fix webui

src/llmtuner/webui/components/train.py

@@ -65,16 +65,16 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
             save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
             warmup_steps = gr.Slider(value=0, minimum=0, maximum=5000, step=1)
-            neft_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1)
+            neftune_alpha = gr.Slider(value=0, minimum=0, maximum=10, step=0.1)
 
             with gr.Column():
                 train_on_prompt = gr.Checkbox(value=False)
                 upcast_layernorm = gr.Checkbox(value=False)
 
-    input_elems.update({logging_steps, save_steps, warmup_steps, neft_alpha, train_on_prompt, upcast_layernorm})
+    input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, train_on_prompt, upcast_layernorm})
     elem_dict.update(dict(
         advanced_tab=advanced_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
-        neft_alpha=neft_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm
+        neftune_alpha=neftune_alpha, train_on_prompt=train_on_prompt, upcast_layernorm=upcast_layernorm
     ))
 
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:

src/llmtuner/webui/locales.py

@@ -333,7 +333,7 @@ LOCALES = {
             "info": "学习率预热采用的步数。"
         }
     },
-    "neft_alpha": {
+    "neftune_alpha": {
         "en": {
             "label": "NEFTune Alpha",
             "info": "Magnitude of noise adding to embedding vectors."

src/llmtuner/webui/runner.py

@@ -119,7 +119,7 @@ class Runner:
             logging_steps=get("train.logging_steps"),
             save_steps=get("train.save_steps"),
             warmup_steps=get("train.warmup_steps"),
-            neft_alpha=get("train.neft_alpha"),
+            neftune_noise_alpha=get("train.neftune_alpha"),
             train_on_prompt=get("train.train_on_prompt"),
             upcast_layernorm=get("train.upcast_layernorm"),
             lora_rank=get("train.lora_rank"),