diff --git a/lilac/embeddings/bge.py b/lilac/embeddings/bge.py
index 0c578b1e..5ab1560f 100644
--- a/lilac/embeddings/bge.py
+++ b/lilac/embeddings/bge.py
@@ -1,9 +1,11 @@
 """Gegeral Text Embeddings (GTE) model. Open-source model, designed to run on device."""
 import gc
-from typing import TYPE_CHECKING, ClassVar, Iterator, Optional
+from typing import TYPE_CHECKING, ClassVar, Optional
 
 from typing_extensions import override
 
+from ..utils import log
+
 if TYPE_CHECKING:
   from FlagEmbedding import BGEM3FlagModel
 
@@ -15,7 +17,7 @@
 from ..splitters.spacy_splitter import clustering_spacy_chunker
 from ..tasks import TaskExecutionType
 from .embedding import chunked_compute_embedding
-from .transformer_utils import SENTENCE_TRANSFORMER_BATCH_SIZE, setup_model_device
+from .transformer_utils import SENTENCE_TRANSFORMER_BATCH_SIZE
 
 # See https://huggingface.co/spaces/mteb/leaderboard for leaderboard of models.
 BGE_M3 = 'BAAI/bge-m3'
@@ -33,8 +35,11 @@ def _get_and_cache_bge_m3(model_name: str) -> 'BGEM3FlagModel':
   model = BGEM3FlagModel(
     'BAAI/bge-m3', use_fp16=True
   )  # Setting use_fp16 to True speeds up computation with a slight performance degradation
+
+  log(f'[{model_name}] Using device:', model.device)
+
+  # NOTE: we don't call setup model and device here as this happens internally.
   return model
-  return setup_model_device(model, model_name)
 
 
 class BGEM3(TextEmbeddingSignal):
@@ -62,7 +67,7 @@ def setup(self) -> None:
   def compute(self, docs: list[str]) -> list[Optional[Item]]:
     """Call the embedding function."""
 
-    def _encode(doc):
+    def _encode(doc: list[str]):
       # Extract the dense vectors from the model.
       return self._model.encode(doc)['dense_vecs']
 
@@ -73,16 +78,11 @@ def _encode(doc):
       _encode, docs, self.local_batch_size * 16, chunker=clustering_spacy_chunker
     )
 
-  @override
-  def compute_garden(self, docs: Iterator[str]) -> Iterator[Item]:
-    raise NotImplementedError('Garden computation is not supported for BGE-M3.')
-
   @override
   def teardown(self) -> None:
     if not hasattr(self, '_model'):
       return
 
-    self._model.cpu()
     del self._model
     gc.collect()
 
diff --git a/lilac/embeddings/nomic_embed.py b/lilac/embeddings/nomic_embed.py
new file mode 100644
index 00000000..77c525a7
--- /dev/null
+++ b/lilac/embeddings/nomic_embed.py
@@ -0,0 +1,108 @@
+"""Gegeral Text Embeddings (GTE) model. Open-source model, designed to run on device."""
+import gc
+from typing import TYPE_CHECKING, ClassVar, Optional
+
+from typing_extensions import override
+
+if TYPE_CHECKING:
+  from sentence_transformers import SentenceTransformer
+
+import functools
+
+from ..schema import Item
+from ..signal import TextEmbeddingSignal
+from ..splitters.spacy_splitter import clustering_spacy_chunker
+from ..tasks import TaskExecutionType
+from .embedding import chunked_compute_embedding
+from .transformer_utils import SENTENCE_TRANSFORMER_BATCH_SIZE, setup_model_device
+
+# See https://huggingface.co/spaces/mteb/leaderboard for leaderboard of models.
+NOMIC_EMBED = 'nomic-ai/nomic-embed-text-v1.5'
+
+
+@functools.cache
+def _get_and_cache_model(model_name: str) -> 'SentenceTransformer':
+  try:
+    from sentence_transformers import SentenceTransformer
+  except ImportError:
+    raise ImportError(
+      'Could not import the "sentence_transformers" python package. '
+      'Please install it with `pip install "sentence_transformers".'
+    )
+  return setup_model_device(SentenceTransformer(model_name, trust_remote_code=True), model_name)
+
+
+class NomicEmbed15(TextEmbeddingSignal):
+  """Computes Nomic Embeddings 1.5 full (768 dimensions).
+
+  <br>This embedding runs on-device. See the [model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
+  for details.
+  """
+
+  name: ClassVar[str] = 'nomic-embed-1.5-768'
+  display_name: ClassVar[str] = 'Nomic Embeddings 1.5 784'
+  local_batch_size: ClassVar[int] = SENTENCE_TRANSFORMER_BATCH_SIZE
+  local_parallelism: ClassVar[int] = 1
+  local_strategy: ClassVar[TaskExecutionType] = 'threads'
+  supports_garden: ClassVar[bool] = False
+
+  _model_name = NOMIC_EMBED
+  _model: 'SentenceTransformer'
+  _matryoshka_dim = 768
+
+  @override
+  def setup(self) -> None:
+    self._model = _get_and_cache_model(self._model_name)
+
+  @override
+  def compute(self, docs: list[str]) -> list[Optional[Item]]:
+    """Call the embedding function."""
+    try:
+      import torch.nn.functional as F
+    except ImportError:
+      raise ImportError(
+        'Could not import the "sentence_transformers" python package. '
+        'Please install it with `pip install "sentence_transformers".'
+      )
+
+    def _encode(doc: list[str]):
+      embeddings = self._model.encode(doc, convert_to_tensor=True)
+      # Extract the dense vectors from the model.
+      embeddings = F.layer_norm(embeddings, normalized_shape=(embeddings.shape[1],))
+      embeddings = embeddings[:, : self._matryoshka_dim]
+      return embeddings.cpu().numpy()
+
+    # While we get docs in batches of 1024, the chunker expands that by a factor of 3-10.
+    # The sentence transformer API actually does batching internally, so we pass
+    # local_batch_size * 16 to allow the library to see all the chunks at once.
+    return chunked_compute_embedding(
+      _encode, docs, self.local_batch_size * 16, chunker=clustering_spacy_chunker
+    )
+
+  @override
+  def teardown(self) -> None:
+    if not hasattr(self, '_model'):
+      return
+
+    self._model.cpu()
+    del self._model
+    gc.collect()
+
+    try:
+      import torch
+
+      torch.cuda.empty_cache()
+    except ImportError:
+      pass
+
+
+class NomicEmbed15_256(NomicEmbed15):
+  """Computes Nomic Embeddings 1.5 (256 dimensions).
+
+  <br>This embedding runs on-device. See the [model card](https://huggingface.co/nomic-ai/nomic-embed-text-v1.5)
+  for details.
+  """
+
+  name: ClassVar[str] = 'nomic-embed-1.5-256'
+  display_name: ClassVar[str] = 'Nomic Embeddings 1.5 256'
+  _matryoshka_dim = 256
diff --git a/lilac/signals/default_signals.py b/lilac/signals/default_signals.py
index 451788ec..b85c7193 100644
--- a/lilac/signals/default_signals.py
+++ b/lilac/signals/default_signals.py
@@ -4,6 +4,7 @@
 from ..embeddings.cohere import Cohere
 from ..embeddings.gte import GTEBase, GTESmall, GTETiny
 from ..embeddings.jina import JinaV2Base, JinaV2Small
+from ..embeddings.nomic_embed import NomicEmbed15, NomicEmbed15_256
 from ..embeddings.openai import OpenAIEmbedding
 from ..embeddings.sbert import SBERT
 from ..signal import register_signal
@@ -46,3 +47,5 @@ def register_default_signals() -> None:
   register_signal(JinaV2Base, exists_ok=True)
 
   register_signal(BGEM3, exists_ok=True)
+  register_signal(NomicEmbed15, exists_ok=True)
+  register_signal(NomicEmbed15_256, exists_ok=True)
diff --git a/poetry.lock b/poetry.lock
index f5717225..df5a2091 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1421,6 +1421,17 @@ files = [
     {file = "duckdb-0.9.2.tar.gz", hash = "sha256:3843afeab7c3fc4a4c0b53686a4cc1d9cdbdadcbb468d60fef910355ecafd447"},
 ]
 
+[[package]]
+name = "einops"
+version = "0.7.0"
+description = "A new flavour of deep learning operations"
+optional = true
+python-versions = ">=3.8"
+files = [
+    {file = "einops-0.7.0-py3-none-any.whl", hash = "sha256:0f3096f26b914f465f6ff3c66f5478f9a5e380bb367ffc6493a68143fbbf1fd1"},
+    {file = "einops-0.7.0.tar.gz", hash = "sha256:b2b04ad6081a3b227080c9bf5e3ace7160357ff03043cd66cc5b2319eb7031d1"},
+]
+
 [[package]]
 name = "email-reply-parser"
 version = "0.5.12"
@@ -6056,25 +6067,26 @@ win32 = ["pywin32"]
 
 [[package]]
 name = "sentence-transformers"
-version = "2.2.2"
+version = "2.3.1"
 description = "Multilingual text embeddings"
 optional = true
-python-versions = ">=3.6.0"
+python-versions = ">=3.8.0"
 files = [
-    {file = "sentence-transformers-2.2.2.tar.gz", hash = "sha256:dbc60163b27de21076c9a30d24b5b7b6fa05141d68cf2553fa9a77bf79a29136"},
+    {file = "sentence-transformers-2.3.1.tar.gz", hash = "sha256:d589d85a464f45338cdbdf99ea715f8068e1fb01c582e0bcdbf60bcf3eade6d0"},
+    {file = "sentence_transformers-2.3.1-py3-none-any.whl", hash = "sha256:285d6637726c3b002186aa4b8bcace1101364b32671fb605297c4c2636b8190e"},
 ]
 
 [package.dependencies]
-huggingface-hub = ">=0.4.0"
+huggingface-hub = ">=0.15.1"
 nltk = "*"
 numpy = "*"
+Pillow = "*"
 scikit-learn = "*"
 scipy = "*"
 sentencepiece = "*"
-torch = ">=1.6.0"
-torchvision = "*"
+torch = ">=1.11.0"
 tqdm = "*"
-transformers = ">=4.6.0,<5.0.0"
+transformers = ">=4.32.0,<5.0.0"
 
 [[package]]
 name = "sentencepiece"
@@ -7007,44 +7019,6 @@ typing-extensions = "*"
 dynamo = ["jinja2"]
 opt-einsum = ["opt-einsum (>=3.3)"]
 
-[[package]]
-name = "torchvision"
-version = "0.16.2"
-description = "image and video datasets and models for torch deep learning"
-optional = true
-python-versions = ">=3.8"
-files = [
-    {file = "torchvision-0.16.2-cp310-cp310-macosx_10_13_x86_64.whl", hash = "sha256:bc86f2800cb2c0c1a09c581409cdd6bff66e62f103dc83fc63f73346264c3756"},
-    {file = "torchvision-0.16.2-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:b024bd412df6d3a007dcebf311a894eb3c5c21e1af80d12be382bbcb097a7c3a"},
-    {file = "torchvision-0.16.2-cp310-cp310-manylinux1_x86_64.whl", hash = "sha256:e89f10f3c8351972b6e3fda95bc3e479ea8dbfc9dfcfd2c32902dbad4ba5cfc5"},
-    {file = "torchvision-0.16.2-cp310-cp310-manylinux2014_aarch64.whl", hash = "sha256:96c7583700112a410bdc4e1e4f118c429dab49c29c9a31a2cc3579bc9b08b19d"},
-    {file = "torchvision-0.16.2-cp310-cp310-win_amd64.whl", hash = "sha256:9f4032ebb3277fb07ff6a9b818d50a547fb8fcd89d958cfd9e773322454bb688"},
-    {file = "torchvision-0.16.2-cp311-cp311-macosx_10_13_x86_64.whl", hash = "sha256:67b1aaf8b8cb02ce75dd445f291a27c8036a502f8c0aa76e28c37a0faac2e153"},
-    {file = "torchvision-0.16.2-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:bef30d03e1d1c629761f4dca51d3b7d8a0dc0acce6f4068ab2a1634e8e7b64e0"},
-    {file = "torchvision-0.16.2-cp311-cp311-manylinux1_x86_64.whl", hash = "sha256:e59cc7b2bd1ab5c0ce4ae382e4e37be8f1c174e8b5de2f6a23c170de9ae28495"},
-    {file = "torchvision-0.16.2-cp311-cp311-manylinux2014_aarch64.whl", hash = "sha256:e130b08cc9b3cc73a6c59d6edf032394a322f9579bfd21d14bc2e1d0999aa758"},
-    {file = "torchvision-0.16.2-cp311-cp311-win_amd64.whl", hash = "sha256:8692ab1e48807e9604046a6f4beeb67b523294cee1b00828654bb0df2cfce2b2"},
-    {file = "torchvision-0.16.2-cp38-cp38-macosx_10_13_x86_64.whl", hash = "sha256:b82732dcf876a37c852772342aa6ee3480c03bb3e2a802ae109fc5f7e28d26e9"},
-    {file = "torchvision-0.16.2-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:4b065143d1a720fe8a9077fd4be35d491f98819ec80b3dbbc3ec64d0b707a906"},
-    {file = "torchvision-0.16.2-cp38-cp38-manylinux1_x86_64.whl", hash = "sha256:bc5f274e4ecd1b86062063cdf4fd385a1d39d147a3a2685fbbde9ff08bb720b8"},
-    {file = "torchvision-0.16.2-cp38-cp38-manylinux2014_aarch64.whl", hash = "sha256:335959c43b371c0474af34c1ef2a52efdc7603c45700d29e4475eeb02984170c"},
-    {file = "torchvision-0.16.2-cp38-cp38-win_amd64.whl", hash = "sha256:7fd22d86e08eba321af70cad291020c2cdeac069b00ce88b923ca52e06174769"},
-    {file = "torchvision-0.16.2-cp39-cp39-macosx_10_13_x86_64.whl", hash = "sha256:56115268b37f0b75364e3654e47ad9abc66ac34c1f9e5e3dfa89a22d6a40017a"},
-    {file = "torchvision-0.16.2-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:82805f8445b094f9d1e770390ee6cc86855e89955e08ce34af2e2274fc0e5c45"},
-    {file = "torchvision-0.16.2-cp39-cp39-manylinux1_x86_64.whl", hash = "sha256:3f4bd5fcbc361476e2e78016636ac7d5509e59d9962521f06eb98e6803898182"},
-    {file = "torchvision-0.16.2-cp39-cp39-manylinux2014_aarch64.whl", hash = "sha256:8199acdf8ab066a28b84a5b6f4d97b58976d9e164b1acc3a9d14fccfaf74bb3a"},
-    {file = "torchvision-0.16.2-cp39-cp39-win_amd64.whl", hash = "sha256:41dd4fa9f176d563fe9f1b9adef3b7e582cdfb60ce8c9bc51b094a025be687c9"},
-]
-
-[package.dependencies]
-numpy = "*"
-pillow = ">=5.3.0,<8.3.dev0 || >=8.4.dev0"
-requests = "*"
-torch = "2.1.2"
-
-[package.extras]
-scipy = ["scipy"]
-
 [[package]]
 name = "tornado"
 version = "6.4"
@@ -8161,6 +8135,7 @@ gte = ["sentence-transformers"]
 lang-detection = ["langdetect"]
 langsmith = ["langsmith"]
 llms = ["openai"]
+nomic = ["einops", "sentence-transformers"]
 openai = ["openai"]
 pii = ["detect-secrets", "presidio_analyzer"]
 sbert = ["sentence-transformers"]
@@ -8171,4 +8146,4 @@ text-stats = ["textacy"]
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<4.0"
-content-hash = "94b8c942172e5a02cb89c0fe8f7ea134169bdf5356d5486549bd8facb73ba8aa"
+content-hash = "5aec3cf990d020b4c1c66a1ebce20b00778021db680a4c4dd8fdf65aa9fb4295"
diff --git a/pyproject.toml b/pyproject.toml
index 60171251..ec1927fd 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -52,9 +52,10 @@ jinja2 = "^3.1.3"                                        # Used for directory li
 # LLM providers.
 cohere = { version = "^4.32", optional = true }
 openai = { version = "^1.7.1", optional = true }
-sentence-transformers = { version = "^2.2.2", optional = true } # SBERT on-device embeddings.
+sentence-transformers = { version = "^2.3.1", optional = true } # SBERT on-device embeddings.
 FlagEmbedding = { version = "^1.2.3", optional = true }         # bge on-device embeddings.
 transformers = { version = "^4.37.2", optional = true }         # bge on-device embeddings.
+einops = { version = "^0.7.0", optional = true }                # Nomic on-device embeddings.
 
 # Gmail source.
 email-reply-parser = { version = "^0.5.12", optional = true }
@@ -100,6 +101,7 @@ all = [
   "langsmith",
   "llama-hub",
   "llama-index",
+  "nomic",
   "openai",
   "presidio_analyzer",
   "sentence-transformers",
@@ -135,6 +137,7 @@ text_stats = ["textacy"]                      # Text statistics.
 # Individual embeddings.
 gte = ["sentence-transformers"]
 bge = ["FlagEmbedding", "transformers"]
+nomic = ["sentence-transformers", "einops"]
 sbert = ["sentence-transformers"]
 cohere = ["cohere"]
 openai = ["openai"]
diff --git a/web/lib/fastapi_client/models/ConceptSignal.ts b/web/lib/fastapi_client/models/ConceptSignal.ts
index e67095f9..19ea8906 100644
--- a/web/lib/fastapi_client/models/ConceptSignal.ts
+++ b/web/lib/fastapi_client/models/ConceptSignal.ts
@@ -11,7 +11,7 @@ export type ConceptSignal = {
     /**
      * The name of the pre-computed embedding.
      */
-    embedding: 'cohere' | 'sbert' | 'openai' | 'gte-tiny' | 'gte-small' | 'gte-base' | 'jina-v2-small' | 'jina-v2-base' | 'bge-m3';
+    embedding: 'cohere' | 'sbert' | 'openai' | 'gte-tiny' | 'gte-small' | 'gte-base' | 'jina-v2-small' | 'jina-v2-base' | 'bge-m3' | 'nomic-embed-1.5-768' | 'nomic-embed-1.5-256';
     namespace: string;
     concept_name: string;
     version?: (number | null);
diff --git a/web/lib/fastapi_client/models/SemanticSimilaritySignal.ts b/web/lib/fastapi_client/models/SemanticSimilaritySignal.ts
index 7adb8843..ec964d36 100644
--- a/web/lib/fastapi_client/models/SemanticSimilaritySignal.ts
+++ b/web/lib/fastapi_client/models/SemanticSimilaritySignal.ts
@@ -14,7 +14,7 @@ export type SemanticSimilaritySignal = {
     /**
      * The name of the pre-computed embedding.
      */
-    embedding: 'cohere' | 'sbert' | 'openai' | 'gte-tiny' | 'gte-small' | 'gte-base' | 'jina-v2-small' | 'jina-v2-base' | 'bge-m3';
+    embedding: 'cohere' | 'sbert' | 'openai' | 'gte-tiny' | 'gte-small' | 'gte-base' | 'jina-v2-small' | 'jina-v2-base' | 'bge-m3' | 'nomic-embed-1.5-768' | 'nomic-embed-1.5-256';
     query: string;
     /**
      * The input type of the query, used for the query embedding.