diff --git a/langchain/embeddings/huggingface.py b/langchain/embeddings/huggingface.py
index 217b6a84..b0bd03e9 100644
--- a/langchain/embeddings/huggingface.py
+++ b/langchain/embeddings/huggingface.py
@@ -1,7 +1,7 @@
 """Wrapper around HuggingFace embedding models."""
-from typing import Any, List, Optional
+from typing import Any, Dict, List, Optional
 
-from pydantic import BaseModel, Extra
+from pydantic import BaseModel, Extra, Field
 
 from langchain.embeddings.base import Embeddings
 
@@ -22,8 +22,10 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain.embeddings import HuggingFaceEmbeddings
+
             model_name = "sentence-transformers/all-mpnet-base-v2"
-            hf = HuggingFaceEmbeddings(model_name=model_name)
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceEmbeddings(model_name=model_name, model_kwargs=model_kwargs)
     """
 
     client: Any  #: :meta private:
@@ -32,6 +34,8 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
     cache_folder: Optional[str] = None
     """Path to store models. 
     Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Key word arguments to pass to the model."""
 
     def __init__(self, **kwargs: Any):
         """Initialize the sentence_transformer."""
@@ -40,7 +44,7 @@ class HuggingFaceEmbeddings(BaseModel, Embeddings):
             import sentence_transformers
 
             self.client = sentence_transformers.SentenceTransformer(
-                self.model_name, self.cache_folder
+                self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
             )
         except ImportError:
             raise ValueError(
@@ -90,13 +94,22 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
         .. code-block:: python
 
             from langchain.embeddings import HuggingFaceInstructEmbeddings
+
             model_name = "hkunlp/instructor-large"
-            hf = HuggingFaceInstructEmbeddings(model_name=model_name)
+            model_kwargs = {'device': 'cpu'}
+            hf = HuggingFaceInstructEmbeddings(
+                model_name=model_name, model_kwargs=model_kwargs
+            )
     """
 
     client: Any  #: :meta private:
     model_name: str = DEFAULT_INSTRUCT_MODEL
     """Model name to use."""
+    cache_folder: Optional[str] = None
+    """Path to store models. 
+    Can be also set by SENTENCE_TRANSFORMERS_HOME enviroment variable."""
+    model_kwargs: Dict[str, Any] = Field(default_factory=dict)
+    """Key word arguments to pass to the model."""
     embed_instruction: str = DEFAULT_EMBED_INSTRUCTION
     """Instruction to use for embedding documents."""
     query_instruction: str = DEFAULT_QUERY_INSTRUCTION
@@ -108,7 +121,9 @@ class HuggingFaceInstructEmbeddings(BaseModel, Embeddings):
         try:
             from InstructorEmbedding import INSTRUCTOR
 
-            self.client = INSTRUCTOR(self.model_name)
+            self.client = INSTRUCTOR(
+                self.model_name, cache_folder=self.cache_folder, **self.model_kwargs
+            )
         except ImportError as e:
             raise ValueError("Dependencies for InstructorEmbedding not found.") from e