diff --git a/langchain/llms/huggingface_pipeline.py b/langchain/llms/huggingface_pipeline.py
index 22bd3661a5e..ee5678c6fc6 100644
--- a/langchain/llms/huggingface_pipeline.py
+++ b/langchain/llms/huggingface_pipeline.py
@@ -1,4 +1,6 @@
 """Wrapper around HuggingFace Pipeline APIs."""
+import importlib.util
+import logging
 from typing import Any, List, Mapping, Optional
 
 from pydantic import BaseModel, Extra
@@ -10,6 +12,8 @@ DEFAULT_MODEL_ID = "gpt2"
 DEFAULT_TASK = "text-generation"
 VALID_TASKS = ("text2text-generation", "text-generation")
 
+logger = logging.getLogger()
+
 
 class HuggingFacePipeline(LLM, BaseModel):
     """Wrapper around HuggingFace Pipeline API.
@@ -56,6 +60,7 @@ class HuggingFacePipeline(LLM, BaseModel):
         cls,
         model_id: str,
         task: str,
+        device: int = -1,
         model_kwargs: Optional[dict] = None,
         **kwargs: Any,
     ) -> LLM:
@@ -68,8 +73,16 @@ class HuggingFacePipeline(LLM, BaseModel):
             )
             from transformers import pipeline as hf_pipeline
 
-            _model_kwargs = model_kwargs or {}
-            tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
+        except ImportError:
+            raise ValueError(
+                "Could not import transformers python package. "
+                "Please it install it with `pip install transformers`."
+            )
+
+        _model_kwargs = model_kwargs or {}
+        tokenizer = AutoTokenizer.from_pretrained(model_id, **_model_kwargs)
+
+        try:
             if task == "text-generation":
                 model = AutoModelForCausalLM.from_pretrained(model_id, **_model_kwargs)
             elif task == "text2text-generation":
@@ -79,25 +92,47 @@ class HuggingFacePipeline(LLM, BaseModel):
                     f"Got invalid task {task}, "
                     f"currently only {VALID_TASKS} are supported"
                 )
-            pipeline = hf_pipeline(
-                task=task, model=model, tokenizer=tokenizer, model_kwargs=_model_kwargs
-            )
-            if pipeline.task not in VALID_TASKS:
-                raise ValueError(
-                    f"Got invalid task {pipeline.task}, "
-                    f"currently only {VALID_TASKS} are supported"
-                )
-            return cls(
-                pipeline=pipeline,
-                model_id=model_id,
-                model_kwargs=_model_kwargs,
-                **kwargs,
-            )
-        except ImportError:
+        except ImportError as e:
             raise ValueError(
-                "Could not import transformers python package. "
-                "Please it install it with `pip install transformers`."
+                f"Could not load the {task} model due to missing dependencies."
+            ) from e
+
+        if importlib.util.find_spec("torch") is not None:
+            import torch
+
+            cuda_device_count = torch.cuda.device_count()
+            if device < -1 or (device >= cuda_device_count):
+                raise ValueError(
+                    f"Got device=={device}, "
+                    f"device is required to be within [-1, {cuda_device_count})"
+                )
+            if device < 0 and cuda_device_count > 0:
+                logger.warning(
+                    "Device has %d GPUs available. "
+                    "Provide device={deviceId} to `from_model_id` to use available"
+                    "GPUs for execution. deviceId is -1 (default) for CPU and "
+                    "can be a positive integer associated with CUDA device id.",
+                    cuda_device_count,
+                )
+
+        pipeline = hf_pipeline(
+            task=task,
+            model=model,
+            tokenizer=tokenizer,
+            device=device,
+            model_kwargs=_model_kwargs,
+        )
+        if pipeline.task not in VALID_TASKS:
+            raise ValueError(
+                f"Got invalid task {pipeline.task}, "
+                f"currently only {VALID_TASKS} are supported"
             )
+        return cls(
+            pipeline=pipeline,
+            model_id=model_id,
+            model_kwargs=_model_kwargs,
+            **kwargs,
+        )
 
     @property
     def _identifying_params(self) -> Mapping[str, Any]: