size: int = 256,
+        parallel: Optional[int] = None,
+        **kwargs: Any,
+    ) -> Iterable[NumpyArray]:
+        """
+        Encode a list of documents into list of embeddings.
+
+        Args:
+            documents: Iterator of documents or single document to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        yield from self._embed_documents(
+            model_name=self.model_name,
+            cache_dir=str(self.cache_dir),
+            documents=documents,
+            batch_size=batch_size,
+            parallel=parallel,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_ids=self.device_ids,
+            local_files_only=self._local_files_only,
+            specific_model_path=self._specific_model_path,
+            extra_session_options=self._extra_session_options,
+            **kwargs,
+        )
+
+    def embed_image(
+        self,
+        images: ImageInput | Iterable[ImageInput],
+        batch_size: int = 16,
+        parallel: Optional[int] = None,
+        **kwargs: Any,
+    ) -> Iterable[NumpyArray]:
+        """
+        Encode a list of images into list of embeddings.
+
+        Args:
+            images: Iterator of image paths or single image path to embed
+            batch_size: Batch size for encoding -- higher values will use more memory, but be faster
+            parallel:
+                If > 1, data-parallel encoding will be used, recommended for offline encoding of large datasets.
+                If 0, use all available cores.
+                If None, don't use data-parallel processing, use default onnxruntime threading instead.
+
+        Returns:
+            List of embeddings, one per document
+        """
+        yield from self._embed_images(
+            model_name=self.model_name,
+            cache_dir=str(self.cache_dir),
+            images=images,
+            batch_size=batch_size,
+            parallel=parallel,
+            providers=self.providers,
+            cuda=self.cuda,
+            device_ids=self.device_ids,
+            local_files_only=self._local_files_only,
+            specific_model_path=self._specific_model_path,
+            extra_session_options=self._extra_session_options,
+            **kwargs,
+        )
+
+    @classmethod
+    def _get_text_worker_class(cls) -> Type[TextEmbeddingWorker[NumpyArray]]:
+        return ColModernVBERTTextEmbeddingWorker
+
+    @classmethod
+    def _get_image_worker_class(cls) -> Type[ImageEmbeddingWorker[NumpyArray]]:
+        return ColModernVBERTImageEmbeddingWorker
+
+
+class ColModernVBERTTextEmbeddingWorker(TextEmbeddingWorker[NumpyArray]):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> ColModernVBERT:
+        return ColModernVBERT(
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=1,
+            **kwargs,
+        )
+
+
+class ColModernVBERTImageEmbeddingWorker(ImageEmbeddingWorker[NumpyArray]):
+    def init_embedding(self, model_name: str, cache_dir: str, **kwargs: Any) -> ColModernVBERT:
+        return ColModernVBERT(
+            model_name=model_name,
+            cache_dir=cache_dir,
+            threads=1,
+            **kwargs,
+        )

fastembed/late_interaction_multimodal/colpali.py

@@ -1,11 +1,11 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from tokenizers import Encoding
 
 from fastembed.common import OnnxProvider, ImageInput
 from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common.utils import define_cache_dir, iter_batch
 from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding_base import (
     LateInteractionMultimodalEmbeddingBase,
@@ -46,14 +46,14 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -65,10 +65,11 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -87,7 +88,7 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -174,7 +175,7 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         batch_size: int = 1024,
         include_extension: bool = False,
         **kwargs: Any,
@@ -227,9 +228,9 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
 
     def embed_text(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -263,9 +264,9 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
 
     def embed_image(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """

fastembed/late_interaction_multimodal/late_interaction_multimodal_embedding.py

@@ -1,9 +1,10 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
 from fastembed.common import OnnxProvider, ImageInput
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.late_interaction_multimodal.colpali import ColPali
+from fastembed.late_interaction_multimodal.colmodernvbert import ColModernVBERT
 
 from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding_base import (
     LateInteractionMultimodalEmbeddingBase,
@@ -12,7 +13,10 @@ from fastembed.common.model_description import DenseModelDescription
 
 
 class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase):
-    EMBEDDINGS_REGISTRY: list[Type[LateInteractionMultimodalEmbeddingBase]] = [ColPali]
+    EMBEDDINGS_REGISTRY: list[Type[LateInteractionMultimodalEmbeddingBase]] = [
+        ColPali,
+        ColModernVBERT,
+    ]
 
     @classmethod
     def list_supported_models(cls) -> list[dict[str, Any]]:
@@ -54,11 +58,11 @@ class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase)
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -104,7 +108,7 @@ class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase)
             ValueError: If the model name is not found in the supported models.
         """
         descriptions = cls._list_supported_models()
-        embedding_size: Optional[int] = None
+        embedding_size: int | None = None
         for description in descriptions:
             if description.model.lower() == model_name.lower():
                 embedding_size = description.dim
@@ -119,9 +123,9 @@ class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase)
 
     def embed_text(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -142,9 +146,9 @@ class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase)
 
     def embed_image(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -165,7 +169,7 @@ class LateInteractionMultimodalEmbedding(LateInteractionMultimodalEmbeddingBase)
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         batch_size: int = 1024,
         include_extension: bool = False,
         **kwargs: Any,

fastembed/late_interaction_multimodal/late_interaction_multimodal_embedding_base.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Any
 
 
 from fastembed.common import ImageInput
@@ -11,21 +11,21 @@ class LateInteractionMultimodalEmbeddingBase(ModelManagement[DenseModelDescripti
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
         self.threads = threads
         self._local_files_only = kwargs.pop("local_files_only", False)
-        self._embedding_size: Optional[int] = None
+        self._embedding_size: int | None = None
 
     def embed_text(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -47,9 +47,9 @@ class LateInteractionMultimodalEmbeddingBase(ModelManagement[DenseModelDescripti
 
     def embed_image(
         self,
-        images: Union[ImageInput, Iterable[ImageInput]],
+        images: ImageInput | Iterable[ImageInput],
         batch_size: int = 16,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -79,7 +79,7 @@ class LateInteractionMultimodalEmbeddingBase(ModelManagement[DenseModelDescripti
 
     def token_count(
         self,
-        texts: Union[str, Iterable[str]],
+        texts: str | Iterable[str],
         **kwargs: Any,
     ) -> int:
         """Returns the number of tokens in the texts."""

fastembed/late_interaction_multimodal/onnx_multimodal_model.py

@@ -2,7 +2,7 @@ import contextlib
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from PIL import Image
@@ -11,19 +11,19 @@ from tokenizers import Encoding, Tokenizer
 from fastembed.common import OnnxProvider, ImageInput
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
 from fastembed.common.preprocessor_utils import load_tokenizer, load_preprocessor
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common.utils import iter_batch
 from fastembed.image.transform.operators import Compose
 from fastembed.parallel_processor import ParallelWorkerPool
 
 
 class OnnxMultimodalModel(OnnxModel[T]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
+    ONNX_OUTPUT_NAMES: list[str] | None = None
 
     def __init__(self) -> None:
         super().__init__()
-        self.tokenizer: Optional[Tokenizer] = None
-        self.processor: Optional[Compose] = None
+        self.tokenizer: Tokenizer | None = None
+        self.processor: Compose | None = None
         self.special_token_to_id: dict[str, int] = {}
 
     def _preprocess_onnx_text_input(
@@ -60,11 +60,11 @@ class OnnxMultimodalModel(OnnxModel[T]):
         self,
         model_dir: Path,
         model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        threads: int | None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_id: int | None = None,
+        extra_session_options: dict[str, Any] | None = None,
     ) -> None:
         super()._load_onnx_model(
             model_dir=model_dir,
@@ -116,15 +116,15 @@ class OnnxMultimodalModel(OnnxModel[T]):
         self,
         model_name: str,
         cache_dir: str,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        parallel: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        specific_model_path: str | None = None,
+        extra_session_options: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False
@@ -170,9 +170,11 @@ class OnnxMultimodalModel(OnnxModel[T]):
                 yield from self._post_process_onnx_text_output(batch)  # type: ignore
 
     def onnx_embed_image(self, images: list[ImageInput], **kwargs: Any) -> OnnxOutputContext:
-        with contextlib.ExitStack():
+        with contextlib.ExitStack() as stack:
             image_files = [
-                Image.open(image) if not isinstance(image, Image.Image) else image
+                stack.enter_context(Image.open(image))
+                if not isinstance(image, Image.Image)
+                else image
                 for image in images
             ]
             assert self.processor is not None, "Processor is not initialized"
@@ -187,15 +189,15 @@ class OnnxMultimodalModel(OnnxModel[T]):
         self,
         model_name: str,
         cache_dir: str,
-        images: Union[Iterable[ImageInput], ImageInput],
+        images: Iterable[ImageInput] | ImageInput,
         batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        parallel: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        specific_model_path: str | None = None,
+        extra_session_options: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False

fastembed/parallel_processor.py

@@ -8,8 +8,9 @@ from multiprocessing.context import BaseContext
 from multiprocessing.process import BaseProcess
 from multiprocessing.sharedctypes import Synchronized as BaseValue
 from queue import Empty
-from typing import Any, Iterable, Optional, Type
+from typing import Any, Iterable, Type
 
+from fastembed.common.types import Device
 
 # Single item should be processed in less than:
 processing_timeout = 10 * 60  # seconds
@@ -38,7 +39,7 @@ def _worker(
     output_queue: Queue,
     num_active_workers: BaseValue,
     worker_id: int,
-    kwargs: Optional[dict[str, Any]] = None,
+    kwargs: dict[str, Any] | None = None,
 ) -> None:
     """
     A worker that pulls data pints off the input queue, and places the execution result on the output queue.
@@ -93,21 +94,21 @@ class ParallelWorkerPool:
         self,
         num_workers: int,
         worker: Type[Worker],
-        start_method: Optional[str] = None,
-        device_ids: Optional[list[int]] = None,
-        cuda: bool = False,
+        start_method: str | None = None,
+        device_ids: list[int] | None = None,
+        cuda: bool | Device = Device.AUTO,
     ):
         self.worker_class = worker
         self.num_workers = num_workers
-        self.input_queue: Optional[Queue] = None
-        self.output_queue: Optional[Queue] = None
+        self.input_queue: Queue | None = None
+        self.output_queue: Queue | None = None
         self.ctx: BaseContext = get_context(start_method)
         self.processes: list[BaseProcess] = []
         self.queue_size = self.num_workers * max_internal_batch_size
         self.emergency_shutdown = False
         self.device_ids = device_ids
         self.cuda = cuda
-        self.num_active_workers: Optional[BaseValue] = None
+        self.num_active_workers: BaseValue | None = None
 
     def start(self, **kwargs: Any) -> None:
         self.input_queue = self.ctx.Queue(self.queue_size)
@@ -220,7 +221,7 @@ class ParallelWorkerPool:
                     f"Worker PID: {process.pid} terminated unexpectedly with code {process.exitcode}"
                 )
 
-    def join_or_terminate(self, timeout: Optional[int] = 1) -> None:
+    def join_or_terminate(self, timeout: int = 1) -> None:
         """
         Emergency shutdown
         @param timeout:

fastembed/postprocess/muvera.py

@@ -1,5 +1,3 @@
-from typing import Union
-
 import numpy as np
 
 from fastembed.common.types import NumpyArray
@@ -11,7 +9,7 @@ from fastembed.late_interaction_multimodal.late_interaction_multimodal_embedding
 )
 
 
-MultiVectorModel = Union[LateInteractionTextEmbeddingBase, LateInteractionMultimodalEmbeddingBase]
+MultiVectorModel = LateInteractionTextEmbeddingBase | LateInteractionMultimodalEmbeddingBase
 MAX_HAMMING_DISTANCE = 65  # 64 bits + 1
 POPCOUNT_LUT = np.array([bin(x).count("1") for x in range(256)], dtype=np.uint8)
 

fastembed/rerank/cross_encoder/custom_text_cross_encoder.py

@@ -1,7 +1,8 @@
-from typing import Optional, Sequence, Any
+from typing import Sequence, Any
 
 from fastembed.common import OnnxProvider
 from fastembed.common.model_description import BaseModelDescription
+from fastembed.common.types import Device
 from fastembed.rerank.cross_encoder.onnx_text_cross_encoder import OnnxTextCrossEncoder
 
 
@@ -11,14 +12,14 @@ class CustomTextCrossEncoder(OnnxTextCrossEncoder):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         super().__init__(

fastembed/rerank/cross_encoder/onnx_text_cross_encoder.py

@@ -1,9 +1,10 @@
-from typing import Any, Iterable, Optional, Sequence, Type
+from typing import Any, Iterable, Sequence, Type
 
 from loguru import logger
 
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.types import Device
 from fastembed.common.utils import define_cache_dir
 from fastembed.rerank.cross_encoder.onnx_text_model import (
     OnnxCrossEncoderModel,
@@ -77,14 +78,14 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -96,10 +97,11 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -124,7 +126,7 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
             )
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -180,7 +182,7 @@ class OnnxTextCrossEncoder(TextCrossEncoderBase, OnnxCrossEncoderModel):
         self,
         pairs: Iterable[tuple[str, str]],
         batch_size: int = 64,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[float]:
         yield from self._rerank_pairs(

fastembed/rerank/cross_encoder/onnx_text_model.py

@@ -1,7 +1,7 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from tokenizers import Encoding
@@ -12,14 +12,14 @@ from fastembed.common.onnx_model import (
     OnnxOutputContext,
     OnnxProvider,
 )
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common.preprocessor_utils import load_tokenizer
 from fastembed.common.utils import iter_batch
 from fastembed.parallel_processor import ParallelWorkerPool
 
 
 class OnnxCrossEncoderModel(OnnxModel[float]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
+    ONNX_OUTPUT_NAMES: list[str] | None = None
 
     @classmethod
     def _get_worker_class(cls) -> Type["TextRerankerWorker"]:
@@ -29,11 +29,11 @@ class OnnxCrossEncoderModel(OnnxModel[float]):
         self,
         model_dir: Path,
         model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        threads: int | None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_id: int | None = None,
+        extra_session_options: dict[str, Any] | None = None,
     ) -> None:
         super()._load_onnx_model(
             model_dir=model_dir,
@@ -92,13 +92,13 @@ class OnnxCrossEncoderModel(OnnxModel[float]):
         cache_dir: str,
         pairs: Iterable[tuple[str, str]],
         batch_size: int,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        parallel: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        specific_model_path: str | None = None,
+        extra_session_options: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[float]:
         is_small = False

fastembed/rerank/cross_encoder/text_cross_encoder.py

@@ -1,7 +1,8 @@
-from typing import Any, Iterable, Optional, Sequence, Type
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
 from fastembed.common import OnnxProvider
+from fastembed.common.types import Device
 from fastembed.rerank.cross_encoder.onnx_text_cross_encoder import OnnxTextCrossEncoder
 from fastembed.rerank.cross_encoder.custom_text_cross_encoder import CustomTextCrossEncoder
 
@@ -53,11 +54,11 @@ class TextCrossEncoder(TextCrossEncoderBase):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -102,7 +103,7 @@ class TextCrossEncoder(TextCrossEncoderBase):
         self,
         pairs: Iterable[tuple[str, str]],
         batch_size: int = 64,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[float]:
         """
@@ -140,7 +141,7 @@ class TextCrossEncoder(TextCrossEncoderBase):
         description: str = "",
         license: str = "",
         size_in_gb: float = 0.0,
-        additional_files: Optional[list[str]] = None,
+        additional_files: list[str] | None = None,
     ) -> None:
         registered_models = cls._list_supported_models()
         for registered_model in registered_models:

fastembed/rerank/cross_encoder/text_cross_encoder_base.py

@@ -1,4 +1,4 @@
-from typing import Any, Iterable, Optional
+from typing import Any, Iterable
 
 from fastembed.common.model_description import BaseModelDescription
 from fastembed.common.model_management import ModelManagement
@@ -8,8 +8,8 @@ class TextCrossEncoderBase(ModelManagement[BaseModelDescription]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
@@ -41,7 +41,7 @@ class TextCrossEncoderBase(ModelManagement[BaseModelDescription]):
         self,
         pairs: Iterable[tuple[str, str]],
         batch_size: int = 64,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[float]:
         """Rerank query-document pairs.

fastembed/sparse/bm25.py

@@ -2,7 +2,7 @@ import os
 from collections import defaultdict
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Type, Union
+from typing import Any, Iterable, Type
 
 import mmh3
 import numpy as np
@@ -91,14 +91,14 @@ class Bm25(SparseTextEmbeddingBase):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
+        cache_dir: str | None = None,
         k: float = 1.2,
         b: float = 0.75,
         avg_len: float = 256.0,
         language: str = "english",
         token_max_length: int = 40,
         disable_stemmer: bool = False,
-        specific_model_path: Optional[str] = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         super().__init__(model_name, cache_dir, **kwargs)
@@ -158,11 +158,11 @@ class Bm25(SparseTextEmbeddingBase):
         self,
         model_name: str,
         cache_dir: str,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
+        specific_model_path: str | None = None,
     ) -> Iterable[SparseEmbedding]:
         is_small = False
 
@@ -205,9 +205,9 @@ class Bm25(SparseTextEmbeddingBase):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
@@ -268,7 +268,7 @@ class Bm25(SparseTextEmbeddingBase):
             embeddings.append(SparseEmbedding.from_dict(token_id2value))
         return embeddings
 
-    def token_count(self, texts: Union[str, Iterable[str]], **kwargs: Any) -> int:
+    def token_count(self, texts: str | Iterable[str], **kwargs: Any) -> int:
         token_num = 0
         texts = [texts] if isinstance(texts, str) else texts
         for text in texts:
@@ -311,9 +311,7 @@ class Bm25(SparseTextEmbeddingBase):
     def compute_token_id(cls, token: str) -> int:
         return abs(mmh3.hash(token))
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """To emulate BM25 behaviour, we don't need to use weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
         """

fastembed/sparse/bm42.py

@@ -1,7 +1,7 @@
 import math
 import string
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import mmh3
 import numpy as np
@@ -9,6 +9,7 @@ from py_rust_stemmers import SnowballStemmer
 
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.types import Device
 from fastembed.common.utils import define_cache_dir
 from fastembed.sparse.sparse_embedding_base import (
     SparseEmbedding,
@@ -65,15 +66,15 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
         alpha: float = 0.5,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -87,10 +88,11 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             alpha (float, optional): Parameter, that defines the importance of the token weight in the document
                 versus the importance of the token frequency in the corpus. Defaults to 0.5, based on empirical testing.
                 It is recommended to only change this parameter based on training data for a specific dataset.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -110,7 +112,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -282,9 +284,9 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
@@ -325,9 +327,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             result[token_id] = 1.0
         return result
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         To emulate BM25 behaviour, we don't need to use smart weights in the query, and
         it's enough to just hash the tokens and assign a weight of 1.0 to them.
@@ -353,7 +353,7 @@ class Bm42(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         return Bm42TextEmbeddingWorker
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         if not hasattr(self, "model") or self.model is None:
             self.load_onnx_model()  # loads the tokenizer as well

fastembed/sparse/minicoil.py

@@ -1,6 +1,6 @@
 from pathlib import Path
 
-from typing import Any, Optional, Sequence, Iterable, Union, Type
+from typing import Any, Sequence, Iterable, Type
 
 import numpy as np
 from numpy.typing import NDArray
@@ -10,6 +10,7 @@ from tokenizers import Tokenizer
 from fastembed.common.model_description import SparseModelDescription, ModelSource
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common import OnnxProvider
+from fastembed.common.types import Device
 from fastembed.common.utils import define_cache_dir
 from fastembed.sparse.sparse_embedding_base import (
     SparseEmbedding,
@@ -72,17 +73,17 @@ class MiniCOIL(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
         k: float = 1.2,
         b: float = 0.75,
         avg_len: float = 150.0,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -98,10 +99,11 @@ class MiniCOIL(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             b (float, optional): The b parameter in the BM25 formula. Defines the importance of the document length.
                 Defaults to 0.75.
             avg_len (float, optional): The average length of the documents in the corpus. Defaults to 150.0.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -124,15 +126,15 @@ class MiniCOIL(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.avg_len = avg_len
 
         # Initialize class attributes
-        self.tokenizer: Optional[Tokenizer] = None
+        self.tokenizer: Tokenizer | None = None
         self.invert_vocab: dict[int, str] = {}
         self.special_tokens: set[str] = set()
         self.special_tokens_ids: set[int] = set()
         self.stopwords: set[str] = set()
-        self.vocab_resolver: Optional[VocabResolver] = None
-        self.encoder: Optional[Encoder] = None
-        self.output_dim: Optional[int] = None
-        self.sparse_vector_converter: Optional[SparseVectorConverter] = None
+        self.vocab_resolver: VocabResolver | None = None
+        self.encoder: Encoder | None = None
+        self.output_dim: int | None = None
+        self.sparse_vector_converter: SparseVectorConverter | None = None
 
         self.model_description = self._get_model_description(model_name)
         self.cache_dir = str(define_cache_dir(cache_dir))
@@ -188,15 +190,15 @@ class MiniCOIL(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         )
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         return self._token_count(texts, batch_size=batch_size, **kwargs)
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
@@ -233,9 +235,7 @@ class MiniCOIL(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             **kwargs,
         )
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         Encode a list of queries into list of embeddings.
         """

fastembed/sparse/sparse_embedding_base.py

@@ -1,5 +1,5 @@
 from dataclasses import dataclass
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Any
 
 import numpy as np
 from numpy.typing import NDArray
@@ -12,7 +12,7 @@ from fastembed.common.model_management import ModelManagement
 @dataclass
 class SparseEmbedding:
     values: NumpyArray
-    indices: Union[NDArray[np.int64], NDArray[np.int32]]
+    indices: NDArray[np.int64] | NDArray[np.int32]
 
     def as_object(self) -> dict[str, NumpyArray]:
         return {
@@ -35,8 +35,8 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
@@ -46,9 +46,9 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         raise NotImplementedError()
@@ -68,9 +68,7 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
 
@@ -87,6 +85,6 @@ class SparseTextEmbeddingBase(ModelManagement[SparseModelDescription]):
         else:
             yield from self.embed(query, **kwargs)
 
-    def token_count(self, texts: Union[str, Iterable[str]], **kwargs: Any) -> int:
+    def token_count(self, texts: str | Iterable[str], **kwargs: Any) -> int:
         """Returns the number of tokens in the texts."""
         raise NotImplementedError("Subclasses must implement this method")

fastembed/sparse/sparse_text_embedding.py

@@ -1,7 +1,8 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
 from fastembed.common import OnnxProvider
+from fastembed.common.types import Device
 from fastembed.sparse.bm25 import Bm25
 from fastembed.sparse.bm42 import Bm42
 from fastembed.sparse.minicoil import MiniCOIL
@@ -53,11 +54,11 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -93,9 +94,9 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """
@@ -115,9 +116,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(
-        self, query: Union[str, Iterable[str]], **kwargs: Any
-    ) -> Iterable[SparseEmbedding]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[SparseEmbedding]:
         """
         Embeds queries
 
@@ -130,7 +129,7 @@ class SparseTextEmbedding(SparseTextEmbeddingBase):
         yield from self.model.query_embed(query, **kwargs)
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         """Returns the number of tokens in the texts.
 

fastembed/sparse/splade_pp.py

@@ -1,8 +1,9 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from fastembed.common import OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
+from fastembed.common.types import Device
 from fastembed.common.utils import define_cache_dir
 from fastembed.sparse.sparse_embedding_base import (
     SparseEmbedding,
@@ -54,7 +55,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             yield SparseEmbedding(values=scores, indices=indices)
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         return self._token_count(texts, batch_size=batch_size, **kwargs)
 
@@ -70,14 +71,14 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -89,10 +90,11 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -111,7 +113,7 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -144,9 +146,9 @@ class SpladePP(SparseTextEmbeddingBase, OnnxTextModel[SparseEmbedding]):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[SparseEmbedding]:
         """

fastembed/sparse/utils/sparse_vectors_converter.py

@@ -1,12 +1,11 @@
-from typing import Dict, List, Set
-from py_rust_stemmers import SnowballStemmer
-from fastembed.common.utils import get_all_punctuation, remove_non_alphanumeric
-import mmh3
 import copy
 from dataclasses import dataclass
 
+import mmh3
 import numpy as np
+from py_rust_stemmers import SnowballStemmer
 
+from fastembed.common.utils import get_all_punctuation, remove_non_alphanumeric
 from fastembed.sparse.sparse_embedding_base import SparseEmbedding
 
 GAP = 32000
@@ -16,16 +15,16 @@ INT32_MAX = 2**31 - 1
 @dataclass
 class WordEmbedding:
     word: str
-    forms: List[str]
+    forms: list[str]
     count: int
     word_id: int
-    embedding: List[float]
+    embedding: list[float]
 
 
 class SparseVectorConverter:
     def __init__(
         self,
-        stopwords: Set[str],
+        stopwords: set[str],
         stemmer: SnowballStemmer,
         k: float = 1.2,
         b: float = 0.75,
@@ -58,15 +57,15 @@ class SparseVectorConverter:
         return res
 
     @classmethod
-    def normalize_vector(cls, vector: List[float]) -> List[float]:
+    def normalize_vector(cls, vector: list[float]) -> list[float]:
         norm = sum([x**2 for x in vector]) ** 0.5
         if norm < 1e-8:
             return vector
         return [x / norm for x in vector]
 
     def clean_words(
-        self, sentence_embedding: Dict[str, WordEmbedding], token_max_length: int = 40
-    ) -> Dict[str, WordEmbedding]:
+        self, sentence_embedding: dict[str, WordEmbedding], token_max_length: int = 40
+    ) -> dict[str, WordEmbedding]:
         """
         Clean miniCOIL-produced sentence_embedding, as unknown to the miniCOIL's stemmer tokens should fully resemble
         our BM25 token representation.
@@ -85,7 +84,7 @@ class SparseVectorConverter:
                 }
         """
 
-        new_sentence_embedding: Dict[str, WordEmbedding] = {}
+        new_sentence_embedding: dict[str, WordEmbedding] = {}
 
         for word, embedding in sentence_embedding.items():
             # embedding = {
@@ -127,7 +126,7 @@ class SparseVectorConverter:
 
     def embedding_to_vector(
         self,
-        sentence_embedding: Dict[str, WordEmbedding],
+        sentence_embedding: dict[str, WordEmbedding],
         embedding_size: int,
         vocab_size: int,
     ) -> SparseEmbedding:
@@ -156,14 +155,14 @@ class SparseVectorConverter:
 
         """
 
-        indices: List[int] = []
-        values: List[float] = []
-        
+        indices: list[int] = []
+        values: list[float] = []
+
         # Example:
         # vocab_size = 10000
         # embedding_size = 4
         # GAP = 32000
-        # 
+        #
         # We want to start random words section from the bucket, that is guaranteed to not
         # include any vocab words.
         # We need (vocab_size * embedding_size) slots for vocab words.
@@ -171,9 +170,7 @@ class SparseVectorConverter:
         # Therefore, we can start random words from bucket (vocab_size * embedding_size) // GAP + 1 + 1
 
         # ID at which the scope of OOV words starts
-        unknown_words_shift = (
-            (vocab_size * embedding_size) // GAP + 2
-        ) * GAP
+        unknown_words_shift = ((vocab_size * embedding_size) // GAP + 2) * GAP
         sentence_embedding_cleaned = self.clean_words(sentence_embedding)
 
         # Calculate sentence length after cleaning
@@ -208,7 +205,7 @@ class SparseVectorConverter:
 
     def embedding_to_vector_query(
         self,
-        sentence_embedding: Dict[str, WordEmbedding],
+        sentence_embedding: dict[str, WordEmbedding],
         embedding_size: int,
         vocab_size: int,
     ) -> SparseEmbedding:
@@ -216,8 +213,8 @@ class SparseVectorConverter:
         Same as `embedding_to_vector`, but no TF
         """
 
-        indices: List[int] = []
-        values: List[float] = []
+        indices: list[int] = []
+        values: list[float] = []
 
         # ID at which the scope of OOV words starts
         unknown_words_shift = ((vocab_size * embedding_size) // GAP + 2) * GAP

fastembed/text/custom_text_embedding.py

@@ -1,5 +1,4 @@
-from typing import Optional, Sequence, Any, Iterable
-
+from typing import Sequence, Any, Iterable
 from dataclasses import dataclass
 
 import numpy as np
@@ -11,7 +10,7 @@ from fastembed.common.model_description import (
     DenseModelDescription,
 )
 from fastembed.common.onnx_model import OnnxOutputContext
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, Device
 from fastembed.common.utils import normalize, mean_pooling
 from fastembed.text.onnx_embedding import OnnxTextEmbedding
 
@@ -29,14 +28,14 @@ class CustomTextEmbedding(OnnxTextEmbedding):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         super().__init__(
@@ -64,7 +63,7 @@ class CustomTextEmbedding(OnnxTextEmbedding):
         return self._normalize(self._pool(output.model_output, output.attention_mask))
 
     def _pool(
-        self, embeddings: NumpyArray, attention_mask: Optional[NDArray[np.int64]] = None
+        self, embeddings: NumpyArray, attention_mask: NDArray[np.int64] | None = None
     ) -> NumpyArray:
         if self._pooling == PoolingType.CLS:
             return embeddings[:, 0]

fastembed/text/multitask_embedding.py

@@ -1,5 +1,5 @@
 from enum import Enum
-from typing import Any, Type, Iterable, Union, Optional
+from typing import Any, Type, Iterable
 
 import numpy as np
 
@@ -45,11 +45,9 @@ class JinaEmbeddingV3(PooledNormalizedEmbedding):
     PASSAGE_TASK = Task.RETRIEVAL_PASSAGE
     QUERY_TASK = Task.RETRIEVAL_QUERY
 
-    def __init__(self, *args: Any, task_id: Optional[int] = None, **kwargs: Any):
+    def __init__(self, *args: Any, task_id: int | None = None, **kwargs: Any):
         super().__init__(*args, **kwargs)
-        self.default_task_id: Union[Task, int] = (
-            task_id if task_id is not None else self.PASSAGE_TASK
-        )
+        self.default_task_id: Task | int = task_id if task_id is not None else self.PASSAGE_TASK
 
     @classmethod
     def _get_worker_class(cls) -> Type[OnnxTextEmbeddingWorker]:
@@ -62,7 +60,7 @@ class JinaEmbeddingV3(PooledNormalizedEmbedding):
     def _preprocess_onnx_input(
         self,
         onnx_input: dict[str, NumpyArray],
-        task_id: Optional[Union[int, Task]] = None,
+        task_id: int | Task | None = None,
         **kwargs: Any,
     ) -> dict[str, NumpyArray]:
         if task_id is None:
@@ -72,10 +70,10 @@ class JinaEmbeddingV3(PooledNormalizedEmbedding):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
-        task_id: Optional[int] = None,
+        parallel: int | None = None,
+        task_id: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         task_id = (
@@ -83,7 +81,7 @@ class JinaEmbeddingV3(PooledNormalizedEmbedding):
         )  # required for multiprocessing
         yield from super().embed(documents, batch_size, parallel, task_id=task_id, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         yield from super().embed(query, task_id=self.QUERY_TASK, **kwargs)
 
     def passage_embed(self, texts: Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:

fastembed/text/onnx_embedding.py

@@ -1,6 +1,6 @@
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+from fastembed.common.types import NumpyArray, OnnxProvider, Device
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
 from fastembed.text.onnx_text_model import OnnxTextModel, TextEmbeddingWorker
@@ -199,14 +199,14 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
     def __init__(
         self,
         model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
-        device_id: Optional[int] = None,
-        specific_model_path: Optional[str] = None,
+        device_id: int | None = None,
+        specific_model_path: str | None = None,
         **kwargs: Any,
     ):
         """
@@ -218,10 +218,11 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
             threads (int, optional): The number of threads single onnxruntime session can use. Defaults to None.
             providers (Optional[Sequence[OnnxProvider]], optional): The list of onnxruntime providers to use.
                 Mutually exclusive with the `cuda` and `device_ids` arguments. Defaults to None.
-            cuda (bool, optional): Whether to use cuda for inference. Mutually exclusive with `providers`
-                Defaults to False.
+            cuda (Union[bool, Device], optional): Whether to use cuda for inference. Mutually exclusive with `providers`
+                Defaults to Device.AUTO.
             device_ids (Optional[list[int]], optional): The list of device ids to use for data parallel processing in
-                workers. Should be used with `cuda=True`, mutually exclusive with `providers`. Defaults to None.
+                workers. Should be used with `cuda` equals to `True`, `Device.AUTO` or `Device.CUDA`, mutually exclusive
+                with `providers`. Defaults to None.
             lazy_load (bool, optional): Whether to load the model during class initialization or on demand.
                 Should be set to True when using multiple-gpu and parallel encoding. Defaults to False.
             device_id (Optional[int], optional): The device id to use for loading the model in the worker process.
@@ -239,7 +240,7 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         self.cuda = cuda
 
         # This device_id will be used if we need to load model in current process
-        self.device_id: Optional[int] = None
+        self.device_id: int | None = None
         if device_id is not None:
             self.device_id = device_id
         elif self.device_ids is not None:
@@ -260,9 +261,9 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -332,7 +333,7 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
         )
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         return self._token_count(texts, batch_size=batch_size, **kwargs)
 

fastembed/text/onnx_text_model.py

@@ -1,13 +1,13 @@
 import os
 from multiprocessing import get_all_start_methods
 from pathlib import Path
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 
 import numpy as np
 from numpy.typing import NDArray
 from tokenizers import Encoding, Tokenizer
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+from fastembed.common.types import NumpyArray, OnnxProvider, Device
 from fastembed.common.onnx_model import EmbeddingWorker, OnnxModel, OnnxOutputContext, T
 from fastembed.common.preprocessor_utils import load_tokenizer
 from fastembed.common.utils import iter_batch
@@ -15,7 +15,7 @@ from fastembed.parallel_processor import ParallelWorkerPool
 
 
 class OnnxTextModel(OnnxModel[T]):
-    ONNX_OUTPUT_NAMES: Optional[list[str]] = None
+    ONNX_OUTPUT_NAMES: list[str] | None = None
 
     @classmethod
     def _get_worker_class(cls) -> Type["TextEmbeddingWorker[T]"]:
@@ -35,12 +35,12 @@ class OnnxTextModel(OnnxModel[T]):
 
     def __init__(self) -> None:
         super().__init__()
-        self.tokenizer: Optional[Tokenizer] = None
+        self.tokenizer: Tokenizer | None = None
         self.special_token_to_id: dict[str, int] = {}
 
     def _preprocess_onnx_input(
         self, onnx_input: dict[str, NumpyArray], **kwargs: Any
-    ) -> dict[str, Union[NumpyArray, NDArray[np.int64]]]:
+    ) -> dict[str, NumpyArray | NDArray[np.int64]]:
         """
         Preprocess the onnx input.
         """
@@ -50,11 +50,11 @@ class OnnxTextModel(OnnxModel[T]):
         self,
         model_dir: Path,
         model_file: str,
-        threads: Optional[int],
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_id: Optional[int] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        threads: int | None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_id: int | None = None,
+        extra_session_options: dict[str, Any] | None = None,
     ) -> None:
         super()._load_onnx_model(
             model_dir=model_dir,
@@ -104,15 +104,15 @@ class OnnxTextModel(OnnxModel[T]):
         self,
         model_name: str,
         cache_dir: str,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        parallel: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         local_files_only: bool = False,
-        specific_model_path: Optional[str] = None,
-        extra_session_options: Optional[dict[str, Any]] = None,
+        specific_model_path: str | None = None,
+        extra_session_options: dict[str, Any] | None = None,
         **kwargs: Any,
     ) -> Iterable[T]:
         is_small = False
@@ -159,9 +159,7 @@ class OnnxTextModel(OnnxModel[T]):
             for batch in pool.ordered_map(iter_batch(documents, batch_size), **params):
                 yield from self._post_process_onnx_output(batch, **kwargs)  # type: ignore
 
-    def _token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **_: Any
-    ) -> int:
+    def _token_count(self, texts: str | Iterable[str], batch_size: int = 1024, **_: Any) -> int:
         if not hasattr(self, "model") or self.model is None:
             self.load_onnx_model()  # loads the tokenizer as well
 

fastembed/text/text_embedding.py

@@ -1,8 +1,8 @@
 import warnings
-from typing import Any, Iterable, Optional, Sequence, Type, Union
+from typing import Any, Iterable, Sequence, Type
 from dataclasses import asdict
 
-from fastembed.common.types import NumpyArray, OnnxProvider
+from fastembed.common.types import NumpyArray, OnnxProvider, Device
 from fastembed.text.clip_embedding import CLIPOnnxEmbedding
 from fastembed.text.custom_text_embedding import CustomTextEmbedding
 from fastembed.text.pooled_normalized_embedding import PooledNormalizedEmbedding
@@ -51,7 +51,7 @@ class TextEmbedding(TextEmbeddingBase):
         description: str = "",
         license: str = "",
         size_in_gb: float = 0.0,
-        additional_files: Optional[list[str]] = None,
+        additional_files: list[str] | None = None,
     ) -> None:
         registered_models = cls._list_supported_models()
         for registered_model in registered_models:
@@ -79,11 +79,11 @@ class TextEmbedding(TextEmbeddingBase):
     def __init__(
         self,
         model_name: str = "BAAI/bge-small-en-v1.5",
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
-        providers: Optional[Sequence[OnnxProvider]] = None,
-        cuda: bool = False,
-        device_ids: Optional[list[int]] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
+        providers: Sequence[OnnxProvider] | None = None,
+        cuda: bool | Device = Device.AUTO,
+        device_ids: list[int] | None = None,
         lazy_load: bool = False,
         **kwargs: Any,
     ):
@@ -149,7 +149,7 @@ class TextEmbedding(TextEmbeddingBase):
             ValueError: If the model name is not found in the supported models.
         """
         descriptions = cls._list_supported_models()
-        embedding_size: Optional[int] = None
+        embedding_size: int | None = None
         for description in descriptions:
             if description.model.lower() == model_name.lower():
                 embedding_size = description.dim
@@ -164,9 +164,9 @@ class TextEmbedding(TextEmbeddingBase):
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         """
@@ -186,7 +186,7 @@ class TextEmbedding(TextEmbeddingBase):
         """
         yield from self.model.embed(documents, batch_size, parallel, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         """
         Embeds queries
 
@@ -214,7 +214,7 @@ class TextEmbedding(TextEmbeddingBase):
         yield from self.model.passage_embed(texts, **kwargs)
 
     def token_count(
-        self, texts: Union[str, Iterable[str]], batch_size: int = 1024, **kwargs: Any
+        self, texts: str | Iterable[str], batch_size: int = 1024, **kwargs: Any
     ) -> int:
         """Returns the number of tokens in the texts.
 

fastembed/text/text_embedding_base.py

@@ -1,4 +1,4 @@
-from typing import Iterable, Optional, Union, Any
+from typing import Iterable, Any
 
 from fastembed.common.model_description import DenseModelDescription
 from fastembed.common.types import NumpyArray
@@ -9,21 +9,21 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
     def __init__(
         self,
         model_name: str,
-        cache_dir: Optional[str] = None,
-        threads: Optional[int] = None,
+        cache_dir: str | None = None,
+        threads: int | None = None,
         **kwargs: Any,
     ):
         self.model_name = model_name
         self.cache_dir = cache_dir
         self.threads = threads
         self._local_files_only = kwargs.pop("local_files_only", False)
-        self._embedding_size: Optional[int] = None
+        self._embedding_size: int | None = None
 
     def embed(
         self,
-        documents: Union[str, Iterable[str]],
+        documents: str | Iterable[str],
         batch_size: int = 256,
-        parallel: Optional[int] = None,
+        parallel: int | None = None,
         **kwargs: Any,
     ) -> Iterable[NumpyArray]:
         raise NotImplementedError()
@@ -43,7 +43,7 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
         # This is model-specific, so that different models can have specialized implementations
         yield from self.embed(texts, **kwargs)
 
-    def query_embed(self, query: Union[str, Iterable[str]], **kwargs: Any) -> Iterable[NumpyArray]:
+    def query_embed(self, query: str | Iterable[str], **kwargs: Any) -> Iterable[NumpyArray]:
         """
         Embeds queries
 
@@ -70,6 +70,6 @@ class TextEmbeddingBase(ModelManagement[DenseModelDescription]):
         """Returns embedding size for the current model"""
         raise NotImplementedError("Subclasses must implement this method")
 
-    def token_count(self, texts: Union[str, Iterable[str]], **kwargs: Any) -> int:
+    def token_count(self, texts: str | Iterable[str], **kwargs: Any) -> int:
         """Returns the number of tokens in the texts."""
         raise NotImplementedError("Subclasses must implement this method")

mkdocs.yml

@@ -13,6 +13,7 @@ copyright: |
 theme:
   name: material
   logo: assets/favicon.png
+  favicon: assets/favicon.png
   custom_dir: docs/overrides
   icon:
     repo: fontawesome/brands/github

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
-name = "fastembed"
-version = "0.7.4"
+name = "fastembed-gpu"
+version = "0.8.0"
 description = "Fast, light, accurate library built for retrieval embedding generation"
 authors = ["Qdrant Team <info@qdrant.tech>", "NirantK <nirant.bits@gmail.com>"]
 license = "Apache License"
@@ -11,19 +11,19 @@ repository = "https://github.com/qdrant/fastembed"
 keywords = ["vector", "embedding", "neural", "search", "qdrant", "sentence-transformers"]
 
 [tool.poetry.dependencies]
-python = ">=3.9.0"
+python = ">=3.10.0"
 numpy = [
-    { version = ">=1.21,<2.1.0", python = "<3.10" },
-    { version = ">=1.21,<2.3.0", python = ">=3.10,<3.11" },
-    { version = ">=1.21", python = ">=3.11,<3.12" },
-    { version = ">=1.26", python = ">=3.12,<3.13" },
-    { version = ">=2.1.0", python = ">=3.13,<3.14" },
+    { version = ">=1.21,<2.3.0", python = "3.10" },
+    { version = ">=1.21", python = "3.11" },
+    { version = ">=1.26", python = "3.12" },
+    { version = ">=2.1.0", python = "3.13" },
     { version = ">=2.3.0", python = ">=3.14" },
 ]
-onnxruntime = [
-    { version = ">=1.17.0,<1.20.0", python = "<3.10" },
-    { version = ">1.20.0", python = ">=3.13" },
-    { version = ">=1.17.0,!=1.20.0", python = ">=3.10,<3.13" },
+onnxruntime-gpu = [
+    { version = ">=1.17.0,!=1.20.0,<1.24", python = "3.10" },
+    { version = ">=1.17.0,!=1.20.0,!=1.24.0,!=1.24.1", python = ">=3.11,<3.13" },
+    { version = ">1.21.0,!=1.24.0,!=1.24.1", python = "3.13" },
+    { version = ">=1.24.2", python = ">=3.14" },
 ]
 tqdm = "^4.66"
 requests = "^2.31"
@@ -31,9 +31,9 @@ tokenizers = ">=0.15,<1.0"
 huggingface-hub = ">=0.20,<2.0"
 loguru = "^0.7.2"
 pillow = [
-    { version = ">=10.3.0,<11.0", python = "<3.10" },
-    { version = ">=10.3.0,<12.0", python = ">=3.10,<3.13" },
-    { version = ">=11.0.0,<12.0", python = ">=3.13" },
+    { version = ">=10.3.0,<13.0", python = ">=3.10,<3.13" },
+    { version = ">=11.0.0,<13.0", python = "3.13" },
+    { version = ">=12.0.0,<13.0", python = ">=3.14" },
 ]
 mmh3 = ">=4.1.0,<6.0.0"
 py-rust-stemmers = "^0.1.0"
@@ -46,8 +46,9 @@ ruff = ">=0.3.1,<1.0"
 notebook = ">=7.0.2"
 pre-commit = "^3.6.2"
 onnx = [
-    { version = ">=1.15.0", python = "<3.13" },
-    { version = ">=1.18.0", python = ">=3.13" },
+    { version = ">=1.15.0", python = ">=3.10,<3.13" },
+    { version = ">=1.18.0", python = "3.13" },
+    { version = ">=1.20.0", python = ">=3.14" },
 ]
 
 [tool.poetry.group.docs.dependencies]

tests/test_late_interaction_multimodal.py

@@ -1,4 +1,5 @@
 import os
+from contextlib import contextmanager
 
 import pytest
 from PIL import Image
@@ -6,7 +7,7 @@ import numpy as np
 
 from fastembed import LateInteractionMultimodalEmbedding
 from tests.config import TEST_MISC_DIR
-
+from tests.utils import delete_model_cache
 
 # vectors are abridged and rounded for brevity
 CANONICAL_IMAGE_VALUES = {
@@ -21,6 +22,17 @@ CANONICAL_IMAGE_VALUES = {
             [-0.1299, -0.0691, 0.1097, 0.0728, 0.0123, 0.0519, 0.0122],
         ]
     ),
+    "Qdrant/colmodernvbert": np.array(
+        [
+            [0.11614, -0.15793, -0.11194, 0.0688, 0.08001, 0.10575, -0.07871],
+            [0.10094, -0.13301, -0.12069, 0.10932, 0.04645, 0.09884, 0.04048],
+            [0.13106, -0.18613, -0.13469, 0.10566, 0.03659, 0.07712, -0.03916],
+            [0.09754, -0.09596, -0.04839, 0.14991, 0.05692, 0.10569, -0.08349],
+            [0.02576, -0.15651, -0.09977, 0.09707, 0.13412, 0.09994, -0.09931],
+            [-0.06741, -0.1787, -0.19677, -0.07618, 0.13102, -0.02131, -0.02437],
+            [-0.02776, -0.10187, -0.13793, 0.03835, 0.04766, 0.04701, -0.15635],
+        ]
+    ),
 }
 
 CANONICAL_QUERY_VALUES = {
@@ -35,6 +47,17 @@ CANONICAL_QUERY_VALUES = {
             [-0.0165, -0.0106, 0.1672, -0.0768, 0.0389, -0.0038, 0.1137],
         ]
     ),
+    "Qdrant/colmodernvbert": np.array(
+        [
+            [0.05, 0.06557, 0.04026, 0.14981, 0.1842, 0.0263, -0.18706],
+            [-0.05664, -0.14028, 0.00649, -0.02849, 0.09034, -0.01494, 0.10693],
+            [-0.10147, -0.00716, 0.09084, -0.08236, -0.01849, -0.00972, -0.00461],
+            [-0.1233, -0.10814, -0.02337, -0.00329, 0.05984, 0.09934, 0.09846],
+            [-0.07053, -0.13119, -0.06487, 0.01508, 0.07459, 0.07655, 0.14821],
+            [0.00526, -0.13842, -0.05837, -0.02721, 0.13009, 0.05076, 0.17962],
+            [0.00924, -0.14383, -0.03057, -0.03691, 0.11718, 0.037, 0.13344],
+        ]
+    ),
 }
 
 queries = ["hello world", "flag embedding"]
@@ -44,43 +67,69 @@ images = [
     Image.open((TEST_MISC_DIR / "image.jpeg")),
 ]
 
+_MODELS_TO_CACHE = ("Qdrant/colmodernvbert",)
+MODELS_TO_CACHE = tuple(model_name.lower() for model_name in _MODELS_TO_CACHE)
 
-def test_batch_embedding():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
 
+@pytest.fixture(scope="module")
+def model_cache():
+    is_ci = os.getenv("CI")
+    cache = {}
+
+    @contextmanager
+    def get_model(model_name: str):
+        lowercase_model_name = model_name.lower()
+        if lowercase_model_name not in cache:
+            cache[lowercase_model_name] = LateInteractionMultimodalEmbedding(lowercase_model_name)
+        yield cache[lowercase_model_name]
+        if lowercase_model_name not in MODELS_TO_CACHE:
+            model_inst = cache.pop(lowercase_model_name)
+            if is_ci:
+                delete_model_cache(model_inst.model._model_dir)
+            del model_inst
+
+    yield get_model
+
+    if is_ci:
+        for _, model in cache.items():
+            delete_model_cache(model.model._model_dir)
+    cache.clear()
+
+
+def test_batch_embedding(model_cache):
     for model_name, expected_result in CANONICAL_IMAGE_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = list(model.embed_image(images, batch_size=2))
+        if model_name.lower() == "Qdrant/colpali-v1.3-fp16".lower() and os.getenv("CI"):
+            continue  # colpali is too large for ci
 
-        for value in result:
+        print("evaluating", model_name)
+        with model_cache(model_name) as model:
+            result = list(model.embed_image(images, batch_size=2))
+
+            for value in result:
+                token_num, abridged_dim = expected_result.shape
+                assert np.allclose(value[:token_num, :abridged_dim], expected_result, atol=2e-3)
+
+
+def test_single_embedding(model_cache):
+    for model_name, expected_result in CANONICAL_IMAGE_VALUES.items():
+        if model_name.lower() == "Qdrant/colpali-v1.3-fp16".lower() and os.getenv("CI"):
+            continue  # colpali is too large for ci
+        print("evaluating", model_name)
+        with model_cache(model_name) as model:
+            result = next(iter(model.embed_image(images, batch_size=6)))
             token_num, abridged_dim = expected_result.shape
-            assert np.allclose(value[:token_num, :abridged_dim], expected_result, atol=2e-3)
+            assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
 
 
-def test_single_embedding():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-
-    for model_name, expected_result in CANONICAL_IMAGE_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = next(iter(model.embed_image(images, batch_size=6)))
-        token_num, abridged_dim = expected_result.shape
-        assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
-
-
-def test_single_embedding_query():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-
+def test_single_embedding_query(model_cache):
     for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
+        if model_name.lower() == "Qdrant/colpali-v1.3-fp16".lower() and os.getenv("CI"):
+            continue  # colpali is too large for ci
         print("evaluating", model_name)
-        model = LateInteractionMultimodalEmbedding(model_name=model_name)
-        result = next(iter(model.embed_text(queries)))
-        token_num, abridged_dim = expected_result.shape
-        assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
+        with model_cache(model_name) as model:
+            result = next(iter(model.embed_text(queries)))
+            token_num, abridged_dim = expected_result.shape
+            assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
 
 
 def test_get_embedding_size():
@@ -90,33 +139,27 @@ def test_get_embedding_size():
     model_name = "Qdrant/ColPali-v1.3-fp16"
     assert LateInteractionMultimodalEmbedding.get_embedding_size(model_name) == 128
 
+    model_name = "Qdrant/colmodernvbert"
+    assert LateInteractionMultimodalEmbedding.get_embedding_size(model_name) == 128
+
 
 def test_embedding_size():
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-    model_name = "Qdrant/colpali-v1.3-fp16"
-    model = LateInteractionMultimodalEmbedding(model_name=model_name, lazy_load=True)
-    assert model.embedding_size == 128
-
-    model_name = "Qdrant/ColPali-v1.3-fp16"
+    model_name = "Qdrant/colmodernvbert"
     model = LateInteractionMultimodalEmbedding(model_name=model_name, lazy_load=True)
     assert model.embedding_size == 128
 
 
-def test_token_count() -> None:
-    if os.getenv("CI"):
-        pytest.skip("Colpali is too large to test in CI")
-    model_name = "Qdrant/colpali-v1.3-fp16"
-    model = LateInteractionMultimodalEmbedding(model_name=model_name, lazy_load=True)
-
-    documents = ["short doc", "it is a long document to check attention mask for paddings"]
-    short_doc_token_count = model.token_count(documents[0])
-    long_doc_token_count = model.token_count(documents[1])
-    documents_token_count = model.token_count(documents)
-    assert short_doc_token_count + long_doc_token_count == documents_token_count
-    assert short_doc_token_count + long_doc_token_count == model.token_count(
-        documents, batch_size=1
-    )
-    assert short_doc_token_count + long_doc_token_count < model.token_count(
-        documents, include_extension=True
-    )
+def test_token_count(model_cache) -> None:
+    model_name = "Qdrant/colmodernvbert"
+    with model_cache(model_name) as model:
+        documents = ["short doc", "it is a long document to check attention mask for paddings"]
+        short_doc_token_count = model.token_count(documents[0])
+        long_doc_token_count = model.token_count(documents[1])
+        documents_token_count = model.token_count(documents)
+        assert short_doc_token_count + long_doc_token_count == documents_token_count
+        assert short_doc_token_count + long_doc_token_count == model.token_count(
+            documents, batch_size=1
+        )
+        assert short_doc_token_count + long_doc_token_count < model.token_count(
+            documents, include_extension=True
+        )

tests/test_multi_gpu.py

@@ -1,5 +1,5 @@
 import pytest
-from typing import Optional
+
 from fastembed import (
     TextEmbedding,
     SparseTextEmbedding,
@@ -14,7 +14,7 @@ CACHE_DIR = "../model_cache"
 
 @pytest.mark.skip(reason="Requires a multi-gpu server")
 @pytest.mark.parametrize("device_id", [None, 0, 1])
-def test_gpu_via_providers(device_id: Optional[int]) -> None:
+def test_gpu_via_providers(device_id: int | None) -> None:
     docs = ["hello world", "flag embedding"]
 
     device_id = device_id if device_id is not None else 0
@@ -86,7 +86,7 @@ def test_gpu_via_providers(device_id: Optional[int]) -> None:
 
 @pytest.mark.skip(reason="Requires a multi-gpu server")
 @pytest.mark.parametrize("device_ids", [None, [0], [1], [0, 1]])
-def test_gpu_cuda_device_ids(device_ids: Optional[list[int]]) -> None:
+def test_gpu_cuda_device_ids(device_ids: list[int] | None) -> None:
     docs = ["hello world", "flag embedding"]
     device_id = device_ids[0] if device_ids else 0
     embedding_model = TextEmbedding(
@@ -171,7 +171,7 @@ def test_gpu_cuda_device_ids(device_ids: Optional[list[int]]) -> None:
 @pytest.mark.parametrize(
     "device_ids,parallel", [(None, None), (None, 2), ([1], None), ([1], 1), ([1], 2), ([0, 1], 2)]
 )
-def test_multi_gpu_parallel_inference(device_ids: Optional[list[int]], parallel: int) -> None:
+def test_multi_gpu_parallel_inference(device_ids: list[int] | None, parallel: int) -> None:
     docs = ["hello world", "flag embedding"] * 100
     batch_size = 5
 

tests/utils.py

@@ -3,12 +3,12 @@ import traceback
 
 from pathlib import Path
 from types import TracebackType
-from typing import Union, Callable, Any, Type, Optional
+from typing import Callable, Any, Type
 
 from fastembed.common.model_description import BaseModelDescription
 
 
-def delete_model_cache(model_dir: Union[str, Path]) -> None:
+def delete_model_cache(model_dir: str | Path) -> None:
     """Delete the model cache directory.
 
     If a model was downloaded from the HuggingFace model hub, then _model_dir is the dir to snapshots, removing
@@ -42,7 +42,7 @@ def delete_model_cache(model_dir: Union[str, Path]) -> None:
 def should_test_model(
     model_desc: BaseModelDescription,
     autotest_model_name: str,
-    is_ci: Optional[str],
+    is_ci: str | None,
     is_manual: bool,
 ):
     """Determine if a model should be tested based on environment