diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index acc29c7..12382ef 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -28,15 +28,11 @@ jobs:
 
     - name: Install main
       run: |
-        pip install -e git+https://github.com/osamproject/osam-core.git#egg=osam-core
-        pip install -e git+https://github.com/osamproject/osam-efficientsam.git#egg=osam-efficientsam
-        pip install -e git+https://github.com/osamproject/osam-sam.git#egg=osam-sam
-        pip install -e git+https://github.com/osamproject/osam-yoloworld.git#egg=osam-yoloworld
         pip install .[test]
 
     - name: Lint
       run: |
-        make lint
+        make lint mypy
 
     - name: Test
       run: |
diff --git a/Makefile b/Makefile
index d9887e3..842ec38 100644
--- a/Makefile
+++ b/Makefile
@@ -5,10 +5,12 @@ all:
 
 PACKAGE_DIR=osam
 
+mypy:
+	mypy --package $(PACKAGE_DIR)
+
 lint:
 	ruff format --check
 	ruff check
-	mypy --package $(PACKAGE_DIR)
 
 format:
 	ruff format
diff --git a/osam/__init__.py b/osam/__init__.py
index 6dab54f..68884bc 100644
--- a/osam/__init__.py
+++ b/osam/__init__.py
@@ -2,6 +2,5 @@
 
 __version__ = importlib.metadata.version("osam")
 
-from . import _models  # noqa: F401
 from . import apis  # noqa: F401
 from . import types  # noqa: F401
diff --git a/osam/__main__.py b/osam/__main__.py
index 751e399..d0d3694 100644
--- a/osam/__main__.py
+++ b/osam/__main__.py
@@ -10,12 +10,12 @@
 import PIL.Image
 import uvicorn
 from loguru import logger
-from osam_core import apis
-from osam_core import types
 
 from . import __version__
 from . import _humanize
 from . import _tabulate
+from . import apis
+from . import types
 
 
 @click.group(context_settings=dict(help_option_names=["-h", "--help"]))
@@ -131,7 +131,9 @@ def run(model_name: str, image_path: str, prompt, json: bool) -> None:
 
         if request.prompt and request.prompt.texts is not None:
             labels = [
-                1 + request.prompt.texts.index(annotation.text)
+                0
+                if annotation.text is None
+                else 1 + request.prompt.texts.index(annotation.text)
                 for annotation in response.annotations
             ]
         else:
diff --git a/osam/_contextlib.py b/osam/_contextlib.py
new file mode 100644
index 0000000..fed40ec
--- /dev/null
+++ b/osam/_contextlib.py
@@ -0,0 +1,28 @@
+import contextlib
+import os
+import sys
+import tempfile
+
+
+@contextlib.contextmanager
+def suppress():
+    original_stdout_fd = os.dup(sys.stdout.fileno())
+    original_stderr_fd = os.dup(sys.stderr.fileno())
+
+    with tempfile.TemporaryFile(mode="w+b") as temp_stdout, tempfile.TemporaryFile(
+        mode="w+b"
+    ) as temp_stderr:
+        os.dup2(temp_stdout.fileno(), sys.stdout.fileno())
+        os.dup2(temp_stderr.fileno(), sys.stderr.fileno())
+
+        try:
+            yield
+        finally:
+            sys.stdout.flush()
+            sys.stderr.flush()
+
+            os.dup2(original_stdout_fd, sys.stdout.fileno())
+            os.dup2(original_stderr_fd, sys.stderr.fileno())
+
+            os.close(original_stdout_fd)
+            os.close(original_stderr_fd)
diff --git a/osam/_json.py b/osam/_json.py
new file mode 100644
index 0000000..61a9731
--- /dev/null
+++ b/osam/_json.py
@@ -0,0 +1,19 @@
+import base64
+import io
+
+import numpy as np
+import PIL.Image
+
+
+def image_ndarray_to_b64data(ndarray):
+    pil = PIL.Image.fromarray(ndarray)
+    f = io.BytesIO()
+    pil.save(f, format="PNG")
+    data = f.getvalue()
+    return base64.b64encode(data).decode("utf-8")
+
+
+def image_b64data_to_ndarray(b64data):
+    data = base64.b64decode(b64data)
+    pil = PIL.Image.open(io.BytesIO(data))
+    return np.asarray(pil)
diff --git a/osam/_json_test.py b/osam/_json_test.py
new file mode 100644
index 0000000..4c4d716
--- /dev/null
+++ b/osam/_json_test.py
@@ -0,0 +1,25 @@
+import numpy as np
+import pytest
+
+from . import _json
+
+
+@pytest.fixture
+def image():
+    y, x = np.meshgrid(np.arange(10), np.arange(10))
+    center = (np.array(x.shape) - 1) / 2
+    image_float = np.exp(-((x - center[1]) ** 2 + (y - center[0]) ** 2) / 10)
+    image = (image_float * 255).astype(np.uint8)
+    return image
+
+
+def test_image_ndarray_to_b64data(image):
+    b64data = _json.image_ndarray_to_b64data(image)
+    assert isinstance(b64data, str)
+    assert len(b64data) == 204
+
+
+def test_image_b64data_to_ndarray(image):
+    b64data = _json.image_ndarray_to_b64data(image)
+    image_recovered = _json.image_b64data_to_ndarray(b64data)
+    np.testing.assert_array_equal(image, image_recovered)
diff --git a/osam/_models.py b/osam/_models.py
deleted file mode 100644
index 17f824b..0000000
--- a/osam/_models.py
+++ /dev/null
@@ -1,4 +0,0 @@
-# import modules to register the models
-import osam_efficientsam  # noqa: F401
-import osam_sam  # noqa: F401
-import osam_yoloworld  # noqa: F401
diff --git a/osam/_models/__init__.py b/osam/_models/__init__.py
new file mode 100644
index 0000000..7f4dbe8
--- /dev/null
+++ b/osam/_models/__init__.py
@@ -0,0 +1,6 @@
+from .efficientsam import EfficientSam10m  # noqa: F401
+from .efficientsam import EfficientSam30m  # noqa: F401
+from .sam import Sam100m  # noqa: F401
+from .sam import Sam300m  # noqa: F401
+from .sam import Sam600m  # noqa: F401
+from .yoloworld import YoloWorldXL  # noqa: F401
diff --git a/osam/_models/efficientsam/LICENSE b/osam/_models/efficientsam/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/osam/_models/efficientsam/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
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+      "Licensor" shall mean the copyright owner or entity authorized by
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+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
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+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
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+   3. Grant of Patent License. Subject to the terms and conditions of
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+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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diff --git a/osam/_models/efficientsam/__init__.py b/osam/_models/efficientsam/__init__.py
new file mode 100644
index 0000000..07f31c5
--- /dev/null
+++ b/osam/_models/efficientsam/__init__.py
@@ -0,0 +1,126 @@
+import imgviz
+import numpy as np
+from loguru import logger
+
+from ... import types
+
+
+class EfficientSam(types.Model):
+    def encode_image(self, image: np.ndarray) -> types.ImageEmbedding:
+        if image.ndim == 2:
+            raise ValueError("Grayscale images are not supported")
+        if image.ndim == 3 and image.shape[2] == 4:
+            raise ValueError("RGBA images are not supported")
+
+        batched_images = image.transpose(2, 0, 1)[None].astype(np.float32) / 255
+        image_embedding = self._inference_sessions["encoder"].run(
+            output_names=None,
+            input_feed={"batched_images": batched_images},
+        )[0][0]  # (embedding_dim, height, width)
+
+        return types.ImageEmbedding(
+            original_height=image.shape[0],
+            original_width=image.shape[1],
+            embedding=image_embedding,
+        )
+
+    def generate(self, request: types.GenerateRequest) -> types.GenerateResponse:
+        if request.image_embedding is None:
+            if request.image is None:
+                raise ValueError("request.image or request.image_embedding is required")
+            image_embedding = self.encode_image(request.image)
+        else:
+            image_embedding = request.image_embedding
+
+        if request.prompt is None:
+            prompt = types.Prompt(
+                points=np.array(
+                    [
+                        [
+                            image_embedding.original_width / 2,
+                            image_embedding.original_height / 2,
+                        ]
+                    ],
+                    dtype=np.float32,
+                ),
+                point_labels=np.array([1], dtype=np.int32),
+            )
+            logger.warning(
+                "Prompt is not given, so using the center point as prompt: {prompt!r}",
+                prompt=prompt,
+            )
+        else:
+            prompt = request.prompt
+        del request
+
+        if prompt.points is None or prompt.point_labels is None:
+            raise ValueError("Prompt must contain points and point_labels: %r", prompt)
+
+        input_point = np.array(prompt.points, dtype=np.float32)
+        input_label = np.array(prompt.point_labels, dtype=np.float32)
+
+        # batch_size, embedding_dim, height, width
+        batched_image_embedding = image_embedding.embedding[None, :, :, :]
+        # batch_size, num_queries, num_points, 2
+        batched_point_coords = input_point[None, None, :, :]
+        # batch_size, num_queries, num_points
+        batched_point_labels = input_label[None, None, :]
+
+        decoder_inputs = {
+            "image_embeddings": batched_image_embedding,
+            "batched_point_coords": batched_point_coords,
+            "batched_point_labels": batched_point_labels,
+            "orig_im_size": np.array(
+                (image_embedding.original_height, image_embedding.original_width),
+                dtype=np.int64,
+            ),
+        }
+
+        masks, _, _ = self._inference_sessions["decoder"].run(None, decoder_inputs)
+        mask = masks[0, 0, 0, :, :]  # (1, 1, 3, H, W) -> (H, W)
+        mask = mask > 0.0
+
+        bbox = imgviz.instances.mask_to_bbox([mask])[0].astype(int)
+
+        return types.GenerateResponse(
+            model=self.name,
+            image_embedding=image_embedding,
+            annotations=[
+                types.Annotation(
+                    mask=mask,
+                    bounding_box=types.BoundingBox(
+                        ymin=bbox[0], xmin=bbox[1], ymax=bbox[2], xmax=bbox[3]
+                    ),
+                )
+            ],
+        )
+
+
+class EfficientSam10m(EfficientSam):
+    name = "efficientsam:10m"
+
+    _blobs = {
+        "encoder": types.Blob(
+            url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vitt_encoder.onnx",
+            hash="sha256:7a73ee65aa2c37237c89b4b18e73082f757ffb173899609c5d97a2bbd4ebb02d",
+        ),
+        "decoder": types.Blob(
+            url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vitt_decoder.onnx",
+            hash="sha256:e1afe46232c3bfa3470a6a81c7d3181836a94ea89528aff4e0f2d2c611989efd",
+        ),
+    }
+
+
+class EfficientSam30m(EfficientSam):
+    name = "efficientsam:latest"
+
+    _blobs = {
+        "encoder": types.Blob(
+            url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vits_encoder.onnx",
+            hash="sha256:4cacbb23c6903b1acf87f1d77ed806b840800c5fcd4ac8f650cbffed474b8896",
+        ),
+        "decoder": types.Blob(
+            url="https://github.com/labelmeai/efficient-sam/releases/download/onnx-models-20231225/efficient_sam_vits_decoder.onnx",
+            hash="sha256:4727baf23dacfb51d4c16795b2ac382c403505556d0284e84c6ff3d4e8e36f22",
+        ),
+    }
diff --git a/osam/_models/sam/LICENSE b/osam/_models/sam/LICENSE
new file mode 100644
index 0000000..261eeb9
--- /dev/null
+++ b/osam/_models/sam/LICENSE
@@ -0,0 +1,201 @@
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
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diff --git a/osam/_models/sam/__init__.py b/osam/_models/sam/__init__.py
new file mode 100644
index 0000000..e45ab28
--- /dev/null
+++ b/osam/_models/sam/__init__.py
@@ -0,0 +1,216 @@
+import numpy as np
+import PIL.Image
+from loguru import logger
+
+from ... import types
+
+
+class Sam(types.Model):
+    _image_size: int = 1024
+
+    def encode_image(self, image: np.ndarray) -> types.ImageEmbedding:
+        if image.ndim == 2:
+            raise ValueError("Grayscale images are not supported")
+        if image.ndim == 3 and image.shape[2] == 4:
+            raise ValueError("RGBA images are not supported")
+
+        image_embedding = _compute_image_embedding(
+            encoder_session=self._inference_sessions["encoder"],
+            image=image,
+            image_size=self._image_size,
+        )
+
+        return types.ImageEmbedding(
+            original_height=image.shape[0],
+            original_width=image.shape[1],
+            embedding=image_embedding,
+        )
+
+    def generate(self: "Sam", request: types.GenerateRequest) -> types.GenerateResponse:
+        if request.image_embedding is None:
+            if request.image is None:
+                raise ValueError("request.image or request.image_embedding is required")
+            image_embedding = self.encode_image(request.image)
+        else:
+            image_embedding = request.image_embedding
+
+        if request.prompt is None:
+            prompt = types.Prompt(
+                points=np.array(
+                    [
+                        [
+                            image_embedding.original_width / 2,
+                            image_embedding.original_height / 2,
+                        ]
+                    ],
+                    dtype=np.float32,
+                ),
+                point_labels=np.array([1], dtype=np.int32),
+            )
+            logger.warning(
+                "Prompt is not given, so using the center point as prompt: {prompt!r}",
+                prompt=prompt,
+            )
+        else:
+            prompt = request.prompt
+        del request
+
+        if prompt.points is None or prompt.point_labels is None:
+            raise ValueError("Prompt must contain points and point_labels: %r", prompt)
+
+        mask = _generate_mask(
+            decoder_session=self._inference_sessions["decoder"],
+            image_embedding=image_embedding,
+            prompt=prompt,
+            image_size=self._image_size,
+        )
+        return types.GenerateResponse(
+            model=self.name,
+            image_embedding=image_embedding,
+            annotations=[types.Annotation(mask=mask)],
+        )
+
+
+def _compute_scale_to_resize_image(height: int, width: int, image_size: int):
+    if width > height:
+        scale = image_size / width
+        new_height = int(round(height * scale))
+        new_width = image_size
+    else:
+        scale = image_size / height
+        new_height = image_size
+        new_width = int(round(width * scale))
+    return scale, new_height, new_width
+
+
+def _resize_image(image: np.ndarray, image_size: int):
+    scale, new_height, new_width = _compute_scale_to_resize_image(
+        height=image.shape[0],
+        width=image.shape[1],
+        image_size=image_size,
+    )
+    scaled_image = np.asarray(
+        PIL.Image.fromarray(image).resize(
+            (new_width, new_height), resample=PIL.Image.BILINEAR
+        )
+    ).astype(np.float32)
+    return scale, scaled_image
+
+
+def _compute_image_embedding(
+    encoder_session,
+    image: np.ndarray,
+    image_size: int,
+):
+    scale, x = _resize_image(image=image, image_size=image_size)
+    x = (x - np.array([123.675, 116.28, 103.53], dtype=np.float32)) / np.array(
+        [58.395, 57.12, 57.375], dtype=np.float32
+    )
+    x = np.pad(
+        x,
+        (
+            (0, image_size - x.shape[0]),
+            (0, image_size - x.shape[1]),
+            (0, 0),
+        ),
+    )
+    x = x.transpose(2, 0, 1)[None, :, :, :]
+
+    output = encoder_session.run(output_names=None, input_feed={"x": x})
+    image_embedding = output[0][0]  # (embedding_dim, height, width)
+    return image_embedding
+
+
+def _generate_mask(
+    decoder_session,
+    image_embedding: types.ImageEmbedding,
+    prompt: types.Prompt,
+    image_size: int,
+):
+    if prompt.points is None or prompt.point_labels is None:
+        raise ValueError("Prompt must contain points and point_labels: %r" % prompt)
+
+    onnx_coord = np.concatenate([prompt.points, np.array([[0.0, 0.0]])], axis=0)[
+        None, :, :
+    ]
+    onnx_label = np.concatenate([prompt.point_labels, np.array([-1])], axis=0)[
+        None, :
+    ].astype(np.float32)
+
+    scale, new_height, new_width = _compute_scale_to_resize_image(
+        height=image_embedding.original_height,
+        width=image_embedding.original_width,
+        image_size=image_size,
+    )
+    onnx_coord = (
+        onnx_coord.astype(float)
+        * (
+            new_width / image_embedding.original_width,
+            new_height / image_embedding.original_height,
+        )
+    ).astype(np.float32)
+
+    onnx_mask_input = np.zeros((1, 1, 256, 256), dtype=np.float32)
+    onnx_has_mask_input = np.array([-1], dtype=np.float32)
+
+    decoder_inputs = {
+        "image_embeddings": image_embedding.embedding[None, :, :, :],
+        "point_coords": onnx_coord,
+        "point_labels": onnx_label,
+        "mask_input": onnx_mask_input,
+        "has_mask_input": onnx_has_mask_input,
+        "orig_im_size": np.array(
+            (image_embedding.original_height, image_embedding.original_width),
+            dtype=np.float32,
+        ),
+    }
+
+    masks, _, _ = decoder_session.run(None, decoder_inputs)
+    mask = masks[0, 0]  # (1, 1, H, W) -> (H, W)
+    mask = mask > 0.0
+    return mask
+
+
+class Sam100m(Sam):
+    name = "sam:100m"
+
+    _blobs = {
+        "encoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_b_01ec64.quantized.encoder.onnx",
+            hash="sha256:3346b9cc551c9902fbf3b203935e933592b22e042365f58321c17fc12641fd6a",
+        ),
+        "decoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_b_01ec64.quantized.decoder.onnx",
+            hash="sha256:edbcf1a0afaa55621fb0abe6b3db1516818b609ea9368f309746a3afc68f7613",
+        ),
+    }
+
+
+class Sam300m(Sam):
+    name = "sam:300m"
+
+    _blobs = {
+        "encoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_l_0b3195.quantized.encoder.onnx",
+            hash="sha256:f7158a4a1fe7f670ef47ea2f7f852685425c1ed6caa40f5df86cbe2b0502034f",
+        ),
+        "decoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_l_0b3195.quantized.decoder.onnx",
+            hash="sha256:552ebb23bf52c5e5b971ac710d1eb8dccfd88b36cc6aff881d1536d1662e6d7b",
+        ),
+    }
+
+
+class Sam600m(Sam):
+    name = "sam:latest"
+
+    _blobs = {
+        "encoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.encoder.onnx",
+            hash="sha256:a5c745fd4279efc5e5436b412200e983dafc2249ce172af6cc0002a71bb5f485",
+        ),
+        "decoder": types.Blob(
+            url="https://github.com/wkentaro/labelme/releases/download/sam-20230416/sam_vit_h_4b8939.quantized.decoder.onnx",
+            hash="sha256:020b385a45ffe51097e1acd10cd791075a86171153505f789a793bc382eef210",
+        ),
+    }
diff --git a/osam/_models/yoloworld/LICENSE b/osam/_models/yoloworld/LICENSE
new file mode 100644
index 0000000..dd8b209
--- /dev/null
+++ b/osam/_models/yoloworld/LICENSE
@@ -0,0 +1,1347 @@
+                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
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+
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+    copy of the Corresponding Source for all the software in the
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+
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+
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+  7. Additional Terms.
+
+  "Additional permissions" are terms that supplement the terms of this
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+Additional permissions that are applicable to the entire Program shall
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+  However, if you cease all violation of this License, then your
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+  9. Acceptance Not Required for Having Copies.
+
+  You are not required to accept this License in order to receive or
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+
+  10. Automatic Licensing of Downstream Recipients.
+
+  Each time you convey a covered work, the recipient automatically
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+any patent claim is infringed by making, using, selling, offering for
+sale, or importing the Program or any portion of it.
+
+  11. Patents.
+
+  A "contributor" is a copyright holder who authorizes use under this
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+work thus licensed is called the contributor's "contributor version".
+
+  A contributor's "essential patent claims" are all patent claims
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+hereafter acquired, that would be infringed by some manner, permitted
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+
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+
+  A patent license is "discriminatory" if it does not include within
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+or that patent license was granted, prior to 28 March 2007.
+
+  Nothing in this License shall be construed as excluding or limiting
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+
+  12. No Surrender of Others' Freedom.
+
+  If conditions are imposed on you (whether by court order, agreement or
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+
+  13. Use with the GNU Affero General Public License.
+
+  Notwithstanding any other provision of this License, you have
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+
+  14. Revised Versions of this License.
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+  The Free Software Foundation may publish revised and/or new versions of
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+
+  Each version is given a distinguishing version number.  If the
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+  If the Program specifies that a proxy can decide which future
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+
+  Later license versions may give you additional or different
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+  15. Disclaimer of Warranty.
+
+  THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY
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+  16. Limitation of Liability.
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+
+  17. Interpretation of Sections 15 and 16.
+
+  If the disclaimer of warranty and limitation of liability provided
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
+  If you develop a new program, and you want it to be of the greatest
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+
+    This program is distributed in the hope that it will be useful,
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+
+    You should have received a copy of the GNU General Public License
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+
+Also add information on how to contact you by electronic and paper mail.
+
+  If the program does terminal interaction, make it output a short
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+
+    <program>  Copyright (C) <year>  <name of author>
+    This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
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+
+The hypothetical commands `show w' and `show c' should show the appropriate
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+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
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+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
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+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.                    GNU GENERAL PUBLIC LICENSE
+                       Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
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+
+                            Preamble
+
+  The GNU General Public License is a free, copyleft license for
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+  The licenses for most software and other practical works are designed
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+
+                       TERMS AND CONDITIONS
+
+  0. Definitions.
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+  "This License" refers to version 3 of the GNU General Public License.
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+  "Copyright" also means copyright-like laws that apply to other kinds of
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+
+                     END OF TERMS AND CONDITIONS
+
+            How to Apply These Terms to Your New Programs
+
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+possible use to the public, the best way to achieve this is to make it
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+    <program>  Copyright (C) <year>  <name of author>
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+    This is free software, and you are welcome to redistribute it
+    under certain conditions; type `show c' for details.
+
+The hypothetical commands `show w' and `show c' should show the appropriate
+parts of the General Public License.  Of course, your program's commands
+might be different; for a GUI interface, you would use an "about box".
+
+  You should also get your employer (if you work as a programmer) or school,
+if any, to sign a "copyright disclaimer" for the program, if necessary.
+For more information on this, and how to apply and follow the GNU GPL, see
+<https://www.gnu.org/licenses/>.
+
+  The GNU General Public License does not permit incorporating your program
+into proprietary programs.  If your program is a subroutine library, you
+may consider it more useful to permit linking proprietary applications with
+the library.  If this is what you want to do, use the GNU Lesser General
+Public License instead of this License.  But first, please read
+<https://www.gnu.org/licenses/why-not-lgpl.html>.
\ No newline at end of file
diff --git a/osam/_models/yoloworld/__init__.py b/osam/_models/yoloworld/__init__.py
new file mode 100644
index 0000000..6957254
--- /dev/null
+++ b/osam/_models/yoloworld/__init__.py
@@ -0,0 +1,181 @@
+from typing import Tuple
+
+import imgviz
+import numpy as np
+import onnxruntime
+from loguru import logger
+
+from ... import types
+from . import clip
+
+
+class _YoloWorld(types.Model):
+    _image_size: int
+
+    def generate(self, request: types.GenerateRequest) -> types.GenerateResponse:
+        if request.prompt is None:
+            prompt = types.Prompt(texts=["anything"])
+            logger.warning(
+                "Prompt is not given, so using 'anything' as prompt: {prompt!r}",
+                prompt=prompt,
+            )
+        else:
+            prompt = request.prompt
+
+        if prompt.texts is None:
+            raise ValueError("prompt.texts is required: prompt=%r", prompt)
+        token = clip.tokenize(texts=prompt.texts + [" "])
+        (text_features,) = self._inference_sessions["textual"].run(
+            None, {"input": token}
+        )
+        text_features = text_features / np.linalg.norm(
+            text_features, ord=2, axis=1, keepdims=True
+        )
+        #
+        if request.image is None:
+            raise ValueError("request.image is required: request=%r", request)
+        transformed_image, original_image_hw, padding_hw = _transform_image(
+            image=request.image, image_size=self._image_size
+        )
+        scores, bboxes = self._inference_sessions["yolo"].run(
+            output_names=["scores", "boxes"],
+            input_feed={
+                "images": transformed_image[None],
+                "text_features": text_features[None],
+            },
+        )
+        scores = scores[0]
+        bboxes = bboxes[0]
+        #
+        iou_threshold = 1.0 if prompt.iou_threshold is None else prompt.iou_threshold
+        score_threshold = (
+            0.0 if prompt.score_threshold is None else prompt.score_threshold
+        )
+        max_annotations = (
+            len(bboxes) if prompt.max_annotations is None else prompt.max_annotations
+        )
+        bboxes, scores, labels = _non_maximum_suppression(
+            inference_session=self._inference_sessions["nms"],
+            boxes=bboxes,
+            scores=scores,
+            iou_threshold=iou_threshold,
+            score_threshold=score_threshold,
+            max_num_detections=max_annotations,
+        )
+        bboxes = _untransform_bboxes(
+            bboxes=bboxes,
+            image_size=self._image_size,
+            original_image_hw=original_image_hw,
+            padding_hw=padding_hw,
+        )
+        annotations = [
+            types.Annotation(
+                bounding_box=types.BoundingBox(
+                    xmin=bbox[0], ymin=bbox[1], xmax=bbox[2], ymax=bbox[3]
+                ),
+                text=prompt.texts[label],
+                score=score,
+            )
+            for bbox, label, score in zip(bboxes, labels, scores)
+        ]
+        return types.GenerateResponse(
+            model=self.name,
+            image_embedding=None,
+            annotations=annotations,
+        )
+
+
+class YoloWorldXL(_YoloWorld):
+    name: str = "yoloworld:latest"
+
+    _image_size = 640
+    _blobs = {
+        "textual": types.Blob(
+            url="https://clip-as-service.s3.us-east-2.amazonaws.com/models/onnx/ViT-B-32/textual.onnx",
+            hash="sha256:55c85d8cbb096023781c1d13c557eb95d26034c111bd001b7360fdb7399eec68",
+        ),
+        "yolo": types.Blob(
+            url="https://github.com/wkentaro/yolo-world-onnx/releases/download/v0.1.0/yolo_world_v2_xl_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_lvis_minival.onnx",
+            hash="sha256:92660c6456766439a2670cf19a8a258ccd3588118622a15959f39e253731c05d",
+        ),
+        "nms": types.Blob(
+            url="https://github.com/wkentaro/yolo-world-onnx/releases/download/v0.1.0/non_maximum_suppression.onnx",
+            hash="sha256:328310ba8fdd386c7ca63fc9df3963cc47b1268909647abd469e8ebdf7f3d20a",
+        ),
+    }
+
+
+def _transform_image(
+    image: np.ndarray, image_size: int
+) -> Tuple[np.ndarray, Tuple[int, int], Tuple[int, int]]:
+    height, width = image.shape[:2]
+
+    scale = image_size / max(height, width)
+    image_resized = imgviz.resize(
+        image,
+        height=int(height * scale),
+        width=int(width * scale),
+        interpolation="linear",
+    )
+    pad_height = image_size - image_resized.shape[0]
+    pad_width = image_size - image_resized.shape[1]
+    image_resized = np.pad(
+        image_resized,
+        (
+            (pad_height // 2, pad_height - pad_height // 2),
+            (pad_width // 2, pad_width - pad_width // 2),
+            (0, 0),
+        ),
+        mode="constant",
+        constant_values=114,
+    )
+    input_image = image_resized.transpose(2, 0, 1).astype(np.float32) / 255.0
+    return input_image, (height, width), (pad_height, pad_width)
+
+
+def _untransform_bboxes(
+    bboxes: np.ndarray,
+    image_size: int,
+    original_image_hw: Tuple[int, int],
+    padding_hw: Tuple[int, int],
+) -> np.ndarray:
+    bboxes -= np.array([padding_hw[1] // 2, padding_hw[0] // 2] * 2)
+    bboxes /= image_size / max(original_image_hw)
+    bboxes[:, 0::2] = np.clip(bboxes[:, 0::2], 0, original_image_hw[1])
+    bboxes[:, 1::2] = np.clip(bboxes[:, 1::2], 0, original_image_hw[0])
+    bboxes = bboxes.round().astype(int)
+    return bboxes
+
+
+def _non_maximum_suppression(
+    inference_session: onnxruntime.InferenceSession,
+    boxes: np.ndarray,
+    scores: np.ndarray,
+    iou_threshold: float,
+    score_threshold: float,
+    max_num_detections: int,
+) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    selected_indices = inference_session.run(
+        output_names=["selected_indices"],
+        input_feed={
+            "boxes": boxes[None, :, :],
+            "scores": scores[None, :, :].transpose(0, 2, 1),
+            "max_output_boxes_per_class": np.array(
+                [max_num_detections], dtype=np.int64
+            ),
+            "iou_threshold": np.array([iou_threshold], dtype=np.float32),
+            "score_threshold": np.array([score_threshold], dtype=np.float32),
+        },
+    )[0]
+    labels = selected_indices[:, 1]
+    box_indices = selected_indices[:, 2]
+    boxes = boxes[box_indices]
+    scores = scores[box_indices, labels]
+
+    if len(boxes) > max_num_detections:
+        keep_indices = np.argsort(scores)[-max_num_detections:]
+        boxes = boxes[keep_indices]
+        scores = scores[keep_indices]
+        labels = labels[keep_indices]
+
+    return boxes, scores, labels
diff --git a/osam/_models/yoloworld/clip/LICENSE b/osam/_models/yoloworld/clip/LICENSE
new file mode 100644
index 0000000..c123b69
--- /dev/null
+++ b/osam/_models/yoloworld/clip/LICENSE
@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2021 OpenAI
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
diff --git a/osam/_models/yoloworld/clip/__init__.py b/osam/_models/yoloworld/clip/__init__.py
new file mode 100644
index 0000000..def9fd2
--- /dev/null
+++ b/osam/_models/yoloworld/clip/__init__.py
@@ -0,0 +1 @@
+from .clip import tokenize  # noqa: F401
diff --git a/osam/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz b/osam/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz
new file mode 100644
index 0000000..7b5088a
Binary files /dev/null and b/osam/_models/yoloworld/clip/bpe_simple_vocab_16e6.txt.gz differ
diff --git a/osam/_models/yoloworld/clip/clip.py b/osam/_models/yoloworld/clip/clip.py
new file mode 100644
index 0000000..9824665
--- /dev/null
+++ b/osam/_models/yoloworld/clip/clip.py
@@ -0,0 +1,32 @@
+# https://github.com/openai/CLIP/blob/main/clip/clip.py
+
+from typing import List
+
+import numpy as np
+
+from .simple_tokenizer import SimpleTokenizer
+
+_tokenizer = SimpleTokenizer()
+
+
+def tokenize(
+    texts: List[str], context_length: int = 77, truncate: bool = False
+) -> np.ndarray:
+    sot_token = _tokenizer.encoder["<|startoftext|>"]
+    eot_token = _tokenizer.encoder["<|endoftext|>"]
+    all_tokens = [[sot_token] + _tokenizer.encode(text) + [eot_token] for text in texts]
+
+    result = np.zeros((len(all_tokens), context_length), dtype=np.int64)
+
+    for i, tokens in enumerate(all_tokens):
+        if len(tokens) > context_length:
+            if truncate:
+                tokens = tokens[:context_length]
+                tokens[-1] = eot_token
+            else:
+                raise RuntimeError(
+                    f"Input {texts[i]} is too long for context length {context_length}"
+                )
+        result[i, : len(tokens)] = tokens
+
+    return result
diff --git a/osam/_models/yoloworld/clip/simple_tokenizer.py b/osam/_models/yoloworld/clip/simple_tokenizer.py
new file mode 100644
index 0000000..ccad974
--- /dev/null
+++ b/osam/_models/yoloworld/clip/simple_tokenizer.py
@@ -0,0 +1,152 @@
+# https://github.com/openai/CLIP/blob/main/clip/simple_tokenizer.py
+
+import functools
+import gzip
+import html
+import os
+import re
+from typing import Any
+from typing import List
+
+
+@functools.lru_cache()
+def default_bpe():
+    return os.path.join(
+        os.path.dirname(os.path.abspath(__file__)), "bpe_simple_vocab_16e6.txt.gz"
+    )
+
+
+@functools.lru_cache()
+def bytes_to_unicode():
+    """
+    Returns list of utf-8 byte and a corresponding list of unicode strings.
+    The reversible bpe codes work on unicode strings.
+    This means you need a large # of unicode characters in your vocab if you want to avoid UNKs.
+    When you're at something like a 10B token dataset you end up needing around 5K for decent coverage.
+    This is a signficant percentage of your normal, say, 32K bpe vocab.
+    To avoid that, we want lookup tables between utf-8 bytes and unicode strings.
+    And avoids mapping to whitespace/control characters the bpe code barfs on.
+    """  # noqa: E501
+    bs = (
+        list(range(ord("!"), ord("~") + 1))
+        + list(range(ord("¡"), ord("¬") + 1))
+        + list(range(ord("®"), ord("ÿ") + 1))
+    )
+    cs = bs[:]
+    n = 0
+    for b in range(2**8):
+        if b not in bs:
+            bs.append(b)
+            cs.append(2**8 + n)
+            n += 1
+    cs = [chr(n) for n in cs]
+    return dict(zip(bs, cs))
+
+
+def get_pairs(word):
+    """Return set of symbol pairs in a word.
+    Word is represented as tuple of symbols (symbols being variable-length strings).
+    """
+    pairs = set()
+    prev_char = word[0]
+    for char in word[1:]:
+        pairs.add((prev_char, char))
+        prev_char = char
+    return pairs
+
+
+def basic_clean(text):
+    text = html.unescape(html.unescape(text))
+    return text.strip()
+
+
+def whitespace_clean(text):
+    text = re.sub(r"\s+", " ", text)
+    text = text.strip()
+    return text
+
+
+class SimpleTokenizer(object):
+    def __init__(self, bpe_path: str = default_bpe()):
+        self.byte_encoder = bytes_to_unicode()
+        self.byte_decoder = {v: k for k, v in self.byte_encoder.items()}
+        merges: List[Any]
+        merges = gzip.open(bpe_path).read().decode("utf-8").split("\n")
+        merges = merges[1 : 49152 - 256 - 2 + 1]
+        merges = [tuple(merge.split()) for merge in merges]
+        vocab = list(bytes_to_unicode().values())
+        vocab = vocab + [v + "</w>" for v in vocab]
+        for merge in merges:
+            vocab.append("".join(merge))
+        vocab.extend(["<|startoftext|>", "<|endoftext|>"])
+        self.encoder = dict(zip(vocab, range(len(vocab))))
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self.bpe_ranks = dict(zip(merges, range(len(merges))))
+        self.cache = {
+            "<|startoftext|>": "<|startoftext|>",
+            "<|endoftext|>": "<|endoftext|>",
+        }
+        self.pat = re.compile(
+            r"""<\|startoftext\|>|<\|endoftext\|>|'s|'t|'re|'ve|'m|'ll|'d|\w+|\d+|[^\s\w]+""",  # noqa: E501
+            re.UNICODE | re.IGNORECASE,
+        )
+
+    def bpe(self, token):
+        if token in self.cache:
+            return self.cache[token]
+        word = tuple(token[:-1]) + (token[-1] + "</w>",)
+        pairs = get_pairs(word)
+
+        if not pairs:
+            return token + "</w>"
+
+        while True:
+            bigram = min(pairs, key=lambda pair: self.bpe_ranks.get(pair, float("inf")))
+            if bigram not in self.bpe_ranks:
+                break
+            first, second = bigram
+            new_word = []
+            i = 0
+            while i < len(word):
+                try:
+                    j = word.index(first, i)
+                    new_word.extend(word[i:j])
+                    i = j
+                except Exception:
+                    new_word.extend(word[i:])
+                    break
+
+                if word[i] == first and i < len(word) - 1 and word[i + 1] == second:
+                    new_word.append(first + second)
+                    i += 2
+                else:
+                    new_word.append(word[i])
+                    i += 1
+            new_word = tuple(new_word)
+            word = new_word
+            if len(word) == 1:
+                break
+            else:
+                pairs = get_pairs(word)
+        word = " ".join(word)
+        self.cache[token] = word
+        return word
+
+    def encode(self, text):
+        bpe_tokens = []
+        text = whitespace_clean(basic_clean(text)).lower()
+        for token in re.findall(self.pat, text):
+            token = "".join(self.byte_encoder[b] for b in token.encode("utf-8"))
+            bpe_tokens.extend(
+                self.encoder[bpe_token] for bpe_token in self.bpe(token).split(" ")
+            )
+        return bpe_tokens
+
+    def decode(self, tokens):
+        text = "".join([self.decoder[token] for token in tokens])
+        text = (
+            bytearray([self.byte_decoder[c] for c in text])
+            .decode("utf-8", errors="replace")
+            .replace("</w>", " ")
+        )
+        return text
diff --git a/osam/_server.py b/osam/_server.py
index 28cf33b..67a0bb1 100644
--- a/osam/_server.py
+++ b/osam/_server.py
@@ -2,8 +2,9 @@
 import os
 
 import fastapi
-from osam_core import apis
-from osam_core import types
+
+from . import apis
+from . import types
 
 app: fastapi.FastAPI = fastapi.FastAPI()
 
diff --git a/osam/apis.py b/osam/apis.py
index 5f37522..24c8f62 100644
--- a/osam/apis.py
+++ b/osam/apis.py
@@ -1 +1,44 @@
-from osam_core.apis import *  # noqa: F403
+from typing import List
+from typing import Optional
+from typing import Type
+
+from . import _models
+from . import types
+
+running_model: Optional[types.Model] = None
+
+registered_model_types: List[Type] = [
+    _models.EfficientSam10m,
+    _models.EfficientSam30m,
+    _models.Sam100m,
+    _models.Sam300m,
+    _models.Sam600m,
+    _models.YoloWorldXL,
+]
+
+
+def get_model_type_by_name(name: str) -> Type:
+    model_name: str
+    if ":" in name:
+        model_name = name
+    else:
+        model_name = f"{name}:latest"
+
+    for cls in registered_model_types:
+        if cls.name == model_name:
+            break
+    else:
+        raise ValueError(f"Model {name!r} not found.")
+    return cls
+
+
+def generate(request: types.GenerateRequest) -> types.GenerateResponse:
+    global running_model
+
+    model_type = get_model_type_by_name(name=request.model)
+    if running_model is None or running_model.name != model_type.name:
+        running_model = model_type()
+    assert running_model is not None
+
+    response: types.GenerateResponse = running_model.generate(request=request)
+    return response
diff --git a/osam/apis_test.py b/osam/apis_test.py
new file mode 100644
index 0000000..e69de29
diff --git a/osam/types.py b/osam/types.py
deleted file mode 100644
index d077ed8..0000000
--- a/osam/types.py
+++ /dev/null
@@ -1 +0,0 @@
-from osam_core.types import *  # noqa: F403
diff --git a/osam/types/__init__.py b/osam/types/__init__.py
new file mode 100644
index 0000000..4b8bebe
--- /dev/null
+++ b/osam/types/__init__.py
@@ -0,0 +1,26 @@
+import warnings
+
+from ._annotation import Annotation  # noqa: F401
+from ._blob import Blob  # noqa: F401
+from ._bounding_box import BoundingBox  # noqa: F401
+from ._generate import GenerateRequest  # noqa: F401
+from ._generate import GenerateResponse  # noqa: F401
+from ._image_embedding import ImageEmbedding  # noqa: F401
+from ._model import Model  # noqa: F401
+from ._prompt import Prompt  # noqa: F401
+
+
+class ModelBase(Model):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        warnings.warning(
+            "This class is deprecated, use `Model` instead.", DeprecationWarning
+        )
+
+
+class ModelBlob(Blob):
+    def __init__(self, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        warnings.warning(
+            "This class is deprecated, use `Blob` instead.", DeprecationWarning
+        )
diff --git a/osam/types/_annotation.py b/osam/types/_annotation.py
new file mode 100644
index 0000000..cbde3fd
--- /dev/null
+++ b/osam/types/_annotation.py
@@ -0,0 +1,30 @@
+from typing import Optional
+
+import numpy as np
+import pydantic
+
+from .. import _json
+from ._bounding_box import BoundingBox
+
+
+class Annotation(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+
+    bounding_box: Optional[BoundingBox] = pydantic.Field(default=None)
+    text: Optional[str] = pydantic.Field(default=None)
+    score: Optional[float] = pydantic.Field(default=None)
+    mask: Optional[np.ndarray] = pydantic.Field(default=None)
+
+    @pydantic.field_validator("mask")
+    def validate_mask(cls, mask: Optional[np.ndarray]) -> Optional[np.ndarray]:
+        if mask is None:
+            return None
+        if mask.dtype != bool:
+            raise ValueError("mask must be boolean arrays")
+        return mask
+
+    @pydantic.field_serializer("mask")
+    def serialize_mask(self, mask: Optional[np.ndarray]) -> Optional[str]:
+        if mask is None:
+            return None
+        return _json.image_ndarray_to_b64data(ndarray=mask.view(np.uint8) * 255)
diff --git a/osam/types/_blob.py b/osam/types/_blob.py
new file mode 100644
index 0000000..34e01b2
--- /dev/null
+++ b/osam/types/_blob.py
@@ -0,0 +1,39 @@
+import dataclasses
+import os
+
+import gdown
+from loguru import logger
+
+
+@dataclasses.dataclass
+class Blob:
+    url: str
+    hash: str
+
+    @property
+    def path(self):
+        return os.path.expanduser(f"~/.cache/osam/models/blobs/{self.hash}")
+
+    @property
+    def size(self):
+        if os.path.exists(self.path):
+            return os.stat(self.path).st_size
+        else:
+            return None
+
+    @property
+    def modified_at(self):
+        if os.path.exists(self.path):
+            return os.stat(self.path).st_mtime
+        else:
+            return None
+
+    def pull(self):
+        gdown.cached_download(url=self.url, path=self.path, hash=self.hash)
+
+    def remove(self):
+        if os.path.exists(self.path):
+            logger.debug("Removing blob {path!r}", path=self.path)
+            os.remove(self.path)
+        else:
+            logger.warning("Blob {path!r} not found", path=self.path)
diff --git a/osam/types/_bounding_box.py b/osam/types/_bounding_box.py
new file mode 100644
index 0000000..1915f0c
--- /dev/null
+++ b/osam/types/_bounding_box.py
@@ -0,0 +1,8 @@
+import pydantic
+
+
+class BoundingBox(pydantic.BaseModel):
+    xmin: int
+    ymin: int
+    xmax: int
+    ymax: int
diff --git a/osam/types/_generate.py b/osam/types/_generate.py
new file mode 100644
index 0000000..5f4ea8e
--- /dev/null
+++ b/osam/types/_generate.py
@@ -0,0 +1,38 @@
+from typing import List
+from typing import Optional
+from typing import Union
+
+import numpy as np
+import pydantic
+
+from .. import _json
+from ._annotation import Annotation
+from ._image_embedding import ImageEmbedding
+from ._prompt import Prompt
+
+
+class GenerateRequest(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+
+    model: str
+    image_embedding: Optional[ImageEmbedding] = pydantic.Field(default=None)
+    image: Optional[np.ndarray] = pydantic.Field(default=None)
+    prompt: Optional[Prompt] = pydantic.Field(default=None)
+    annotations: Optional[List[Annotation]] = pydantic.Field(default=None)
+
+    @pydantic.field_validator("image", mode="before")
+    @classmethod
+    def validate_image(
+        cls, image: Optional[Union[str, np.ndarray]]
+    ) -> Optional[np.ndarray]:
+        if isinstance(image, str):
+            return _json.image_b64data_to_ndarray(b64data=image)
+        return image
+
+
+class GenerateResponse(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+
+    model: str
+    image_embedding: Optional[ImageEmbedding] = pydantic.Field(default=None)
+    annotations: List[Annotation]
diff --git a/osam/types/_image_embedding.py b/osam/types/_image_embedding.py
new file mode 100644
index 0000000..915b3e4
--- /dev/null
+++ b/osam/types/_image_embedding.py
@@ -0,0 +1,24 @@
+from typing import List
+
+import numpy as np
+import pydantic
+
+
+class ImageEmbedding(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+
+    original_height: int
+    original_width: int
+    embedding: np.ndarray
+
+    @pydantic.field_validator("embedding")
+    def validate_embedding(cls, embedding):
+        if embedding.ndim != 3:
+            raise ValueError(
+                "embedding must be 3-dimensional: (embedding_dim, height, width)"
+            )
+        return embedding
+
+    @pydantic.field_serializer("embedding")
+    def serialize_embedding(self, embedding: np.ndarray) -> List[List[List[float]]]:
+        return embedding.tolist()
diff --git a/osam/types/_model.py b/osam/types/_model.py
new file mode 100644
index 0000000..0764618
--- /dev/null
+++ b/osam/types/_model.py
@@ -0,0 +1,102 @@
+import hashlib
+from typing import Dict
+from typing import Optional
+
+import numpy as np
+import onnxruntime
+from loguru import logger
+
+from .. import _contextlib
+from ._blob import Blob
+from ._generate import GenerateRequest
+from ._generate import GenerateResponse
+from ._image_embedding import ImageEmbedding
+
+
+class Model:
+    name: str
+
+    _blobs: Dict[str, Blob]
+    _inference_sessions: Dict[str, onnxruntime.InferenceSession]
+
+    def __init__(self):
+        logger.debug("Initializing model {name}", name=self.name)
+
+        self.pull()
+
+        providers = None
+        self._inference_sessions = {}
+        for key, blob in self._blobs.items():
+            try:
+                # Try to use all of the available providers e.g., cuda, tensorrt.
+                if providers is None:
+                    if "CUDAExecutionProvider" in onnxruntime.get_available_providers():
+                        providers = ["CUDAExecutionProvider", "CPUExecutionProvider"]
+                    else:
+                        providers = ["CPUExecutionProvider"]
+                # Suppress all the error messages from the missing providers.
+                with _contextlib.suppress():
+                    inference_session = onnxruntime.InferenceSession(
+                        blob.path, providers=providers
+                    )
+            except Exception as e:
+                # Even though there is fallback in onnxruntime, it won't always work.
+                # e.g., CUDA is installed and CUDA_PATH is set, but CUDA_VISIBLE_DEVICES
+                # is empty. We fallback to cpu in such cases.
+                logger.error(
+                    "Failed to create inference session with providers {providers!r}. "
+                    "Falling back to ['CPUExecutionProvider']",
+                    providers=providers,
+                    e=e,
+                )
+                providers = ["CPUExecutionProvider"]
+                inference_session = onnxruntime.InferenceSession(
+                    blob.path, providers=providers
+                )
+            self._inference_sessions[key] = inference_session
+
+            providers = inference_session.get_providers()
+        logger.info(
+            "Initialized inference sessions with providers {providers!r}",
+            providers=providers,
+        )
+
+    @classmethod
+    def pull(cls):
+        for blob in cls._blobs.values():
+            blob.pull()
+
+    @classmethod
+    def remove(cls):
+        for blob in cls._blobs.values():
+            blob.remove()
+
+    @classmethod
+    def get_id(cls) -> str:
+        return hashlib.md5(
+            "+".join(blob.hash for blob in cls._blobs.values()).encode()
+        ).hexdigest()[:12]
+
+    @classmethod
+    def get_size(cls) -> Optional[int]:
+        size = 0
+        for blob in cls._blobs.values():
+            if blob.size is None:
+                return None
+            size += blob.size
+        return size
+
+    @classmethod
+    def get_modified_at(cls) -> Optional[float]:
+        modified_at = 0
+        for blob in cls._blobs.values():
+            if blob.modified_at is None:
+                return None
+            modified_at = max(modified_at, blob.modified_at)
+        return modified_at
+
+    def encode_image(self, image: np.ndarray) -> ImageEmbedding:
+        raise NotImplementedError
+
+    def generate(self, request: GenerateRequest) -> GenerateResponse:
+        raise NotImplementedError
diff --git a/osam/types/_prompt.py b/osam/types/_prompt.py
new file mode 100644
index 0000000..f2b20ae
--- /dev/null
+++ b/osam/types/_prompt.py
@@ -0,0 +1,67 @@
+from typing import List
+from typing import Optional
+from typing import Type
+from typing import Union
+
+import numpy as np
+import pydantic
+
+
+class Prompt(pydantic.BaseModel):
+    model_config = pydantic.ConfigDict(arbitrary_types_allowed=True)
+
+    points: Optional[np.ndarray] = pydantic.Field(default=None)
+    point_labels: Optional[np.ndarray] = pydantic.Field(default=None)
+    texts: Optional[List[str]] = pydantic.Field(default=None)
+
+    iou_threshold: Optional[float] = pydantic.Field(default=0.5)
+    score_threshold: Optional[float] = pydantic.Field(default=0.1)
+    max_annotations: Optional[int] = pydantic.Field(default=100)
+
+    @pydantic.field_serializer("points")
+    def _serialize_points(self: "Prompt", points: np.ndarray) -> List[List[float]]:
+        return points.tolist()
+
+    @pydantic.field_serializer("point_labels")
+    def _serialize_point_labels(self: "Prompt", point_labels: np.ndarray) -> List[int]:
+        return point_labels.tolist()
+
+    @pydantic.field_validator("points", mode="before")
+    @classmethod
+    def _validate_points(cls: Type, points: Union[list, np.ndarray]):
+        if isinstance(points, list):
+            points = np.array(points, dtype=float)
+        if points.ndim != 2:
+            raise ValueError("points must be 2-dimensional")
+        if points.shape[1] != 2:
+            raise ValueError("points must have 2 columns")
+        return points
+
+    @pydantic.field_validator("point_labels", mode="before")
+    @classmethod
+    def _validate_point_labels(cls: Type, point_labels: Union[list, np.ndarray]):
+        if isinstance(point_labels, list):
+            point_labels = np.array(point_labels, dtype=int)
+        if point_labels.ndim != 1:
+            raise ValueError("point_labels must be 1-dimensional")
+        if not set(np.unique(point_labels).tolist()).issubset({0, 1, 2, 3}):
+            raise ValueError("point_labels must contain only 0, 1, 2, or 3")
+        return point_labels
+
+    @pydantic.model_validator(mode="after")
+    def _validate_prompt(self) -> "Prompt":
+        if self.points is None and self.point_labels is None:
+            if self.texts is None:
+                raise ValueError(
+                    "texts must be provided when points and point_labels "
+                    "are not provided"
+                )
+        elif (
+            self.points is None
+            or self.point_labels is None
+            or self.points.shape[0] != self.point_labels.shape[0]
+        ):
+            raise ValueError(
+                "points and point_labels must have the same number of rows"
+            )
+        return self
diff --git a/pyproject.toml b/pyproject.toml
index e82b9fe..724e7aa 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -31,14 +31,13 @@ classifiers = [
 ]
 dependencies = [
   "click",
+  "gdown",
   "fastapi",
   "imgviz",
   "loguru",
-  "osam-core",
-  "osam-efficientsam",
-  "osam-sam",
-  "osam-yoloworld",
+  "onnxruntime",
   "Pillow",
+  "pydantic",
   "uvicorn",
 ]
 dynamic = ["readme", "version"]