update web UI

src/api_demo.py

@@ -5,11 +5,14 @@
 
 import uvicorn
 
+from glmtuner import ChatModel
 from glmtuner.api.app import create_app
+from glmtuner.tuner import get_infer_args
 
 
 def main():
-    app = create_app()
+    chat_model = ChatModel(*get_infer_args())
+    app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)
 
 

src/glmtuner/api/app.py

@@ -30,9 +30,7 @@ async def lifespan(app: FastAPI): # collects GPU memory
     torch_gc()
 
 
-def create_app():
-    chat_model = ChatModel(*get_infer_args())
-
+def create_app(chat_model: ChatModel) -> FastAPI:
     app = FastAPI(lifespan=lifespan)
 
     app.add_middleware(
@@ -124,5 +122,6 @@ async def predict(query: str, history: List[Tuple[str, str]], prefix: str, reque
 
 
 if __name__ == "__main__":
-    app = create_app()
+    chat_model = ChatModel(*get_infer_args())
+    app = create_app(chat_model)
     uvicorn.run(app, host="0.0.0.0", port=8000, workers=1)

src/glmtuner/webui/chat.py

@@ -70,14 +70,15 @@ def predict(
         chatbot: List[Tuple[str, str]],
         query: str,
         history: List[Tuple[str, str]],
+        prefix: str,
         max_length: int,
         top_p: float,
         temperature: float
     ):
         chatbot.append([query, ""])
         response = ""
         for new_text in self.stream_chat(
-            query, history, max_length=max_length, top_p=top_p, temperature=temperature
+            query, history, prefix, max_length=max_length, top_p=top_p, temperature=temperature
         ):
             response += new_text
             new_history = history + [(query, response)]

src/glmtuner/webui/components/chatbot.py

@@ -16,11 +16,9 @@ def create_chat_box(
 
         with gr.Row():
             with gr.Column(scale=4):
-                with gr.Column(scale=12):
-                    query = gr.Textbox(show_label=False, lines=8)
-
-                with gr.Column(min_width=32, scale=1):
-                    submit_btn = gr.Button(variant="primary")
+                prefix = gr.Dropdown(show_label=False)
+                query = gr.Textbox(show_label=False, lines=8)
+                submit_btn = gr.Button(variant="primary")
 
             with gr.Column(scale=1):
                 clear_btn = gr.Button()
@@ -36,7 +34,7 @@ def create_chat_box(
 
     submit_btn.click(
         chat_model.predict,
-        [chatbot, query, history, max_length, top_p, temperature],
+        [chatbot, query, history, prefix, max_length, top_p, temperature],
         [chatbot, history],
         show_progress=True
     ).then(
@@ -46,6 +44,7 @@ def create_chat_box(
     clear_btn.click(lambda: ([], []), outputs=[chatbot, history], show_progress=True)
 
     return chat_box, chatbot, history, dict(
+        prefix=prefix,
         query=query,
         submit_btn=submit_btn,
         clear_btn=clear_btn,

src/glmtuner/webui/components/eval.py

@@ -31,7 +31,8 @@ def create_eval_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str
         start_btn = gr.Button()
         stop_btn = gr.Button()
 
-    output_box = gr.Markdown()
+    with gr.Box():
+        output_box = gr.Markdown()
 
     start_btn.click(
         runner.run_eval,

src/glmtuner/webui/components/infer.py

@@ -12,7 +12,7 @@ def create_infer_tab(top_elems: Dict[str, Component]) -> Dict[str, Component]:
         load_btn = gr.Button()
         unload_btn = gr.Button()
 
-    info_box = gr.Markdown()
+    info_box = gr.Textbox(show_label=False, interactive=False)
 
     chat_model = WebChatModel()
     chat_box, chatbot, history, chat_elems = create_chat_box(chat_model)

src/glmtuner/webui/components/sft.py

@@ -35,12 +35,21 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
         lr_scheduler_type = gr.Dropdown(
             value="cosine", choices=[scheduler.value for scheduler in SchedulerType]
         )
+        max_grad_norm = gr.Textbox(value="1.0")
         dev_ratio = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
-        fp16 = gr.Checkbox(value=True)
 
-    with gr.Row():
-        logging_steps = gr.Slider(value=5, minimum=5, maximum=1000, step=5)
-        save_steps = gr.Slider(value=100, minimum=10, maximum=5000, step=10)
+    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
+        with gr.Row():
+            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)
+            compute_type = gr.Radio(choices=["fp16", "bf16"], value="fp16")
+
+    with gr.Accordion(label="LoRA config", open=False) as lora_tab:
+        with gr.Row():
+            lora_rank = gr.Slider(value=8, minimum=1, maximum=1024, step=1, scale=1)
+            lora_dropout = gr.Slider(value=0, minimum=0, maximum=1, step=0.01, scale=1)
+            lora_target = gr.Textbox(scale=2)
 
     with gr.Row():
         start_btn = gr.Button()
@@ -49,7 +58,9 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
     with gr.Row():
         with gr.Column(scale=4):
             output_dir = gr.Textbox(interactive=True)
-            output_box = gr.Markdown()
+
+            with gr.Box():
+                output_box = gr.Markdown()
 
         with gr.Column(scale=1):
             loss_viewer = gr.Plot()
@@ -73,10 +84,15 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
             batch_size,
             gradient_accumulation_steps,
             lr_scheduler_type,
+            max_grad_norm,
             dev_ratio,
-            fp16,
             logging_steps,
             save_steps,
+            warmup_steps,
+            compute_type,
+            lora_rank,
+            lora_dropout,
+            lora_target,
             output_dir
         ],
         [output_box]
@@ -102,10 +118,17 @@ def create_sft_tab(top_elems: Dict[str, Component], runner: Runner) -> Dict[str,
         batch_size=batch_size,
         gradient_accumulation_steps=gradient_accumulation_steps,
         lr_scheduler_type=lr_scheduler_type,
+        max_grad_norm=max_grad_norm,
         dev_ratio=dev_ratio,
-        fp16=fp16,
+        advanced_tab=advanced_tab,
         logging_steps=logging_steps,
         save_steps=save_steps,
+        warmup_steps=warmup_steps,
+        compute_type=compute_type,
+        lora_tab=lora_tab,
+        lora_rank=lora_rank,
+        lora_dropout=lora_dropout,
+        lora_target=lora_target,
         start_btn=start_btn,
         stop_btn=stop_btn,
         output_dir=output_dir,

src/glmtuner/webui/components/top.py

@@ -21,9 +21,10 @@ def create_top() -> Dict[str, Component]:
         checkpoints = gr.Dropdown(multiselect=True, scale=5)
         refresh_btn = gr.Button(scale=1)
 
-    with gr.Row():
-        quantization_bit = gr.Dropdown([8, 4], scale=1)
-        source_prefix = gr.Textbox(scale=6)
+    with gr.Accordion(label="Advanced config", open=False) as advanced_tab:
+        with gr.Row():
+            quantization_bit = gr.Dropdown([8, 4], scale=1)
+            source_prefix = gr.Textbox(scale=4)
 
     model_name.change(
         get_model_path, [model_name], [model_path]
@@ -47,6 +48,7 @@ def create_top() -> Dict[str, Component]:
         finetuning_type=finetuning_type,
         checkpoints=checkpoints,
         refresh_btn=refresh_btn,
+        advanced_tab=advanced_tab,
         quantization_bit=quantization_bit,
         source_prefix=source_prefix
     )

src/glmtuner/webui/interface.py

@@ -27,7 +27,7 @@ def create_ui() -> gr.Blocks:
         with gr.Tab("Evaluate"):
             eval_elems = create_eval_tab(top_elems, runner)
 
-        with gr.Tab("Inference"):
+        with gr.Tab("Chat"):
             infer_elems = create_infer_tab(top_elems)
 
         elem_list = [top_elems, sft_elems, eval_elems, infer_elems]

src/glmtuner/webui/locales.py

@@ -49,6 +49,14 @@
             "value": "刷新断点"
         }
     },
+    "advanced_tab": {
+        "en": {
+            "label": "Advanced configurations"
+        },
+        "zh": {
+            "label": "高级设置"
+        }
+    },
     "quantization_bit": {
         "en": {
             "label": "Quantization bit (optional)",
@@ -71,12 +79,12 @@
     },
     "source_prefix": {
         "en": {
-            "label": "Source prefix (optional)",
-            "info": "A sequence used as the prefix of each samples."
+            "label": "System prompt (optional)",
+            "info": "A sequence used as the default system prompt."
         },
         "zh": {
-            "label": "前缀序列（非必填）",
-            "info": "作为每个输入样本前缀的序列"
+            "label": "系统提示词（非必填）",
+            "info": "默认使用的系统提示词"
         }
     },
     "dataset_dir": {
@@ -209,30 +217,30 @@
             "info": "采用的学习率调节器名称。"
         }
     },
-    "dev_ratio": {
+    "max_grad_norm": {
         "en": {
-            "label": "Dev ratio",
-            "info": "Proportion of data in the dev set."
+            "label": "Maximum gradient norm",
+            "info": "Norm for gradient clipping.."
         },
         "zh": {
-            "label": "验证集比例",
-            "info": "验证集占全部样本的百分比。"
+            "label": "最大梯度范数",
+            "info": "用于梯度裁剪的范数。"
         }
     },
-    "fp16": {
+    "dev_ratio": {
         "en": {
-            "label": "fp16",
-            "info": "Whether to use fp16 mixed precision training."
+            "label": "Dev ratio",
+            "info": "Proportion of data in the dev set."
         },
         "zh": {
-            "label": "fp16",
-            "info": "是否启用 FP16 混合精度训练。"
+            "label": "验证集比例",
+            "info": "验证集占全部样本的百分比。"
         }
     },
     "logging_steps": {
         "en": {
             "label": "Logging steps",
-            "info": "Number of update steps between two logs."
+            "info": "Number of steps between two logs."
         },
         "zh": {
             "label": "日志间隔",
@@ -242,13 +250,71 @@
     "save_steps": {
         "en": {
             "label": "Save steps",
-            "info": "Number of updates steps between two checkpoints."
+            "info": "Number of steps between two checkpoints."
         },
         "zh": {
             "label": "保存间隔",
             "info": "每两次断点保存间的更新步数。"
         }
     },
+    "warmup_steps": {
+        "en": {
+            "label": "Warmup steps",
+            "info": "Number of steps used for warmup."
+        },
+        "zh": {
+            "label": "预热步数",
+            "info": "学习率预热采用的步数。"
+        }
+    },
+    "compute_type": {
+        "en": {
+            "label": "Compute type",
+            "info": "Whether to use fp16 or bf16 mixed precision training."
+        },
+        "zh": {
+            "label": "计算类型",
+            "info": "是否启用 FP16 或 BF16 混合精度训练。"
+        }
+    },
+    "lora_tab": {
+        "en": {
+            "label": "LoRA configurations"
+        },
+        "zh": {
+            "label": "LoRA 参数设置"
+        }
+    },
+    "lora_rank": {
+        "en": {
+            "label": "LoRA rank",
+            "info": "The rank of LoRA matrices."
+        },
+        "zh": {
+            "label": "LoRA 秩",
+            "info": "LoRA 矩阵的秩。"
+        }
+    },
+    "lora_dropout": {
+        "en": {
+            "label": "LoRA Dropout",
+            "info": "Dropout ratio of LoRA weights."
+        },
+        "zh": {
+            "label": "LoRA 随机丢弃",
+            "info": "LoRA 权重随机丢弃的概率。"
+        }
+    },
+    "lora_target": {
+        "en": {
+            "label": "LoRA modules (optional)",
+            "info": "The name(s) of target modules to apply LoRA. Use commas to separate multiple modules."
+        },
+        "zh": {
+            "label": "LoRA 作用层（非必填）",
+            "info": "应用 LoRA 的线性层名称。使用英文逗号分隔多个名称。"
+        }
+    },
     "start_btn": {
         "en": {
             "value": "Start"
@@ -323,6 +389,14 @@
             "value": "模型未加载，请先加载模型。"
         }
     },
+    "prefix": {
+        "en": {
+            "placeholder": "System prompt (optional)"
+        },
+        "zh": {
+            "placeholder": "系统提示词（非必填）"
+        }
+    },
     "query": {
         "en": {
             "placeholder": "Input..."