update ppo and demo in webui

Former-commit-id: 7537dd434f4c0f0bde06bd8c2ac69bf622772316

src/llmtuner/extras/constants.py

@@ -120,10 +120,12 @@ register_model_group(
 
 register_model_group(
     models={
-        "ChineseLLaMA2-7B": "ziqingyang/chinese-llama-2-7b",
+        "ChineseLLaMA2-1.3B": "hfl/chinese-llama-2-1.3b",
-        "ChineseLLaMA2-13B": "ziqingyang/chinese-llama-2-13b",
+        "ChineseLLaMA2-7B": "hfl/chinese-llama-2-7b",
-        "ChineseLLaMA2-7B-Chat": "ziqingyang/chinese-alpaca-2-7b",
+        "ChineseLLaMA2-13B": "hfl/chinese-llama-2-13b",
-        "ChineseLLaMA2-13B-Chat": "ziqingyang/chinese-alpaca-2-13b"
+        "ChineseLLaMA2-1.3B-Chat": "hfl/chinese-alpaca-2-1.3b",
+        "ChineseLLaMA2-7B-Chat": "hfl/chinese-alpaca-2-7b",
+        "ChineseLLaMA2-13B-Chat": "hfl/chinese-alpaca-2-13b"
     },
     template="llama2_zh"
 )

src/llmtuner/webui/chatter.py

@@ -25,9 +25,13 @@ class WebChatModel(ChatModel):
         self.model = None
         self.tokenizer = None
         self.generating_args = GeneratingArguments()
-        if not lazy_init:
+
+        if not lazy_init: # read arguments from command line
             super().__init__()
 
+        if demo_mode: # load openchat 3.5 by default
+            super().__init__(dict(model_name_or_path="openchat/openchat_3.5", template="openchat"))
+
     @property
     def loaded(self) -> bool:
         return self.model is not None
@@ -75,6 +79,11 @@ class WebChatModel(ChatModel):
 
     def unload_model(self, data: Dict[Component, Any]) -> Generator[str, None, None]:
         lang = data[self.manager.get_elem_by_name("top.lang")]
+
+        if self.demo_mode:
+            yield ALERTS["err_demo"][lang]
+            return
+
         yield ALERTS["info_unloading"][lang]
         self.model = None
         self.tokenizer = None

src/llmtuner/webui/interface.py

@@ -38,7 +38,7 @@ def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
         with gr.Tab("Train"):
             engine.manager.all_elems["train"] = create_train_tab(engine)
 
-        with gr.Tab("Evaluate"):
+        with gr.Tab("Evaluate & Predict"):
             engine.manager.all_elems["eval"] = create_eval_tab(engine)
 
         with gr.Tab("Chat"):

src/llmtuner/webui/runner.py

@@ -31,7 +31,6 @@ class Runner:
         self.thread: "Thread" = None
         self.do_train = True
         self.running_data: Dict["Component", Any] = None
-        self.monitor_inputs: Dict[str, str] = None
         """ State """
         self.aborted = False
         self.running = False
@@ -75,6 +74,7 @@ class Runner:
 
     def _finalize(self, lang: str, finish_info: str) -> str:
         self.thread = None
+        self.running_data = None
         self.running = False
         torch_gc()
         if self.aborted:
@@ -87,9 +87,9 @@ class Runner:
         user_config = load_config()
 
         if get("top.checkpoints"):
-            checkpoint_dir = ",".join([
+            checkpoint_dir = ",".join([get_save_dir(
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints")
+                get("top.model_name"), get("top.finetuning_type"), ckpt
-            ])
+            ) for ckpt in get("top.checkpoints")])
         else:
             checkpoint_dir = None
 
@@ -139,7 +139,10 @@ class Runner:
             args["upcast_layernorm"] = True
 
         if args["stage"] == "ppo":
-            args["reward_model"] = get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.reward_model"))
+            args["reward_model"] = get_save_dir(
+                get("top.model_name"), get("top.finetuning_type"), get("train.reward_model")
+            )
+            args["reward_model_type"] = "lora" if get("top.finetuning_type") == "lora" else "full"
 
         if args["stage"] == "dpo":
             args["dpo_beta"] = get("train.dpo_beta")
@@ -157,9 +160,9 @@ class Runner:
         user_config = load_config()
 
         if get("top.checkpoints"):
-            checkpoint_dir = ",".join([
+            checkpoint_dir = ",".join([get_save_dir(
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), ckpt) for ckpt in get("top.checkpoints")
+                get("top.model_name"), get("top.finetuning_type"), ckpt
-            ])
+            ) for ckpt in get("top.checkpoints")])
             output_dir = get_save_dir(
                 get("top.model_name"), get("top.finetuning_type"), "eval_" + "_".join(get("top.checkpoints"))
             )
@@ -216,7 +219,6 @@ class Runner:
             args = self._parse_train_args(data) if do_train else self._parse_eval_args(data)
             run_kwargs = dict(args=args, callbacks=[self.trainer_callback])
             self.do_train, self.running_data = do_train, data
-            self.monitor_inputs = dict(lang=data[self.manager.get_elem_by_name("top.lang")], output_dir=args["output_dir"])
             self.thread = Thread(target=run_exp, kwargs=run_kwargs)
             self.thread.start()
             yield from self.monitor()
@@ -235,7 +237,10 @@ class Runner:
 
     def monitor(self) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
         self.running = True
-        lang, output_dir = self.monitor_inputs["lang"], self.monitor_inputs["output_dir"]
+        lang = self.running_data[self.manager.get_elem_by_name("top.lang")]
+        output_dir = self.running_data[self.manager.get_elem_by_name(
+            "{}.output_dir".format("train" if self.do_train else "eval")
+        )]
         while self.thread.is_alive():
             time.sleep(2)
             if self.aborted: