diff --git a/modules/LoRA.py b/modules/LoRA.py
index 9d8a9cda..c0c8ae08 100644
--- a/modules/LoRA.py
+++ b/modules/LoRA.py
@@ -11,7 +11,7 @@ from modules.models import reload_model
 def add_lora_to_model(lora_names):
     if 'GPTQForCausalLM' in shared.model.__class__.__name__:
         add_lora_autogptq(lora_names)
-    elif shared.model.__class__.__name__ == 'ExllamaModel':
+    elif shared.model.__class__.__name__ in ['ExllamaModel', 'ExllamaHF']:
         add_lora_exllama(lora_names)
     else:
         add_lora_transformers(lora_names)
@@ -29,7 +29,11 @@ def add_lora_exllama(lora_names):
             return
 
     if len(lora_names) == 0:
-        shared.model.generator.lora = None
+        if shared.model.__class__.__name__ == 'ExllamaModel':
+            shared.model.generator.lora = None
+        else:
+            shared.model.lora = None
+
         shared.lora_names = []
         return
     else:
@@ -41,8 +45,13 @@ def add_lora_exllama(lora_names):
         lora_adapter_path = lora_path / "adapter_model.bin"
 
         logger.info("Applying the following LoRAs to {}: {}".format(shared.model_name, ', '.join([lora_names[0]])))
-        lora = ExLlamaLora(shared.model.model, str(lora_config_path), str(lora_adapter_path))
-        shared.model.generator.lora = lora
+        if shared.model.__class__.__name__ == 'ExllamaModel':
+            lora = ExLlamaLora(shared.model.model, str(lora_config_path), str(lora_adapter_path))
+            shared.model.generator.lora = lora
+        else:
+            lora = ExLlamaLora(shared.model.ex_model, str(lora_config_path), str(lora_adapter_path))
+            shared.model.lora = lora
+
         shared.lora_names = [lora_names[0]]
         return
 
diff --git a/modules/exllama_hf.py b/modules/exllama_hf.py
index 6db620ba..9beb2269 100644
--- a/modules/exllama_hf.py
+++ b/modules/exllama_hf.py
@@ -30,6 +30,7 @@ class ExllamaHF(PreTrainedModel):
         self.ex_config = config
         self.ex_model = ExLlama(self.ex_config)
         self.generation_config = GenerationConfig()
+        self.lora = None
 
     def _validate_model_class(self):
         pass
@@ -53,9 +54,9 @@ class ExllamaHF(PreTrainedModel):
         cache = kwargs['past_key_values'] if 'past_key_values' in kwargs else None
         if cache is None:
             cache = ExLlamaCache(self.ex_model)
-            self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True)
+            self.ex_model.forward(torch.tensor([seq[:-1]], dtype=torch.long), cache, preprocess_only=True, lora=self.lora)
 
-        logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache).to(kwargs['input_ids'].device)
+        logits = self.ex_model.forward(torch.tensor([seq[-1:]], dtype=torch.long), cache, lora=self.lora).to(kwargs['input_ids'].device)
 
         loss = None
         if labels is not None:
diff --git a/server.py b/server.py
index 7a9cbf46..ddd7e8c0 100644
--- a/server.py
+++ b/server.py
@@ -245,7 +245,7 @@ def create_model_menus():
                         shared.gradio['trust_remote_code'] = gr.Checkbox(label="trust-remote-code", value=shared.args.trust_remote_code, info='Make sure to inspect the .py files inside the model folder before loading it with this option enabled.')
                         shared.gradio['gptq_for_llama_info'] = gr.Markdown('GPTQ-for-LLaMa is currently 2x faster than AutoGPTQ on some systems. It is installed by default with the one-click installers. Otherwise, it has to be installed manually following the instructions here: [instructions](https://github.com/oobabooga/text-generation-webui/blob/main/docs/GPTQ-models-(4-bit-mode).md#installation-1).')
                         shared.gradio['exllama_info'] = gr.Markdown('For more information, consult the [docs](https://github.com/oobabooga/text-generation-webui/blob/main/docs/ExLlama.md).')
-                        shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama and doesn\'t support LoRA.')
+                        shared.gradio['exllama_HF_info'] = gr.Markdown('ExLlama_HF is a wrapper that lets you use ExLlama like a Transformers model, which means it can use the Transformers samplers. It\'s a bit slower than the regular ExLlama.')
 
         with gr.Column():
             with gr.Row():