diff --git a/lightrag/llm/hf.py b/lightrag/llm/hf.py
index fb5208b0..954a99b7 100644
--- a/lightrag/llm/hf.py
+++ b/lightrag/llm/hf.py
@@ -138,16 +138,31 @@ async def hf_model_complete(
 
 
 async def hf_embed(texts: list[str], tokenizer, embed_model) -> np.ndarray:
-    device = next(embed_model.parameters()).device
+    # Detect the appropriate device
+    if torch.cuda.is_available():
+        device = next(embed_model.parameters()).device  # Use CUDA if available
+    elif torch.backends.mps.is_available():
+        device = torch.device("mps")  # Use MPS for Apple Silicon
+    else:
+        device = torch.device("cpu")  # Fallback to CPU
+
+    # Move the model to the detected device
+    embed_model = embed_model.to(device)
+
+    # Tokenize the input texts and move them to the same device
     encoded_texts = tokenizer(
         texts, return_tensors="pt", padding=True, truncation=True
     ).to(device)
+
+    # Perform inference
     with torch.no_grad():
         outputs = embed_model(
             input_ids=encoded_texts["input_ids"],
             attention_mask=encoded_texts["attention_mask"],
         )
         embeddings = outputs.last_hidden_state.mean(dim=1)
+
+    # Convert embeddings to NumPy
     if embeddings.dtype == torch.bfloat16:
         return embeddings.detach().to(torch.float32).cpu().numpy()
     else: