fix: Fix memory access by using ort value

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* chore: Trigger CI test

* Trigger CI

* Trigger CI

* Trigger CI

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* Trigger CI test

* new: Added on workflow dispatch

* tests: Updated tests

* fix: Fix CI

* fix: Fix CI

* fix: Fix CI

* improve: Prevent stop iteration error caused by next

* fix: Fix variable might be referenced before assignment

* refactor: Revised the way of getting models to test

* fix: Fix test in image model

* refactor: Call one model

* fix: Fix ci

* fix: Fix splade model name

* tests: Updated tests

* chore: Remove cache

* tests: Update multi task tests

* tests: Update multi task tests

* tests: Updated tests

* refactor: refactor utils func, add comments, conditions refactor

---------

Co-authored-by: George Panchuk <george.panchuk@qdrant.tech>

.github/workflows/python-tests.yml

@@ -1,9 +1,11 @@
 name: Tests
+run-name: Tests (gpu)
 
 on:
-  push:
-    branches: [ master, main, gpu ]
   pull_request:
+    branches: [ master, main, gpu ]
+  workflow_dispatch:
+  
 
 env:
   CARGO_TERM_COLOR: always
@@ -21,8 +23,6 @@ jobs:
           - '3.13.x'
         os:
           - ubuntu-latest
-          - macos-latest
-          - windows-latest
 
     runs-on: ${{ matrix.os }}
 
@@ -42,4 +42,4 @@ jobs:
 
       - name: Run pytest
         run: |
-          poetry run pytest 
+          poetry run pytest 

fastembed/common/model_description.py

@@ -7,6 +7,11 @@ from typing import Optional, Any
 class ModelSource:
     hf: Optional[str] = None
     url: Optional[str] = None
+    _deprecated_tar_struct: bool = False
+
+    @property
+    def deprecated_tar_struct(self) -> bool:
+        return self._deprecated_tar_struct
 
     def __post_init__(self) -> None:
         if self.hf is None and self.url is None:

fastembed/common/model_management.py

@@ -330,9 +330,10 @@ class ModelManagement(Generic[T]):
         model_name: str,
         source_url: str,
         cache_dir: str,
+        deprecated_tar_struct: bool = False,
         local_files_only: bool = False,
     ) -> Path:
-        fast_model_name = f"fast-{model_name.split('/')[-1]}"
+        fast_model_name = f"{'fast-' if deprecated_tar_struct else ''}{model_name.split('/')[-1]}"
         cache_tmp_dir = Path(cache_dir) / "tmp"
         model_tmp_dir = cache_tmp_dir / fast_model_name
         model_dir = Path(cache_dir) / fast_model_name
@@ -438,6 +439,7 @@ class ModelManagement(Generic[T]):
                         model.model,
                         str(url_source),
                         str(cache_dir),
+                        deprecated_tar_struct=model.sources.deprecated_tar_struct,
                         local_files_only=local_files_only,
                     )
                 except Exception:

fastembed/common/utils.py

@@ -5,12 +5,12 @@ import tempfile
 import unicodedata
 from pathlib import Path
 from itertools import islice
-from typing import Iterable, Optional, TypeVar
+from typing import Iterable, Optional, TypeVar, Sequence
 
 import numpy as np
 from numpy.typing import NDArray
 
-from fastembed.common.types import NumpyArray
+from fastembed.common.types import NumpyArray, OnnxProvider
 
 T = TypeVar("T")
 
@@ -67,3 +67,18 @@ def get_all_punctuation() -> set[str]:
 
 def remove_non_alphanumeric(text: str) -> str:
     return re.sub(r"[^\w\s]", " ", text, flags=re.UNICODE)
+
+
+def is_cuda_enabled(cuda: bool, providers: Optional[Sequence[OnnxProvider]]) -> bool:
+    """
+    Check if CUDA is enabled based on the `cuda` and `providers` parameters
+    """
+    if cuda:
+        return True
+    if not providers:
+        return False
+    if isinstance(providers, str):
+        return "CUDAExecutionProvider" in providers
+    return isinstance(providers, (list, tuple)) and any(
+        isinstance(p, str) and "CUDAExecutionProvider" in p for p in providers
+    )

fastembed/image/onnx_embedding.py

@@ -1,6 +1,5 @@
 from typing import Any, Iterable, Optional, Sequence, Type, Union
 
-import numpy as np
 
 from fastembed.common.types import NumpyArray
 from fastembed.common import ImageInput, OnnxProvider
@@ -195,7 +194,7 @@ class OnnxImageEmbedding(ImageEmbeddingBase, OnnxImageModel[NumpyArray]):
         return onnx_input
 
     def _post_process_onnx_output(self, output: OnnxOutputContext) -> Iterable[NumpyArray]:
-        return normalize(output.model_output).astype(np.float32)
+        return normalize(output.model_output)
 
 
 class OnnxImageEmbeddingWorker(ImageEmbeddingWorker[NumpyArray]):

fastembed/late_interaction/colbert.py

@@ -46,7 +46,7 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
         self, output: OnnxOutputContext, is_doc: bool = True
     ) -> Iterable[NumpyArray]:
         if not is_doc:
-            return output.model_output.astype(np.float32)
+            return output.model_output
 
         if output.input_ids is None or output.attention_mask is None:
             raise ValueError(
@@ -58,11 +58,11 @@ class Colbert(LateInteractionTextEmbeddingBase, OnnxTextModel[NumpyArray]):
                 if token_id in self.skip_list or token_id == self.pad_token_id:
                     output.attention_mask[i, j] = 0
 
-        output.model_output *= np.expand_dims(output.attention_mask, 2).astype(np.float32)
+        output.model_output *= np.expand_dims(output.attention_mask, 2)
         norm = np.linalg.norm(output.model_output, ord=2, axis=2, keepdims=True)
         norm_clamped = np.maximum(norm, 1e-12)
         output.model_output /= norm_clamped
-        return output.model_output.astype(np.float32)
+        return output.model_output
 
     def _preprocess_onnx_input(
         self, onnx_input: dict[str, NumpyArray], is_doc: bool = True, **kwargs: Any

fastembed/late_interaction_multimodal/colpali.py

@@ -142,7 +142,7 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
         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
-        ).astype(np.float32)
+        )
 
     def _post_process_onnx_text_output(
         self,
@@ -157,7 +157,7 @@ class ColPali(LateInteractionMultimodalEmbeddingBase, OnnxMultimodalModel[NumpyA
         Returns:
             Iterable[NumpyArray]: Post-processed output as NumPy arrays.
         """
-        return output.model_output.astype(np.float32)
+        return output.model_output
 
     def tokenize(self, documents: list[str], **kwargs: Any) -> list[Encoding]:
         texts_query: list[str] = []

fastembed/text/onnx_embedding.py

@@ -1,6 +1,5 @@
 from typing import Any, Iterable, Optional, Sequence, Type, Union
 
-import numpy as np
 from fastembed.common.types import NumpyArray, OnnxProvider
 from fastembed.common.onnx_model import OnnxOutputContext
 from fastembed.common.utils import define_cache_dir, normalize
@@ -21,6 +20,7 @@ supported_onnx_models: list[DenseModelDescription] = [
         sources=ModelSource(
             hf="Qdrant/fast-bge-base-en",
             url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en.tar.gz",
+            _deprecated_tar_struct=True,
         ),
         model_file="model_optimized.onnx",
     ),
@@ -36,6 +36,7 @@ supported_onnx_models: list[DenseModelDescription] = [
         sources=ModelSource(
             hf="qdrant/bge-base-en-v1.5-onnx-q",
             url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-base-en-v1.5.tar.gz",
+            _deprecated_tar_struct=True,
         ),
         model_file="model_optimized.onnx",
     ),
@@ -63,6 +64,7 @@ supported_onnx_models: list[DenseModelDescription] = [
         sources=ModelSource(
             hf="Qdrant/bge-small-en",
             url="https://storage.googleapis.com/qdrant-fastembed/BAAI-bge-small-en.tar.gz",
+            _deprecated_tar_struct=True,
         ),
         model_file="model_optimized.onnx",
     ),
@@ -90,6 +92,7 @@ supported_onnx_models: list[DenseModelDescription] = [
         sources=ModelSource(
             hf="Qdrant/bge-small-zh-v1.5",
             url="https://storage.googleapis.com/qdrant-fastembed/fast-bge-small-zh-v1.5.tar.gz",
+            _deprecated_tar_struct=True,
         ),
         model_file="model_optimized.onnx",
     ),
@@ -309,7 +312,7 @@ class OnnxTextEmbedding(TextEmbeddingBase, OnnxTextModel[NumpyArray]):
             processed_embeddings = embeddings
         else:
             raise ValueError(f"Unsupported embedding shape: {embeddings.shape}")
-        return normalize(processed_embeddings).astype(np.float32)
+        return normalize(processed_embeddings)
 
     def load_onnx_model(self) -> None:
         self._load_onnx_model(

fastembed/text/onnx_text_model.py

@@ -6,11 +6,12 @@ from typing import Any, Iterable, Optional, Sequence, Type, Union
 import numpy as np
 from numpy.typing import NDArray
 from tokenizers import Encoding, Tokenizer
+import onnxruntime as ort
 
 from fastembed.common.types import NumpyArray, OnnxProvider
 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
+from fastembed.common.utils import iter_batch, is_cuda_enabled
 from fastembed.parallel_processor import ParallelWorkerPool
 
 
@@ -62,6 +63,30 @@ class OnnxTextModel(OnnxModel[T]):
     def tokenize(self, documents: list[str], **kwargs: Any) -> list[Encoding]:
         return self.tokenizer.encode_batch(documents)  # type: ignore[union-attr]
 
+    def _run_with_io_binding(
+        self, onnx_input: dict[str, NumpyArray], device_id: int = 0
+    ) -> NumpyArray:
+        """Run inference with IO Binding for optimized memory transfer for non-CPU execution providers."""
+        io_binding = self.model.io_binding()
+
+        for name, value in onnx_input.items():
+            ort_value = ort.OrtValue.ortvalue_from_numpy(
+                numpy_obj=value, device_type="cuda", device_id=device_id
+            )
+            io_binding.bind_ortvalue_input(name=name, ortvalue=ort_value)
+
+        output_names = [output.name for output in self.model.get_outputs()]
+        for output_name in output_names:
+            io_binding.bind_output(
+                name=output_name,
+                device_type="cuda",
+                device_id=device_id,
+            )
+
+        self.model.run_with_iobinding(io_binding)
+
+        return io_binding.copy_outputs_to_cpu()
+
     def onnx_embed(
         self,
         documents: list[str],
@@ -82,7 +107,13 @@ class OnnxTextModel(OnnxModel[T]):
             )
         onnx_input = self._preprocess_onnx_input(onnx_input, **kwargs)
 
-        model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)  # type: ignore[union-attr]
+        providers = kwargs.get("providers", None)
+        cuda = kwargs.get("cuda", False)
+
+        if is_cuda_enabled(cuda, providers):
+            model_output = self._run_with_io_binding(onnx_input)
+        else:
+            model_output = self.model.run(self.ONNX_OUTPUT_NAMES, onnx_input)  # type: ignore[union-attr]
         return OnnxOutputContext(
             model_output=model_output[0],
             attention_mask=onnx_input.get("attention_mask", attention_mask),
@@ -115,7 +146,9 @@ class OnnxTextModel(OnnxModel[T]):
             if not hasattr(self, "model") or self.model is None:
                 self.load_onnx_model()
             for batch in iter_batch(documents, batch_size):
-                yield from self._post_process_onnx_output(self.onnx_embed(batch))
+                yield from self._post_process_onnx_output(
+                    self.onnx_embed(batch, cuda=cuda, providers=providers)
+                )
         else:
             if parallel == 0:
                 parallel = os.cpu_count()

fastembed/text/pooled_embedding.py

@@ -82,6 +82,7 @@ supported_pooled_models: list[DenseModelDescription] = [
         sources=ModelSource(
             hf="qdrant/multilingual-e5-large-onnx",
             url="https://storage.googleapis.com/qdrant-fastembed/fast-multilingual-e5-large.tar.gz",
+            _deprecated_tar_struct=True,
         ),
         model_file="model.onnx",
         additional_files=["model.onnx_data"],
@@ -115,7 +116,7 @@ class PooledEmbedding(OnnxTextEmbedding):
 
         embeddings = output.model_output
         attn_mask = output.attention_mask
-        return self.mean_pooling(embeddings, attn_mask).astype(np.float32)
+        return self.mean_pooling(embeddings, attn_mask)
 
 
 class PooledEmbeddingWorker(OnnxTextEmbeddingWorker):

fastembed/text/pooled_normalized_embedding.py

@@ -1,6 +1,5 @@
 from typing import Any, Iterable, Type
 
-import numpy as np
 
 from fastembed.common.types import NumpyArray
 from fastembed.common.onnx_model import OnnxOutputContext
@@ -22,6 +21,7 @@ supported_pooled_normalized_models: list[DenseModelDescription] = [
         sources=ModelSource(
             url="https://storage.googleapis.com/qdrant-fastembed/sentence-transformers-all-MiniLM-L6-v2.tar.gz",
             hf="qdrant/all-MiniLM-L6-v2-onnx",
+            _deprecated_tar_struct=True,
         ),
         model_file="model.onnx",
     ),
@@ -144,7 +144,7 @@ class PooledNormalizedEmbedding(PooledEmbedding):
 
         embeddings = output.model_output
         attn_mask = output.attention_mask
-        return normalize(self.mean_pooling(embeddings, attn_mask)).astype(np.float32)
+        return normalize(self.mean_pooling(embeddings, attn_mask))
 
 
 class PooledNormalizedEmbeddingWorker(OnnxTextEmbeddingWorker):

pyproject.toml

@@ -1,5 +1,5 @@
 [tool.poetry]
-name = "fastembed"
+name = "fastembed-gpu"
 version = "0.6.0"
 description = "Fast, light, accurate library built for retrieval embedding generation"
 authors = ["Qdrant Team <info@qdrant.tech>", "NirantK <nirant.bits@gmail.com>"]
@@ -18,7 +18,7 @@ numpy = [
     { version = ">=2.1.0", python = ">=3.13" },
     { version = ">=1.21,<2.1.0", python = "<3.10" },
 ]
-onnxruntime = [
+onnxruntime-gpu = [
     { version = ">1.20.0", python = ">=3.13" },
     { version = ">=1.17.0,<1.20.0", python = "<3.10" },
     { version = ">=1.17.0,!=1.20.0", python = ">=3.10,<3.13" },

tests/test_custom_models.py

@@ -91,15 +91,11 @@ def test_mock_add_custom_models():
     expected_output = {
         f"{PoolingType.MEAN.lower()}-normalized": normalize(
             mean_pooling(dummy_token_embedding, dummy_attention_mask)
-        ).astype(np.float32),
+        ),
         f"{PoolingType.MEAN.lower()}": mean_pooling(dummy_token_embedding, dummy_attention_mask),
-        f"{PoolingType.CLS.lower()}-normalized": normalize(dummy_token_embedding[:, 0]).astype(
-            np.float32
-        ),
+        f"{PoolingType.CLS.lower()}-normalized": normalize(dummy_token_embedding[:, 0]),
         f"{PoolingType.CLS.lower()}": dummy_token_embedding[:, 0],
-        f"{PoolingType.DISABLED.lower()}-normalized": normalize(dummy_pooled_embedding).astype(
-            np.float32
-        ),
+        f"{PoolingType.DISABLED.lower()}-normalized": normalize(dummy_pooled_embedding),
         f"{PoolingType.DISABLED.lower()}": dummy_pooled_embedding,
     }
 

tests/test_image_onnx_embeddings.py

@@ -8,7 +8,7 @@ from PIL import Image
 
 from fastembed import ImageEmbedding
 from tests.config import TEST_MISC_DIR
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_VECTOR_VALUES = {
     "Qdrant/clip-ViT-B-32-vision": np.array([-0.0098, 0.0128, -0.0274, 0.002, -0.0059]),
@@ -27,11 +27,13 @@ CANONICAL_VECTOR_VALUES = {
 }
 
 
-def test_embedding() -> None:
+@pytest.mark.parametrize("model_name", ["Qdrant/clip-ViT-B-32-vision"])
+def test_embedding(model_name: str) -> None:
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
     for model_desc in ImageEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
             continue
 
         dim = model_desc.dim
@@ -74,8 +76,12 @@ def test_batch_embedding(n_dims: int, model_name: str) -> None:
 
     embeddings = list(model.embed(images, batch_size=10))
     embeddings = np.stack(embeddings, axis=0)
+    assert np.allclose(embeddings[1], embeddings[2])
+
+    canonical_vector = CANONICAL_VECTOR_VALUES[model_name]
 
     assert embeddings.shape == (len(test_images) * n_images, n_dims)
+    assert np.allclose(embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3)
     if is_ci:
         delete_model_cache(model.model._model_dir)
 

tests/test_late_interaction_embeddings.py

@@ -6,7 +6,7 @@ import numpy as np
 from fastembed.late_interaction.late_interaction_text_embedding import (
     LateInteractionTextEmbedding,
 )
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 # vectors are abridged and rounded for brevity
 CANONICAL_COLUMN_VALUES = {
@@ -153,31 +153,37 @@ CANONICAL_QUERY_VALUES = {
 docs = ["Hello World"]
 
 
-def test_batch_embedding():
+@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
+def test_batch_embedding(model_name: str):
     is_ci = os.getenv("CI")
     docs_to_embed = docs * 10
 
-    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
-        print("evaluating", model_name)
-        model = LateInteractionTextEmbedding(model_name=model_name)
-        result = list(model.embed(docs_to_embed, batch_size=6))
+    model = LateInteractionTextEmbedding(model_name=model_name)
+    result = list(model.embed(docs_to_embed, batch_size=6))
+    expected_result = CANONICAL_COLUMN_VALUES[model_name]
 
-        for value in result:
-            token_num, abridged_dim = expected_result.shape
-            assert np.allclose(value[:, :abridged_dim], expected_result, atol=2e-3)
+    for value in result:
+        token_num, abridged_dim = expected_result.shape
+        assert np.allclose(value[:, :abridged_dim], expected_result, atol=2e-3)
 
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
+    if is_ci:
+        delete_model_cache(model.model._model_dir)
 
 
-def test_single_embedding():
+@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
+def test_single_embedding(model_name: str):
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
     docs_to_embed = docs
 
-    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+    for model_desc in LateInteractionTextEmbedding._list_supported_models():
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+            continue
+
         print("evaluating", model_name)
         model = LateInteractionTextEmbedding(model_name=model_name)
         result = next(iter(model.embed(docs_to_embed, batch_size=6)))
+        expected_result = CANONICAL_COLUMN_VALUES[model_name]
         token_num, abridged_dim = expected_result.shape
         assert np.allclose(result[:, :abridged_dim], expected_result, atol=2e-3)
 
@@ -185,14 +191,20 @@ def test_single_embedding():
             delete_model_cache(model.model._model_dir)
 
 
-def test_single_embedding_query():
+@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
+def test_single_embedding_query(model_name: str):
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
     queries_to_embed = docs
 
-    for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
+    for model_desc in LateInteractionTextEmbedding._list_supported_models():
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+            continue
+
         print("evaluating", model_name)
         model = LateInteractionTextEmbedding(model_name=model_name)
         result = next(iter(model.query_embed(queries_to_embed)))
+        expected_result = CANONICAL_QUERY_VALUES[model_name]
         token_num, abridged_dim = expected_result.shape
         assert np.allclose(result[:, :abridged_dim], expected_result, atol=2e-3)
 
@@ -200,10 +212,11 @@ def test_single_embedding_query():
             delete_model_cache(model.model._model_dir)
 
 
-def test_parallel_processing():
+@pytest.mark.parametrize("token_dim,model_name", [(96, "answerdotai/answerai-colbert-small-v1")])
+def test_parallel_processing(token_dim: int, model_name: str):
     is_ci = os.getenv("CI")
-    model = LateInteractionTextEmbedding(model_name="colbert-ir/colbertv2.0")
-    token_dim = 128
+    model = LateInteractionTextEmbedding(model_name=model_name)
+
     docs = ["hello world", "flag embedding"] * 100
     embeddings = list(model.embed(docs, batch_size=10, parallel=2))
     embeddings = np.stack(embeddings, axis=0)
@@ -222,10 +235,7 @@ def test_parallel_processing():
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    ["colbert-ir/colbertv2.0"],
-)
+@pytest.mark.parametrize("model_name", ["answerdotai/answerai-colbert-small-v1"])
 def test_lazy_load(model_name: str):
     is_ci = os.getenv("CI")
 

tests/test_late_interaction_multimodal.py

@@ -1,5 +1,6 @@
 import os
 
+import pytest
 from PIL import Image
 import numpy as np
 
@@ -45,38 +46,38 @@ images = [
 
 
 def test_batch_embedding():
-    is_ci = os.getenv("CI")
+    if os.getenv("CI"):
+        pytest.skip("Colpali is too large to test in CI")
 
-    if not is_ci:
-        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))
+    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))
 
-            for value in result:
-                token_num, abridged_dim = expected_result.shape
-                assert np.allclose(value[:token_num, :abridged_dim], expected_result, atol=2e-3)
+        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():
-    is_ci = os.getenv("CI")
-    if not is_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)
+    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():
-    is_ci = os.getenv("CI")
-    if not is_ci:
-        queries_to_embed = queries
+    if os.getenv("CI"):
+        pytest.skip("Colpali is too large to test in CI")
 
-        for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
-            print("evaluating", model_name)
-            model = LateInteractionMultimodalEmbedding(model_name=model_name)
-            result = next(iter(model.embed_text(queries_to_embed)))
-            token_num, abridged_dim = expected_result.shape
-            assert np.allclose(result[:token_num, :abridged_dim], expected_result, atol=2e-3)
+    for model_name, expected_result in CANONICAL_QUERY_VALUES.items():
+        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)

tests/test_sparse_embeddings.py

@@ -5,10 +5,10 @@ import numpy as np
 
 from fastembed.sparse.bm25 import Bm25
 from fastembed.sparse.sparse_text_embedding import SparseTextEmbedding
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_COLUMN_VALUES = {
-    "prithvida/Splade_PP_en_v1": {
+    "prithivida/Splade_PP_en_v1": {
         "indices": [
             2040,
             2047,
@@ -49,28 +49,41 @@ CANONICAL_COLUMN_VALUES = {
 docs = ["Hello World"]
 
 
-def test_batch_embedding() -> None:
+@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
+def test_batch_embedding(model_name: str) -> None:
     is_ci = os.getenv("CI")
     docs_to_embed = docs * 10
 
-    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
-        model = SparseTextEmbedding(model_name=model_name)
-        result = next(iter(model.embed(docs_to_embed, batch_size=6)))
-        assert result.indices.tolist() == expected_result["indices"]
+    model = SparseTextEmbedding(model_name=model_name)
+    result = next(iter(model.embed(docs_to_embed, batch_size=6)))
+    expected_result = CANONICAL_COLUMN_VALUES[model_name]
+    assert result.indices.tolist() == expected_result["indices"]
 
-        for i, value in enumerate(result.values):
-            assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
+    for i, value in enumerate(result.values):
+        assert pytest.approx(value, abs=0.001) == expected_result["values"][i]
+    if is_ci:
+        delete_model_cache(model.model._model_dir)
 
 
-def test_single_embedding() -> None:
+@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
+def test_single_embedding(model_name: str) -> None:
     is_ci = os.getenv("CI")
-    for model_name, expected_result in CANONICAL_COLUMN_VALUES.items():
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+
+    for model_desc in SparseTextEmbedding._list_supported_models():
+        if (
+            model_desc.model not in CANONICAL_COLUMN_VALUES
+        ):  # attention models and bm25 are also parts of
+            # SparseTextEmbedding, however, they have their own tests
+            continue
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+            continue
+
         model = SparseTextEmbedding(model_name=model_name)
 
         passage_result = next(iter(model.embed(docs, batch_size=6)))
         query_result = next(iter(model.query_embed(docs)))
+        expected_result = CANONICAL_COLUMN_VALUES[model_name]
         for result in [passage_result, query_result]:
             assert result.indices.tolist() == expected_result["indices"]
 
@@ -80,9 +93,10 @@ def test_single_embedding() -> None:
             delete_model_cache(model.model._model_dir)
 
 
-def test_parallel_processing() -> None:
+@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
+def test_parallel_processing(model_name: str) -> None:
     is_ci = os.getenv("CI")
-    model = SparseTextEmbedding(model_name="prithivida/Splade_PP_en_v1")
+    model = SparseTextEmbedding(model_name=model_name)
     docs = ["hello world", "flag embedding"] * 30
     sparse_embeddings_duo = list(model.embed(docs, batch_size=10, parallel=2))
     sparse_embeddings_all = list(model.embed(docs, batch_size=10, parallel=0))
@@ -172,10 +186,7 @@ def test_disable_stemmer_behavior(disable_stemmer: bool) -> None:
     assert result == expected, f"Expected {expected}, but got {result}"
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    ["prithivida/Splade_PP_en_v1"],
-)
+@pytest.mark.parametrize("model_name", ["prithivida/Splade_PP_en_v1"])
 def test_lazy_load(model_name: str) -> None:
     is_ci = os.getenv("CI")
     model = SparseTextEmbedding(model_name=model_name, lazy_load=True)

tests/test_text_cross_encoder.py

@@ -4,7 +4,7 @@ import numpy as np
 import pytest
 
 from fastembed.rerank.cross_encoder import TextCrossEncoder
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_SCORE_VALUES = {
     "Xenova/ms-marco-MiniLM-L-6-v2": np.array([8.500708, -2.541011]),
@@ -15,44 +15,37 @@ CANONICAL_SCORE_VALUES = {
     "jinaai/jina-reranker-v2-base-multilingual": np.array([1.6533, -1.6455]),
 }
 
-SELECTED_MODELS = {
-    "Xenova": "Xenova/ms-marco-MiniLM-L-6-v2",
-    "BAAI": "BAAI/bge-reranker-base",
-    "jinaai": "jinaai/jina-reranker-v1-tiny-en",
-}
 
-
-@pytest.mark.parametrize(
-    "model_name",
-    [model_name for model_name in CANONICAL_SCORE_VALUES],
-)
+@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
 def test_rerank(model_name: str) -> None:
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
-    model = TextCrossEncoder(model_name=model_name)
+    for model_desc in TextCrossEncoder._list_supported_models():
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+            continue
 
-    query = "What is the capital of France?"
-    documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
-    scores = np.array(list(model.rerank(query, documents)))
+        model = TextCrossEncoder(model_name=model_name)
 
-    pairs = [(query, doc) for doc in documents]
-    scores2 = np.array(list(model.rerank_pairs(pairs)))
-    assert np.allclose(
-        scores, scores2, atol=1e-5
-    ), f"Model: {model_name}, Scores: {scores}, Scores2: {scores2}"
+        query = "What is the capital of France?"
+        documents = ["Paris is the capital of France.", "Berlin is the capital of Germany."]
+        scores = np.array(list(model.rerank(query, documents)))
 
-    canonical_scores = CANONICAL_SCORE_VALUES[model_name]
-    assert np.allclose(
-        scores, canonical_scores, atol=1e-3
-    ), f"Model: {model_name}, Scores: {scores}, Expected: {canonical_scores}"
-    if is_ci:
-        delete_model_cache(model.model._model_dir)
+        pairs = [(query, doc) for doc in documents]
+        scores2 = np.array(list(model.rerank_pairs(pairs)))
+        assert np.allclose(
+            scores, scores2, atol=1e-5
+        ), f"Model: {model_name}, Scores: {scores}, Scores2: {scores2}"
+
+        canonical_scores = CANONICAL_SCORE_VALUES[model_name]
+        assert np.allclose(
+            scores, canonical_scores, atol=1e-3
+        ), f"Model: {model_name}, Scores: {scores}, Expected: {canonical_scores}"
+        if is_ci:
+            delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    [model_name for model_name in SELECTED_MODELS.values()],
-)
+@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
 def test_batch_rerank(model_name: str) -> None:
     is_ci = os.getenv("CI")
 
@@ -78,10 +71,7 @@ def test_batch_rerank(model_name: str) -> None:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    ["Xenova/ms-marco-MiniLM-L-6-v2"],
-)
+@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
 def test_lazy_load(model_name: str) -> None:
     is_ci = os.getenv("CI")
     model = TextCrossEncoder(model_name=model_name, lazy_load=True)
@@ -95,10 +85,7 @@ def test_lazy_load(model_name: str) -> None:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    [model_name for model_name in SELECTED_MODELS.values()],
-)
+@pytest.mark.parametrize("model_name", ["Xenova/ms-marco-MiniLM-L-6-v2"])
 def test_rerank_pairs_parallel(model_name: str) -> None:
     is_ci = os.getenv("CI")
 

tests/test_text_multitask_embeddings.py

@@ -4,7 +4,7 @@ import numpy as np
 import pytest
 
 from fastembed import TextEmbedding
-from fastembed.text.multitask_embedding import Task
+from fastembed.text.multitask_embedding import JinaEmbeddingV3, Task
 from tests.utils import delete_model_cache
 
 
@@ -60,52 +60,43 @@ CANONICAL_VECTOR_VALUES = {
 docs = ["Hello World", "Follow the white rabbit."]
 
 
-def test_batch_embedding():
+@pytest.mark.parametrize("dim,model_name", [(1024, "jinaai/jina-embeddings-v3")])
+def test_batch_embedding(dim: int, model_name: str):
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+    if is_ci and not is_manual:
+        pytest.skip("Skipping multitask models in CI non-manual mode")
+
     docs_to_embed = docs * 10
     default_task = Task.RETRIEVAL_PASSAGE
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
-            continue
+    model = TextEmbedding(model_name=model_name)
 
-        model_name = model_desc.model
-        dim = model_desc.dim
+    embeddings = list(model.embed(documents=docs_to_embed, batch_size=6))
+    embeddings = np.stack(embeddings, axis=0)
 
-        if model_name not in CANONICAL_VECTOR_VALUES.keys():
-            continue
+    assert embeddings.shape == (len(docs_to_embed), dim)
 
-        model = TextEmbedding(model_name=model_name)
+    canonical_vector = CANONICAL_VECTOR_VALUES[model_name][default_task]["vectors"]
+    assert np.allclose(
+        embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
+    ), model_name
 
-        print(f"evaluating {model_name} default task")
-
-        embeddings = list(model.embed(documents=docs_to_embed, batch_size=6))
-        embeddings = np.stack(embeddings, axis=0)
-
-        assert embeddings.shape == (len(docs_to_embed), dim)
-
-        canonical_vector = CANONICAL_VECTOR_VALUES[model_name][default_task]["vectors"]
-        assert np.allclose(
-            embeddings[: len(docs), : canonical_vector.shape[1]], canonical_vector, atol=1e-4
-        ), model_desc.model
-
-        if is_ci:
-            delete_model_cache(model.model._model_dir)
+    if is_ci:
+        delete_model_cache(model.model._model_dir)
 
 
 def test_single_embedding():
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+    if is_ci and not is_manual:
+        pytest.skip("Skipping multitask models in CI non-manual mode")
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
-            continue
-
+    for model_desc in JinaEmbeddingV3._list_supported_models():
+        # todo: once we add more models, we should not test models >1GB size locally
         model_name = model_desc.model
         dim = model_desc.dim
 
-        if model_name not in CANONICAL_VECTOR_VALUES.keys():
-            continue
-
         model = TextEmbedding(model_name=model_name)
 
         for task in CANONICAL_VECTOR_VALUES[model_name]:
@@ -127,18 +118,17 @@ def test_single_embedding():
 
 def test_single_embedding_query():
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+    if is_ci and not is_manual:
+        pytest.skip("Skipping multitask models in CI non-manual mode")
+
     task_id = Task.RETRIEVAL_QUERY
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
-            continue
-
+    for model_desc in JinaEmbeddingV3._list_supported_models():
+        # todo: once we add more models, we should not test models >1GB size locally
         model_name = model_desc.model
         dim = model_desc.dim
 
-        if model_name not in CANONICAL_VECTOR_VALUES.keys():
-            continue
-
         model = TextEmbedding(model_name=model_name)
 
         print(f"evaluating {model_name} query_embed task_id: {task_id}")
@@ -159,18 +149,18 @@ def test_single_embedding_query():
 
 def test_single_embedding_passage():
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+    if is_ci and not is_manual:
+        pytest.skip("Skipping multitask models in CI non-manual mode")
+
     task_id = Task.RETRIEVAL_PASSAGE
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
-            continue
+    for model_desc in JinaEmbeddingV3._list_supported_models():
+        # todo: once we add more models, we should not test models >1GB size locally
 
         model_name = model_desc.model
         dim = model_desc.dim
 
-        if model_name not in CANONICAL_VECTOR_VALUES.keys():
-            continue
-
         model = TextEmbedding(model_name=model_name)
 
         print(f"evaluating {model_name} passage_embed task_id: {task_id}")
@@ -189,14 +179,15 @@ def test_single_embedding_passage():
             delete_model_cache(model.model._model_dir)
 
 
-def test_parallel_processing():
+@pytest.mark.parametrize("dim,model_name", [(1024, "jinaai/jina-embeddings-v3")])
+def test_parallel_processing(dim: int, model_name: str):
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+    if is_ci and not is_manual:
+        pytest.skip("Skipping in CI non-manual mode")
 
     docs = ["Hello World", "Follow the white rabbit."] * 10
 
-    model_name = "jinaai/jina-embeddings-v3"
-    dim = 1024
-
     model = TextEmbedding(model_name=model_name)
 
     task_id = Task.SEPARATION
@@ -218,14 +209,14 @@ def test_parallel_processing():
 
 def test_task_assignment():
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
-    for model_desc in TextEmbedding._list_supported_models():
-        if not is_ci and model_desc.size_in_GB > 1:
-            continue
+    if is_ci and not is_manual:
+        pytest.skip("Skipping in CI non-manual mode")
 
+    for model_desc in JinaEmbeddingV3._list_supported_models():
+        # todo: once we add more models, we should not test models >1GB size locally
         model_name = model_desc.model
-        if model_name not in CANONICAL_VECTOR_VALUES.keys():
-            continue
 
         model = TextEmbedding(model_name=model_name)
 
@@ -237,12 +228,14 @@ def test_task_assignment():
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    ["jinaai/jina-embeddings-v3"],
-)
+@pytest.mark.parametrize("model_name", ["jinaai/jina-embeddings-v3"])
 def test_lazy_load(model_name: str):
     is_ci = os.getenv("CI")
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
+
+    if is_ci and not is_manual:
+        pytest.skip("Skipping in CI non-manual mode")
+
     model = TextEmbedding(model_name=model_name, lazy_load=True)
     assert not hasattr(model.model, "model")
 

tests/test_text_onnx_embeddings.py

@@ -5,7 +5,7 @@ import numpy as np
 import pytest
 
 from fastembed.text.text_embedding import TextEmbedding
-from tests.utils import delete_model_cache
+from tests.utils import delete_model_cache, should_test_model
 
 CANONICAL_VECTOR_VALUES = {
     "BAAI/bge-small-en": np.array([-0.0232, -0.0255, 0.0174, -0.0639, -0.0006]),
@@ -72,17 +72,19 @@ CANONICAL_VECTOR_VALUES = {
 MULTI_TASK_MODELS = ["jinaai/jina-embeddings-v3"]
 
 
-def test_embedding() -> None:
+@pytest.mark.parametrize("model_name", ["BAAI/bge-small-en-v1.5"])
+def test_embedding(model_name: str) -> None:
     is_ci = os.getenv("CI")
     is_mac = platform.system() == "Darwin"
+    is_manual = os.getenv("GITHUB_EVENT_NAME") == "workflow_dispatch"
 
     for model_desc in TextEmbedding._list_supported_models():
-        if (
-            (not is_ci and model_desc.size_in_GB > 1)
-            or model_desc.model in MULTI_TASK_MODELS
-            or (is_mac and model_desc.model == "nomic-ai/nomic-embed-text-v1.5-Q")
+        if model_desc.model in MULTI_TASK_MODELS or (
+            is_mac and model_desc.model == "nomic-ai/nomic-embed-text-v1.5-Q"
         ):
             continue
+        if not should_test_model(model_desc, model_name, is_ci, is_manual):
+            continue
 
         dim = model_desc.dim
 
@@ -95,15 +97,12 @@ def test_embedding() -> None:
         canonical_vector = CANONICAL_VECTOR_VALUES[model_desc.model]
         assert np.allclose(
             embeddings[0, : canonical_vector.shape[0]], canonical_vector, atol=1e-3
-        ), model_desc["model"]
+        ), model_desc.model
         if is_ci:
             delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "n_dims,model_name",
-    [(384, "BAAI/bge-small-en-v1.5"), (768, "jinaai/jina-embeddings-v2-base-en")],
-)
+@pytest.mark.parametrize("n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5")])
 def test_batch_embedding(n_dims: int, model_name: str) -> None:
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name)
@@ -112,15 +111,12 @@ def test_batch_embedding(n_dims: int, model_name: str) -> None:
     embeddings = list(model.embed(docs, batch_size=10))
     embeddings = np.stack(embeddings, axis=0)
 
-    assert embeddings.shape == (200, n_dims)
+    assert embeddings.shape == (len(docs), n_dims)
     if is_ci:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "n_dims,model_name",
-    [(384, "BAAI/bge-small-en-v1.5"), (768, "jinaai/jina-embeddings-v2-base-en")],
-)
+@pytest.mark.parametrize("n_dims,model_name", [(384, "BAAI/bge-small-en-v1.5")])
 def test_parallel_processing(n_dims: int, model_name: str) -> None:
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name)
@@ -135,7 +131,7 @@ def test_parallel_processing(n_dims: int, model_name: str) -> None:
     embeddings_3 = list(model.embed(docs, batch_size=10, parallel=0))
     embeddings_3 = np.stack(embeddings_3, axis=0)
 
-    assert embeddings.shape == (200, n_dims)
+    assert embeddings.shape == (len(docs), n_dims)
     assert np.allclose(embeddings, embeddings_2, atol=1e-3)
     assert np.allclose(embeddings, embeddings_3, atol=1e-3)
 
@@ -143,10 +139,7 @@ def test_parallel_processing(n_dims: int, model_name: str) -> None:
         delete_model_cache(model.model._model_dir)
 
 
-@pytest.mark.parametrize(
-    "model_name",
-    ["BAAI/bge-small-en-v1.5"],
-)
+@pytest.mark.parametrize("model_name", ["BAAI/bge-small-en-v1.5"])
 def test_lazy_load(model_name: str) -> None:
     is_ci = os.getenv("CI")
     model = TextEmbedding(model_name=model_name, lazy_load=True)

tests/utils.py

@@ -3,7 +3,9 @@ import traceback
 
 from pathlib import Path
 from types import TracebackType
-from typing import Union, Callable, Any, Type
+from typing import Union, Callable, Any, Type, Optional
+
+from fastembed.common.model_description import BaseModelDescription
 
 
 def delete_model_cache(model_dir: Union[str, Path]) -> None:
@@ -35,3 +37,31 @@ def delete_model_cache(model_dir: Union[str, Path]) -> None:
     if model_dir.exists():
         # todo: PermissionDenied is raised on blobs removal in Windows, with blobs > 2GB
         shutil.rmtree(model_dir, onerror=on_error)
+
+
+def should_test_model(
+    model_desc: BaseModelDescription,
+    autotest_model_name: str,
+    is_ci: Optional[str],
+    is_manual: bool,
+):
+    """Determine if a model should be tested based on environment
+
+    Tests can be run either in ci or locally.
+    Testing all models each time in ci is too long.
+    The testing scheme in ci and on a local machine are different, therefore, there are 3 possible scenarious.
+    1) Run lightweight tests in ci:
+        - test only one model that has been manually chosen as a representative for a certain class family
+    2) Run heavyweight (manual) tests in ci:
+        - test all models
+        Running tests in ci each time is too expensive, however, it's fine to run it one time with a manual dispatch
+    3) Run tests locally:
+        - test all models, which are not too heavy, since network speed might be a bottleneck
+
+    """
+    if not is_ci:
+        if model_desc.size_in_GB > 1:
+            return False
+    elif not is_manual and model_desc.model != autotest_model_name:
+        return False
+    return True