😅. Fix 🐛

src/peft/tuners/lora.py

@@ -404,7 +404,7 @@ class LoraLayer:
             nn.init.zeros_(self.lora_B[adapter_name].weight)
 
 
-class Linear(nn.Linear):
+class Linear(nn.Linear, LoraLayer):
     # Lora implemented in a dense layer
     def __init__(
         self,

src/peft/utils/save_and_load.py

@@ -53,7 +53,7 @@ def get_peft_model_state_dict(model, state_dict=None, adapter_name="default"):
     elif isinstance(config, PromptLearningConfig):
         to_return = {}
         if config.inference_mode:
-            prompt_embeddings = model.prompt_encoder.embedding.weight
+            prompt_embeddings = model.prompt_encoder[adapter_name].embedding.weight
         else:
             prompt_embeddings = model.get_prompt_embedding_to_save(adapter_name)
         to_return["prompt_embeddings"] = prompt_embeddings