format style

Former-commit-id: 53b683531b83cd1d19de97c6565f16c1eca6f5e1

src/llmtuner/webui/chatter.py

@@ -1,24 +1,22 @@
-import gradio as gr
-from gradio.components import Component # cannot use TYPE_CHECKING here
 from typing import TYPE_CHECKING, Any, Dict, Generator, List, Optional, Tuple
 
+import gradio as gr
+from gradio.components import Component  # cannot use TYPE_CHECKING here
+
 from ..chat import ChatModel
 from ..extras.misc import torch_gc
 from ..hparams import GeneratingArguments
 from .common import get_save_dir
 from .locales import ALERTS
 
+
 if TYPE_CHECKING:
     from .manager import Manager
 
 
 class WebChatModel(ChatModel):
-
     def __init__(
-        self,
-        manager: "Manager",
-        demo_mode: Optional[bool] = False,
-        lazy_init: Optional[bool] = True
+        self, manager: "Manager", demo_mode: Optional[bool] = False, lazy_init: Optional[bool] = True
     ) -> None:
         self.manager = manager
         self.demo_mode = demo_mode
@@ -26,11 +24,12 @@ class WebChatModel(ChatModel):
         self.tokenizer = None
         self.generating_args = GeneratingArguments()
 
-        if not lazy_init: # read arguments from command line
+        if not lazy_init:  # read arguments from command line
             super().__init__()
 
-        if demo_mode: # load demo_config.json if exists
+        if demo_mode:  # load demo_config.json if exists
             import json
+
             try:
                 with open("demo_config.json", "r", encoding="utf-8") as f:
                     args = json.load(f)
@@ -38,7 +37,7 @@ class WebChatModel(ChatModel):
                 super().__init__(args)
             except AssertionError:
                 print("Please provided model name and template in `demo_config.json`.")
-            except:
+            except Exception:
                 print("Cannot find `demo_config.json` at current directory.")
 
     @property
@@ -64,9 +63,12 @@ class WebChatModel(ChatModel):
             return
 
         if get("top.adapter_path"):
-            adapter_name_or_path = ",".join([
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
-            for adapter in get("top.adapter_path")])
+            adapter_name_or_path = ",".join(
+                [
+                    get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
+                    for adapter in get("top.adapter_path")
+                ]
+            )
         else:
             adapter_name_or_path = None
 
@@ -79,7 +81,7 @@ class WebChatModel(ChatModel):
             template=get("top.template"),
             flash_attn=(get("top.booster") == "flash_attn"),
             use_unsloth=(get("top.booster") == "unsloth"),
-            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None
+            rope_scaling=get("top.rope_scaling") if get("top.rope_scaling") in ["linear", "dynamic"] else None,
         )
         super().__init__(args)
 
@@ -108,7 +110,7 @@ class WebChatModel(ChatModel):
         tools: str,
         max_new_tokens: int,
         top_p: float,
-        temperature: float
+        temperature: float,
     ) -> Generator[Tuple[List[Tuple[str, str]], List[Tuple[str, str]]], None, None]:
         chatbot.append([query, ""])
         response = ""

src/llmtuner/webui/common.py

@@ -1,9 +1,10 @@
-import os
 import json
-import gradio as gr
+import os
 from collections import defaultdict
 from typing import Any, Dict, Optional
-from peft.utils import WEIGHTS_NAME, SAFETENSORS_WEIGHTS_NAME
+
+import gradio as gr
+from peft.utils import SAFETENSORS_WEIGHTS_NAME, WEIGHTS_NAME
 
 from ..extras.constants import (
     DATA_CONFIG,
@@ -12,7 +13,7 @@ from ..extras.constants import (
     PEFT_METHODS,
     SUPPORTED_MODELS,
     TRAINING_STAGES,
-    DownloadSource
+    DownloadSource,
 )
 from ..extras.misc import use_modelscope
 
@@ -36,7 +37,7 @@ def load_config() -> Dict[str, Any]:
     try:
         with open(get_config_path(), "r", encoding="utf-8") as f:
             return json.load(f)
-    except:
+    except Exception:
         return {"lang": None, "last_model": None, "path_dict": {}, "cache_dir": None}
 
 
@@ -59,7 +60,7 @@ def get_model_path(model_name: str) -> str:
         use_modelscope()
         and path_dict.get(DownloadSource.MODELSCOPE)
         and model_path == path_dict.get(DownloadSource.DEFAULT)
-    ): # replace path
+    ):  # replace path
         model_path = path_dict.get(DownloadSource.MODELSCOPE)
     return model_path
 
@@ -87,9 +88,8 @@ def list_adapters(model_name: str, finetuning_type: str) -> Dict[str, Any]:
         save_dir = get_save_dir(model_name, finetuning_type)
         if save_dir and os.path.isdir(save_dir):
             for adapter in os.listdir(save_dir):
-                if (
-                    os.path.isdir(os.path.join(save_dir, adapter))
-                    and any([os.path.isfile(os.path.join(save_dir, adapter, name)) for name in ADAPTER_NAMES])
+                if os.path.isdir(os.path.join(save_dir, adapter)) and any(
+                    os.path.isfile(os.path.join(save_dir, adapter, name)) for name in ADAPTER_NAMES
                 ):
                     adapters.append(adapter)
     return gr.update(value=[], choices=adapters, interactive=True)

src/llmtuner/webui/components/__init__.py

@@ -1,11 +1,16 @@
+from .chatbot import create_chat_box
+from .eval import create_eval_tab
+from .export import create_export_tab
+from .infer import create_infer_tab
 from .top import create_top
 from .train import create_train_tab
-from .eval import create_eval_tab
-from .infer import create_infer_tab
-from .export import create_export_tab
-from .chatbot import create_chat_box
 
 
 __all__ = [
-    "create_top", "create_train_tab", "create_eval_tab", "create_infer_tab", "create_export_tab", "create_chat_box"
+    "create_top",
+    "create_train_tab",
+    "create_eval_tab",
+    "create_infer_tab",
+    "create_export_tab",
+    "create_chat_box",
 ]

src/llmtuner/webui/components/chatbot.py

@@ -1,6 +1,7 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict, Optional, Tuple
 
+import gradio as gr
+
 from ..utils import check_json_schema
 
 
@@ -12,8 +13,7 @@ if TYPE_CHECKING:
 
 
 def create_chat_box(
-    engine: "Engine",
-    visible: Optional[bool] = False
+    engine: "Engine", visible: Optional[bool] = False
 ) -> Tuple["Block", "Component", "Component", Dict[str, "Component"]]:
     with gr.Box(visible=visible) as chat_box:
         chatbot = gr.Chatbot()
@@ -38,20 +38,23 @@ def create_chat_box(
         engine.chatter.predict,
         [chatbot, query, history, system, tools, max_new_tokens, top_p, temperature],
         [chatbot, history],
-        show_progress=True
-    ).then(
-        lambda: gr.update(value=""), outputs=[query]
-    )
+        show_progress=True,
+    ).then(lambda: gr.update(value=""), outputs=[query])
 
     clear_btn.click(lambda: ([], []), outputs=[chatbot, history], show_progress=True)
 
-    return chat_box, chatbot, history, dict(
-        system=system,
-        tools=tools,
-        query=query,
-        submit_btn=submit_btn,
-        clear_btn=clear_btn,
-        max_new_tokens=max_new_tokens,
-        top_p=top_p,
-        temperature=temperature
+    return (
+        chat_box,
+        chatbot,
+        history,
+        dict(
+            system=system,
+            tools=tools,
+            query=query,
+            submit_btn=submit_btn,
+            clear_btn=clear_btn,
+            max_new_tokens=max_new_tokens,
+            top_p=top_p,
+            temperature=temperature,
+        ),
     )

src/llmtuner/webui/components/data.py

@@ -1,10 +1,12 @@
-import os
 import json
-import gradio as gr
+import os
 from typing import TYPE_CHECKING, Any, Dict, Tuple
 
+import gradio as gr
+
 from ...extras.constants import DATA_CONFIG
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -24,7 +26,7 @@ def can_preview(dataset_dir: str, dataset: list) -> Dict[str, Any]:
     try:
         with open(os.path.join(dataset_dir, DATA_CONFIG), "r", encoding="utf-8") as f:
             dataset_info = json.load(f)
-    except:
+    except Exception:
         return gr.update(interactive=False)
 
     if (
@@ -48,7 +50,7 @@ def get_preview(dataset_dir: str, dataset: list, page_index: int) -> Tuple[int,
         elif data_file.endswith(".jsonl"):
             data = [json.loads(line) for line in f]
         else:
-            data = [line for line in f]
+            data = [line for line in f]  # noqa: C416
     return len(data), data[PAGE_SIZE * page_index : PAGE_SIZE * (page_index + 1)], gr.update(visible=True)
 
 
@@ -67,32 +69,17 @@ def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> Dic
         with gr.Row():
             preview_samples = gr.JSON(interactive=False)
 
-    dataset.change(
-        can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False
-    ).then(
+    dataset.change(can_preview, [dataset_dir, dataset], [data_preview_btn], queue=False).then(
         lambda: 0, outputs=[page_index], queue=False
     )
     data_preview_btn.click(
-        get_preview,
-        [dataset_dir, dataset, page_index],
-        [preview_count, preview_samples, preview_box],
-        queue=False
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
     )
-    prev_btn.click(
-        prev_page, [page_index], [page_index], queue=False
-    ).then(
-        get_preview,
-        [dataset_dir, dataset, page_index],
-        [preview_count, preview_samples, preview_box],
-        queue=False
+    prev_btn.click(prev_page, [page_index], [page_index], queue=False).then(
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
     )
-    next_btn.click(
-        next_page, [page_index, preview_count], [page_index], queue=False
-    ).then(
-        get_preview,
-        [dataset_dir, dataset, page_index],
-        [preview_count, preview_samples, preview_box],
-        queue=False
+    next_btn.click(next_page, [page_index, preview_count], [page_index], queue=False).then(
+        get_preview, [dataset_dir, dataset, page_index], [preview_count, preview_samples, preview_box], queue=False
     )
     close_btn.click(lambda: gr.update(visible=False), outputs=[preview_box], queue=False)
     return dict(
@@ -102,5 +89,5 @@ def create_preview_box(dataset_dir: "gr.Textbox", dataset: "gr.Dropdown") -> Dic
         prev_btn=prev_btn,
         next_btn=next_btn,
         close_btn=close_btn,
-        preview_samples=preview_samples
+        preview_samples=preview_samples,
     )

src/llmtuner/webui/components/eval.py

@@ -1,9 +1,11 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
-from ..common import list_dataset, DEFAULT_DATA_DIR
+import gradio as gr
+
+from ..common import DEFAULT_DATA_DIR, list_dataset
 from .data import create_preview_box
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -31,9 +33,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
         predict = gr.Checkbox(value=True)
 
     input_elems.update({cutoff_len, max_samples, batch_size, predict})
-    elem_dict.update(dict(
-        cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict
-    ))
+    elem_dict.update(dict(cutoff_len=cutoff_len, max_samples=max_samples, batch_size=batch_size, predict=predict))
 
     with gr.Row():
         max_new_tokens = gr.Slider(10, 2048, value=128, step=1)
@@ -42,9 +42,7 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
         output_dir = gr.Textbox()
 
     input_elems.update({max_new_tokens, top_p, temperature, output_dir})
-    elem_dict.update(dict(
-        max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir
-    ))
+    elem_dict.update(dict(max_new_tokens=max_new_tokens, top_p=top_p, temperature=temperature, output_dir=output_dir))
 
     with gr.Row():
         cmd_preview_btn = gr.Button()
@@ -59,10 +57,16 @@ def create_eval_tab(engine: "Engine") -> Dict[str, "Component"]:
         output_box = gr.Markdown()
 
     output_elems = [output_box, process_bar]
-    elem_dict.update(dict(
-        cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn,
-        resume_btn=resume_btn, process_bar=process_bar, output_box=output_box
-    ))
+    elem_dict.update(
+        dict(
+            cmd_preview_btn=cmd_preview_btn,
+            start_btn=start_btn,
+            stop_btn=stop_btn,
+            resume_btn=resume_btn,
+            process_bar=process_bar,
+            output_box=output_box,
+        )
+    )
 
     cmd_preview_btn.click(engine.runner.preview_eval, input_elems, output_elems)
     start_btn.click(engine.runner.run_eval, input_elems, output_elems)

src/llmtuner/webui/components/export.py

@@ -1,10 +1,12 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict, Generator, List
 
+import gradio as gr
+
 from ...train import export_model
 from ..common import get_save_dir
 from ..locales import ALERTS
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -24,7 +26,7 @@ def save_model(
     max_shard_size: int,
     export_quantization_bit: int,
     export_quantization_dataset: str,
-    export_dir: str
+    export_dir: str,
 ) -> Generator[str, None, None]:
     error = ""
     if not model_name:
@@ -44,7 +46,9 @@ def save_model(
         return
 
     if adapter_path:
-        adapter_name_or_path = ",".join([get_save_dir(model_name, finetuning_type, adapter) for adapter in adapter_path])
+        adapter_name_or_path = ",".join(
+            [get_save_dir(model_name, finetuning_type, adapter) for adapter in adapter_path]
+        )
     else:
         adapter_name_or_path = None
 
@@ -56,7 +60,7 @@ def save_model(
         export_dir=export_dir,
         export_size=max_shard_size,
         export_quantization_bit=int(export_quantization_bit) if export_quantization_bit in GPTQ_BITS else None,
-        export_quantization_dataset=export_quantization_dataset
+        export_quantization_dataset=export_quantization_dataset,
     )
 
     yield ALERTS["info_exporting"][lang]
@@ -86,9 +90,9 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
             max_shard_size,
             export_quantization_bit,
             export_quantization_dataset,
-            export_dir
+            export_dir,
         ],
-        [info_box]
+        [info_box],
     )
 
     return dict(
@@ -97,5 +101,5 @@ def create_export_tab(engine: "Engine") -> Dict[str, "Component"]:
         export_quantization_dataset=export_quantization_dataset,
         export_dir=export_dir,
         export_btn=export_btn,
-        info_box=info_box
+        info_box=info_box,
     )

src/llmtuner/webui/components/infer.py

@@ -1,8 +1,10 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
+import gradio as gr
+
 from .chatbot import create_chat_box
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -23,18 +25,12 @@ def create_infer_tab(engine: "Engine") -> Dict[str, "Component"]:
     chat_box, chatbot, history, chat_elems = create_chat_box(engine, visible=False)
     elem_dict.update(dict(chat_box=chat_box, **chat_elems))
 
-    load_btn.click(
-        engine.chatter.load_model, input_elems, [info_box]
-    ).then(
+    load_btn.click(engine.chatter.load_model, input_elems, [info_box]).then(
         lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box]
     )
 
-    unload_btn.click(
-        engine.chatter.unload_model, input_elems, [info_box]
-    ).then(
+    unload_btn.click(engine.chatter.unload_model, input_elems, [info_box]).then(
         lambda: ([], []), outputs=[chatbot, history]
-    ).then(
-        lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box]
-    )
+    ).then(lambda: gr.update(visible=engine.chatter.loaded), outputs=[chat_box])
 
     return elem_dict

src/llmtuner/webui/components/top.py

@@ -1,11 +1,13 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict
 
+import gradio as gr
+
 from ...data import templates
 from ...extras.constants import METHODS, SUPPORTED_MODELS
 from ..common import get_model_path, get_template, list_adapters, save_config
 from ..utils import can_quantize
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -30,25 +32,19 @@ def create_top() -> Dict[str, "Component"]:
             rope_scaling = gr.Radio(choices=["none", "linear", "dynamic"], value="none")
             booster = gr.Radio(choices=["none", "flash_attn", "unsloth"], value="none")
 
-    model_name.change(
-        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
-    ).then(
+    model_name.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
         get_model_path, [model_name], [model_path], queue=False
     ).then(
         get_template, [model_name], [template], queue=False
-    ) # do not save config since the below line will save
+    )  # do not save config since the below line will save
 
     model_path.change(save_config, inputs=[lang, model_name, model_path], queue=False)
 
-    finetuning_type.change(
-        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
-    ).then(
+    finetuning_type.change(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False).then(
         can_quantize, [finetuning_type], [quantization_bit], queue=False
     )
 
-    refresh_btn.click(
-        list_adapters, [model_name, finetuning_type], [adapter_path], queue=False
-    )
+    refresh_btn.click(list_adapters, [model_name, finetuning_type], [adapter_path], queue=False)
 
     return dict(
         lang=lang,
@@ -61,5 +57,5 @@ def create_top() -> Dict[str, "Component"]:
         quantization_bit=quantization_bit,
         template=template,
         rope_scaling=rope_scaling,
-        booster=booster
+        booster=booster,
     )

src/llmtuner/webui/components/train.py

@@ -1,12 +1,14 @@
-import gradio as gr
 from typing import TYPE_CHECKING, Dict
+
+import gradio as gr
 from transformers.trainer_utils import SchedulerType
 
 from ...extras.constants import TRAINING_STAGES
-from ..common import list_adapters, list_dataset, DEFAULT_DATA_DIR
+from ..common import DEFAULT_DATA_DIR, list_adapters, list_dataset
 from ..components.data import create_preview_box
 from ..utils import gen_plot
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
@@ -29,9 +31,7 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
     dataset_dir.change(list_dataset, [dataset_dir, training_stage], [dataset], queue=False)
 
     input_elems.update({training_stage, dataset_dir, dataset})
-    elem_dict.update(dict(
-        training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems
-    ))
+    elem_dict.update(dict(training_stage=training_stage, dataset_dir=dataset_dir, dataset=dataset, **preview_elems))
 
     with gr.Row():
         cutoff_len = gr.Slider(value=1024, minimum=4, maximum=8192, step=1)
@@ -41,25 +41,33 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         compute_type = gr.Radio(choices=["fp16", "bf16", "fp32"], value="fp16")
 
     input_elems.update({cutoff_len, learning_rate, num_train_epochs, max_samples, compute_type})
-    elem_dict.update(dict(
-        cutoff_len=cutoff_len, learning_rate=learning_rate, num_train_epochs=num_train_epochs,
-        max_samples=max_samples, compute_type=compute_type
-    ))
+    elem_dict.update(
+        dict(
+            cutoff_len=cutoff_len,
+            learning_rate=learning_rate,
+            num_train_epochs=num_train_epochs,
+            max_samples=max_samples,
+            compute_type=compute_type,
+        )
+    )
 
     with gr.Row():
         batch_size = gr.Slider(value=4, minimum=1, maximum=512, step=1)
         gradient_accumulation_steps = gr.Slider(value=4, minimum=1, maximum=512, step=1)
-        lr_scheduler_type = gr.Dropdown(
-            choices=[scheduler.value for scheduler in SchedulerType], value="cosine"
-        )
+        lr_scheduler_type = gr.Dropdown(choices=[scheduler.value for scheduler in SchedulerType], value="cosine")
         max_grad_norm = gr.Textbox(value="1.0")
         val_size = gr.Slider(value=0, minimum=0, maximum=1, step=0.001)
 
     input_elems.update({batch_size, gradient_accumulation_steps, lr_scheduler_type, max_grad_norm, val_size})
-    elem_dict.update(dict(
-        batch_size=batch_size, gradient_accumulation_steps=gradient_accumulation_steps,
-        lr_scheduler_type=lr_scheduler_type, max_grad_norm=max_grad_norm, val_size=val_size
-    ))
+    elem_dict.update(
+        dict(
+            batch_size=batch_size,
+            gradient_accumulation_steps=gradient_accumulation_steps,
+            lr_scheduler_type=lr_scheduler_type,
+            max_grad_norm=max_grad_norm,
+            val_size=val_size,
+        )
+    )
 
     with gr.Accordion(label="Extra config", open=False) as extra_tab:
         with gr.Row():
@@ -73,10 +81,17 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
                 upcast_layernorm = gr.Checkbox(value=False)
 
     input_elems.update({logging_steps, save_steps, warmup_steps, neftune_alpha, sft_packing, upcast_layernorm})
-    elem_dict.update(dict(
-        extra_tab=extra_tab, logging_steps=logging_steps, save_steps=save_steps, warmup_steps=warmup_steps,
-        neftune_alpha=neftune_alpha, sft_packing=sft_packing, upcast_layernorm=upcast_layernorm
-    ))
+    elem_dict.update(
+        dict(
+            extra_tab=extra_tab,
+            logging_steps=logging_steps,
+            save_steps=save_steps,
+            warmup_steps=warmup_steps,
+            neftune_alpha=neftune_alpha,
+            sft_packing=sft_packing,
+            upcast_layernorm=upcast_layernorm,
+        )
+    )
 
     with gr.Accordion(label="LoRA config", open=False) as lora_tab:
         with gr.Row():
@@ -87,10 +102,16 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
             create_new_adapter = gr.Checkbox(scale=1)
 
     input_elems.update({lora_rank, lora_dropout, lora_target, additional_target, create_new_adapter})
-    elem_dict.update(dict(
-        lora_tab=lora_tab, lora_rank=lora_rank, lora_dropout=lora_dropout, lora_target=lora_target,
-        additional_target=additional_target, create_new_adapter=create_new_adapter
-    ))
+    elem_dict.update(
+        dict(
+            lora_tab=lora_tab,
+            lora_rank=lora_rank,
+            lora_dropout=lora_dropout,
+            lora_target=lora_target,
+            additional_target=additional_target,
+            create_new_adapter=create_new_adapter,
+        )
+    )
 
     with gr.Accordion(label="RLHF config", open=False) as rlhf_tab:
         with gr.Row():
@@ -103,13 +124,13 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
         list_adapters,
         [engine.manager.get_elem_by_name("top.model_name"), engine.manager.get_elem_by_name("top.finetuning_type")],
         [reward_model],
-        queue=False
+        queue=False,
     )
 
     input_elems.update({dpo_beta, dpo_ftx, reward_model})
-    elem_dict.update(dict(
-        rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, reward_model=reward_model, refresh_btn=refresh_btn
-    ))
+    elem_dict.update(
+        dict(rlhf_tab=rlhf_tab, dpo_beta=dpo_beta, dpo_ftx=dpo_ftx, reward_model=reward_model, refresh_btn=refresh_btn)
+    )
 
     with gr.Row():
         cmd_preview_btn = gr.Button()
@@ -139,20 +160,28 @@ def create_train_tab(engine: "Engine") -> Dict[str, "Component"]:
     stop_btn.click(engine.runner.set_abort, queue=False)
     resume_btn.change(engine.runner.monitor, outputs=output_elems)
 
-    elem_dict.update(dict(
-        cmd_preview_btn=cmd_preview_btn, start_btn=start_btn, stop_btn=stop_btn, output_dir=output_dir,
-        resume_btn=resume_btn, process_bar=process_bar, output_box=output_box, loss_viewer=loss_viewer
-    ))
+    elem_dict.update(
+        dict(
+            cmd_preview_btn=cmd_preview_btn,
+            start_btn=start_btn,
+            stop_btn=stop_btn,
+            output_dir=output_dir,
+            resume_btn=resume_btn,
+            process_bar=process_bar,
+            output_box=output_box,
+            loss_viewer=loss_viewer,
+        )
+    )
 
     output_box.change(
         gen_plot,
         [
             engine.manager.get_elem_by_name("top.model_name"),
             engine.manager.get_elem_by_name("top.finetuning_type"),
-            output_dir
+            output_dir,
         ],
         loss_viewer,
-        queue=False
+        queue=False,
     )
 
     return elem_dict

src/llmtuner/webui/engine.py

@@ -1,7 +1,8 @@
-import gradio as gr
-from gradio.components import Component # cannot use TYPE_CHECKING here
 from typing import Any, Dict, Generator, Optional
 
+import gradio as gr
+from gradio.components import Component  # cannot use TYPE_CHECKING here
+
 from .chatter import WebChatModel
 from .common import get_model_path, list_dataset, load_config
 from .locales import LOCALES
@@ -11,7 +12,6 @@ from .utils import get_time
 
 
 class Engine:
-
     def __init__(self, demo_mode: Optional[bool] = False, pure_chat: Optional[bool] = False) -> None:
         self.demo_mode = demo_mode
         self.pure_chat = pure_chat
@@ -26,10 +26,7 @@ class Engine:
         user_config = load_config() if not self.demo_mode else {}
         lang = user_config.get("lang", None) or "en"
 
-        init_dict = {
-            "top.lang": {"value": lang},
-            "infer.chat_box": {"visible": self.chatter.loaded}
-        }
+        init_dict = {"top.lang": {"value": lang}, "infer.chat_box": {"visible": self.chatter.loaded}}
 
         if not self.pure_chat:
             init_dict["train.dataset"] = {"choices": list_dataset()["choices"]}
@@ -49,13 +46,17 @@ class Engine:
                 else:
                     yield self._form_dict({"eval.resume_btn": {"value": True}})
             else:
-                yield self._form_dict({
-                    "train.output_dir": {"value": "train_" + get_time()},
-                    "eval.output_dir": {"value": "eval_" + get_time()},
-                })
+                yield self._form_dict(
+                    {
+                        "train.output_dir": {"value": "train_" + get_time()},
+                        "eval.output_dir": {"value": "eval_" + get_time()},
+                    }
+                )
 
     def change_lang(self, lang: str) -> Dict[Component, Dict[str, Any]]:
         return {
             component: gr.update(**LOCALES[name][lang])
-            for elems in self.manager.all_elems.values() for name, component in elems.items() if name in LOCALES
+            for elems in self.manager.all_elems.values()
+            for name, component in elems.items()
+            if name in LOCALES
         }

src/llmtuner/webui/interface.py

@@ -1,21 +1,22 @@
-import gradio as gr
 from typing import Optional
+
+import gradio as gr
 from transformers.utils.versions import require_version
 
+from .common import save_config
 from .components import (
+    create_chat_box,
+    create_eval_tab,
+    create_export_tab,
+    create_infer_tab,
     create_top,
     create_train_tab,
-    create_eval_tab,
-    create_infer_tab,
-    create_export_tab,
-    create_chat_box
 )
-from .common import save_config
 from .css import CSS
 from .engine import Engine
 
 
-require_version("gradio>=3.38.0,<4.0.0", "To fix: pip install \"gradio>=3.38.0,<4.0.0\"")
+require_version("gradio>=3.38.0,<4.0.0", 'To fix: pip install "gradio>=3.38.0,<4.0.0"')
 
 
 def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
@@ -23,11 +24,9 @@ def create_ui(demo_mode: Optional[bool] = False) -> gr.Blocks:
 
     with gr.Blocks(title="LLaMA Board", css=CSS) as demo:
         if demo_mode:
+            gr.HTML("<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>")
             gr.HTML(
-                "<h1><center>LLaMA Board: A One-stop Web UI for Getting Started with LLaMA Factory</center></h1>"
-            )
-            gr.HTML(
-                "<h3><center>Visit <a href=\"https://github.com/hiyouga/LLaMA-Factory\" target=\"_blank\">"
+                '<h3><center>Visit <a href="https://github.com/hiyouga/LLaMA-Factory" target="_blank">'
                 "LLaMA Factory</a> for details.</center></h3>"
             )
             gr.DuplicateButton(value="Duplicate Space for private use", elem_classes="duplicate-button")

src/llmtuner/webui/locales.py

@@ -1,726 +1,220 @@
 LOCALES = {
-    "lang": {
-        "en": {
-            "label": "Lang"
-        },
-        "zh": {
-            "label": "语言"
-        }
-    },
-    "model_name": {
-        "en": {
-            "label": "Model name"
-        },
-        "zh": {
-            "label": "模型名称"
-        }
-    },
+    "lang": {"en": {"label": "Lang"}, "zh": {"label": "语言"}},
+    "model_name": {"en": {"label": "Model name"}, "zh": {"label": "模型名称"}},
     "model_path": {
-        "en": {
-            "label": "Model path",
-            "info": "Path to pretrained model or model identifier from Hugging Face."
-        },
-        "zh": {
-            "label": "模型路径",
-            "info": "本地模型的文件路径或 Hugging Face 的模型标识符。"
-        }
-    },
-    "finetuning_type": {
-        "en": {
-            "label": "Finetuning method"
-        },
-        "zh": {
-            "label": "微调方法"
-        }
-    },
-    "adapter_path": {
-        "en": {
-            "label": "Adapter path"
-        },
-        "zh": {
-            "label": "适配器路径"
-        }
-    },
-    "refresh_btn": {
-        "en": {
-            "value": "Refresh adapters"
-        },
-        "zh": {
-            "value": "刷新适配器"
-        }
-    },
-    "advanced_tab": {
-        "en": {
-            "label": "Advanced configurations"
-        },
-        "zh": {
-            "label": "高级设置"
-        }
+        "en": {"label": "Model path", "info": "Path to pretrained model or model identifier from Hugging Face."},
+        "zh": {"label": "模型路径", "info": "本地模型的文件路径或 Hugging Face 的模型标识符。"},
     },
+    "finetuning_type": {"en": {"label": "Finetuning method"}, "zh": {"label": "微调方法"}},
+    "adapter_path": {"en": {"label": "Adapter path"}, "zh": {"label": "适配器路径"}},
+    "refresh_btn": {"en": {"value": "Refresh adapters"}, "zh": {"value": "刷新适配器"}},
+    "advanced_tab": {"en": {"label": "Advanced configurations"}, "zh": {"label": "高级设置"}},
     "quantization_bit": {
-        "en": {
-            "label": "Quantization bit",
-            "info": "Enable 4/8-bit model quantization (QLoRA)."
-        },
-        "zh": {
-            "label": "量化等级",
-            "info": "启用 4/8 比特模型量化（QLoRA）。"
-        }
+        "en": {"label": "Quantization bit", "info": "Enable 4/8-bit model quantization (QLoRA)."},
+        "zh": {"label": "量化等级", "info": "启用 4/8 比特模型量化（QLoRA）。"},
     },
     "template": {
-        "en": {
-            "label": "Prompt template",
-            "info": "The template used in constructing prompts."
-        },
-        "zh": {
-            "label": "提示模板",
-            "info": "构建提示词时使用的模板"
-        }
-    },
-    "rope_scaling": {
-        "en": {
-            "label": "RoPE scaling"
-        },
-        "zh": {
-            "label": "RoPE 插值方法"
-        }
-    },
-    "booster": {
-        "en": {
-            "label": "Booster"
-        },
-        "zh": {
-            "label": "加速方式"
-        }
+        "en": {"label": "Prompt template", "info": "The template used in constructing prompts."},
+        "zh": {"label": "提示模板", "info": "构建提示词时使用的模板"},
     },
+    "rope_scaling": {"en": {"label": "RoPE scaling"}, "zh": {"label": "RoPE 插值方法"}},
+    "booster": {"en": {"label": "Booster"}, "zh": {"label": "加速方式"}},
     "training_stage": {
-        "en": {
-            "label": "Stage",
-            "info": "The stage to perform in training."
-        },
-        "zh": {
-            "label": "训练阶段",
-            "info": "目前采用的训练方式。"
-        }
+        "en": {"label": "Stage", "info": "The stage to perform in training."},
+        "zh": {"label": "训练阶段", "info": "目前采用的训练方式。"},
     },
     "dataset_dir": {
-        "en": {
-            "label": "Data dir",
-            "info": "Path to the data directory."
-        },
-        "zh": {
-            "label": "数据路径",
-            "info": "数据文件夹的路径。"
-        }
-    },
-    "dataset": {
-        "en": {
-            "label": "Dataset"
-        },
-        "zh": {
-            "label": "数据集"
-        }
-    },
-    "data_preview_btn": {
-        "en": {
-            "value": "Preview dataset"
-        },
-        "zh": {
-            "value": "预览数据集"
-        }
-    },
-    "preview_count": {
-        "en": {
-            "label": "Count"
-        },
-        "zh": {
-            "label": "数量"
-        }
-    },
-    "page_index": {
-        "en": {
-            "label": "Page"
-        },
-        "zh": {
-            "label": "页数"
-        }
-    },
-    "prev_btn": {
-        "en": {
-            "value": "Prev"
-        },
-        "zh": {
-            "value": "上一页"
-        }
-    },
-    "next_btn": {
-        "en": {
-            "value": "Next"
-        },
-        "zh": {
-            "value": "下一页"
-        }
-    },
-    "close_btn": {
-        "en": {
-            "value": "Close"
-        },
-        "zh": {
-            "value": "关闭"
-        }
-    },
-    "preview_samples": {
-        "en": {
-            "label": "Samples"
-        },
-        "zh": {
-            "label": "样例"
-        }
+        "en": {"label": "Data dir", "info": "Path to the data directory."},
+        "zh": {"label": "数据路径", "info": "数据文件夹的路径。"},
     },
+    "dataset": {"en": {"label": "Dataset"}, "zh": {"label": "数据集"}},
+    "data_preview_btn": {"en": {"value": "Preview dataset"}, "zh": {"value": "预览数据集"}},
+    "preview_count": {"en": {"label": "Count"}, "zh": {"label": "数量"}},
+    "page_index": {"en": {"label": "Page"}, "zh": {"label": "页数"}},
+    "prev_btn": {"en": {"value": "Prev"}, "zh": {"value": "上一页"}},
+    "next_btn": {"en": {"value": "Next"}, "zh": {"value": "下一页"}},
+    "close_btn": {"en": {"value": "Close"}, "zh": {"value": "关闭"}},
+    "preview_samples": {"en": {"label": "Samples"}, "zh": {"label": "样例"}},
     "cutoff_len": {
-        "en": {
-            "label": "Cutoff length",
-            "info": "Max tokens in input sequence."
-        },
-        "zh": {
-            "label": "截断长度",
-            "info": "输入序列分词后的最大长度。"
-        }
+        "en": {"label": "Cutoff length", "info": "Max tokens in input sequence."},
+        "zh": {"label": "截断长度", "info": "输入序列分词后的最大长度。"},
     },
     "learning_rate": {
-        "en": {
-            "label": "Learning rate",
-            "info": "Initial learning rate for AdamW."
-        },
-        "zh": {
-            "label": "学习率",
-            "info": "AdamW 优化器的初始学习率。"
-        }
+        "en": {"label": "Learning rate", "info": "Initial learning rate for AdamW."},
+        "zh": {"label": "学习率", "info": "AdamW 优化器的初始学习率。"},
     },
     "num_train_epochs": {
-        "en": {
-            "label": "Epochs",
-            "info": "Total number of training epochs to perform."
-        },
-        "zh": {
-            "label": "训练轮数",
-            "info": "需要执行的训练总轮数。"
-        }
+        "en": {"label": "Epochs", "info": "Total number of training epochs to perform."},
+        "zh": {"label": "训练轮数", "info": "需要执行的训练总轮数。"},
     },
     "max_samples": {
-        "en": {
-            "label": "Max samples",
-            "info": "Maximum samples per dataset."
-        },
-        "zh": {
-            "label": "最大样本数",
-            "info": "每个数据集最多使用的样本数。"
-        }
+        "en": {"label": "Max samples", "info": "Maximum samples per dataset."},
+        "zh": {"label": "最大样本数", "info": "每个数据集最多使用的样本数。"},
     },
     "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。"
-        }
+        "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。"},
     },
     "batch_size": {
-        "en": {
-            "label": "Batch size",
-            "info": "Number of samples to process per GPU."
-        },
-        "zh":{
-            "label": "批处理大小",
-            "info": "每块 GPU 上处理的样本数量。"
-        }
+        "en": {"label": "Batch size", "info": "Number of samples to process per GPU."},
+        "zh": {"label": "批处理大小", "info": "每块 GPU 上处理的样本数量。"},
     },
     "gradient_accumulation_steps": {
-        "en": {
-            "label": "Gradient accumulation",
-            "info": "Number of gradient accumulation steps."
-        },
-        "zh": {
-            "label": "梯度累积",
-            "info": "梯度累积的步数。"
-        }
+        "en": {"label": "Gradient accumulation", "info": "Number of gradient accumulation steps."},
+        "zh": {"label": "梯度累积", "info": "梯度累积的步数。"},
     },
     "lr_scheduler_type": {
         "en": {
             "label": "LR Scheduler",
             "info": "Name of learning rate scheduler.",
         },
-        "zh": {
-            "label": "学习率调节器",
-            "info": "采用的学习率调节器名称。"
-        }
+        "zh": {"label": "学习率调节器", "info": "采用的学习率调节器名称。"},
     },
     "max_grad_norm": {
-        "en": {
-            "label": "Maximum gradient norm",
-            "info": "Norm for gradient clipping.."
-        },
-        "zh": {
-            "label": "最大梯度范数",
-            "info": "用于梯度裁剪的范数。"
-        }
+        "en": {"label": "Maximum gradient norm", "info": "Norm for gradient clipping.."},
+        "zh": {"label": "最大梯度范数", "info": "用于梯度裁剪的范数。"},
     },
     "val_size": {
-        "en": {
-            "label": "Val size",
-            "info": "Proportion of data in the dev set."
-        },
-        "zh": {
-            "label": "验证集比例",
-            "info": "验证集占全部样本的百分比。"
-        }
-    },
-    "extra_tab": {
-        "en": {
-            "label": "Extra configurations"
-        },
-        "zh": {
-            "label": "其它参数设置"
-        }
+        "en": {"label": "Val size", "info": "Proportion of data in the dev set."},
+        "zh": {"label": "验证集比例", "info": "验证集占全部样本的百分比。"},
     },
+    "extra_tab": {"en": {"label": "Extra configurations"}, "zh": {"label": "其它参数设置"}},
     "logging_steps": {
-        "en": {
-            "label": "Logging steps",
-            "info": "Number of steps between two logs."
-        },
-        "zh": {
-            "label": "日志间隔",
-            "info": "每两次日志输出间的更新步数。"
-        }
+        "en": {"label": "Logging steps", "info": "Number of steps between two logs."},
+        "zh": {"label": "日志间隔", "info": "每两次日志输出间的更新步数。"},
     },
     "save_steps": {
-        "en": {
-            "label": "Save steps",
-            "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。"
-        }
+        "en": {"label": "Save steps", "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。"
-        }
+        "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。"},
     },
     "neftune_alpha": {
-        "en": {
-            "label": "NEFTune Alpha",
-            "info": "Magnitude of noise adding to embedding vectors."
-        },
-        "zh": {
-            "label": "NEFTune 噪声参数",
-            "info": "嵌入向量所添加的噪声大小。"
-        }
+        "en": {"label": "NEFTune Alpha", "info": "Magnitude of noise adding to embedding vectors."},
+        "zh": {"label": "NEFTune 噪声参数", "info": "嵌入向量所添加的噪声大小。"},
     },
     "sft_packing": {
         "en": {
             "label": "Pack sequences",
-            "info": "Pack sequences into samples of fixed length in supervised fine-tuning."
+            "info": "Pack sequences into samples of fixed length in supervised fine-tuning.",
         },
-        "zh": {
-            "label": "序列打包",
-            "info": "在有监督微调阶段将序列打包为相同长度的样本。"
-        }
+        "zh": {"label": "序列打包", "info": "在有监督微调阶段将序列打包为相同长度的样本。"},
     },
     "upcast_layernorm": {
-        "en": {
-            "label": "Upcast LayerNorm",
-            "info": "Upcast weights of layernorm in float32."
-        },
-        "zh": {
-            "label": "缩放归一化层",
-            "info": "将归一化层权重缩放至 32 位精度。"
-        }
-    },
-    "lora_tab": {
-        "en": {
-            "label": "LoRA configurations"
-        },
-        "zh": {
-            "label": "LoRA 参数设置"
-        }
+        "en": {"label": "Upcast LayerNorm", "info": "Upcast weights of layernorm in float32."},
+        "zh": {"label": "缩放归一化层", "info": "将归一化层权重缩放至 32 位精度。"},
     },
+    "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。"
-        }
+        "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。"
-        }
+        "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": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules."
+            "info": "Name(s) of target modules to apply LoRA. Use commas to separate multiple modules.",
         },
-        "zh": {
-            "label": "LoRA 作用模块（非必填）",
-            "info": "应用 LoRA 的目标模块名称。使用英文逗号分隔多个名称。"
-        }
+        "zh": {"label": "LoRA 作用模块（非必填）", "info": "应用 LoRA 的目标模块名称。使用英文逗号分隔多个名称。"},
     },
     "additional_target": {
         "en": {
             "label": "Additional modules (optional)",
-            "info": "Name(s) of modules apart from LoRA layers to be set as trainable. Use commas to separate multiple modules."
+            "info": "Name(s) of modules apart from LoRA layers to be set as trainable. Use commas to separate multiple modules.",
         },
-        "zh": {
-            "label": "附加模块（非必填）",
-            "info": "除 LoRA 层以外的可训练模块名称。使用英文逗号分隔多个名称。"
-        }
+        "zh": {"label": "附加模块（非必填）", "info": "除 LoRA 层以外的可训练模块名称。使用英文逗号分隔多个名称。"},
     },
     "create_new_adapter": {
         "en": {
             "label": "Create new adapter",
-            "info": "Whether to create a new adapter with randomly initialized weight or not."
+            "info": "Whether to create a new adapter with randomly initialized weight or not.",
         },
-        "zh": {
-            "label": "新建适配器",
-            "info": "是否创建一个经过随机初始化的新适配器。"
-        }
-    },
-    "rlhf_tab": {
-        "en": {
-            "label": "RLHF configurations"
-        },
-        "zh": {
-            "label": "RLHF 参数设置"
-        }
+        "zh": {"label": "新建适配器", "info": "是否创建一个经过随机初始化的新适配器。"},
     },
+    "rlhf_tab": {"en": {"label": "RLHF configurations"}, "zh": {"label": "RLHF 参数设置"}},
     "dpo_beta": {
-        "en": {
-            "label": "DPO beta",
-            "info": "Value of the beta parameter in the DPO loss."
-        },
-        "zh": {
-            "label": "DPO beta 参数",
-            "info": "DPO 损失函数中 beta 超参数大小。"
-        }
+        "en": {"label": "DPO beta", "info": "Value of the beta parameter in the DPO loss."},
+        "zh": {"label": "DPO beta 参数", "info": "DPO 损失函数中 beta 超参数大小。"},
     },
     "dpo_ftx": {
-        "en": {
-            "label": "DPO-ftx weight",
-            "info": "The weight of SFT loss in the DPO-ftx."
-        },
-        "zh": {
-            "label": "DPO-ftx 权重",
-            "info": "DPO-ftx 中 SFT 损失的权重大小。"
-        }
+        "en": {"label": "DPO-ftx weight", "info": "The weight of SFT loss in the DPO-ftx."},
+        "zh": {"label": "DPO-ftx 权重", "info": "DPO-ftx 中 SFT 损失的权重大小。"},
     },
     "reward_model": {
         "en": {
             "label": "Reward model",
-            "info": "Adapter of the reward model for PPO training. (Needs to refresh adapters)"
+            "info": "Adapter of the reward model for PPO training. (Needs to refresh adapters)",
         },
-        "zh": {
-            "label": "奖励模型",
-            "info": "PPO 训练中奖励模型的适配器路径。（需要刷新适配器）"
-        }
-    },
-    "cmd_preview_btn": {
-        "en": {
-            "value": "Preview command"
-        },
-        "zh": {
-            "value": "预览命令"
-        }
-    },
-    "start_btn": {
-        "en": {
-            "value": "Start"
-        },
-        "zh": {
-            "value": "开始"
-        }
-    },
-    "stop_btn": {
-        "en": {
-            "value": "Abort"
-        },
-        "zh": {
-            "value": "中断"
-        }
+        "zh": {"label": "奖励模型", "info": "PPO 训练中奖励模型的适配器路径。（需要刷新适配器）"},
     },
+    "cmd_preview_btn": {"en": {"value": "Preview command"}, "zh": {"value": "预览命令"}},
+    "start_btn": {"en": {"value": "Start"}, "zh": {"value": "开始"}},
+    "stop_btn": {"en": {"value": "Abort"}, "zh": {"value": "中断"}},
     "output_dir": {
-        "en": {
-            "label": "Output dir",
-            "info": "Directory for saving results."
-        },
-        "zh": {
-            "label": "输出目录",
-            "info": "保存结果的路径。"
-        }
-    },
-    "output_box": {
-        "en": {
-            "value": "Ready."
-        },
-        "zh": {
-            "value": "准备就绪。"
-        }
-    },
-    "loss_viewer": {
-        "en": {
-            "label": "Loss"
-        },
-        "zh": {
-            "label": "损失"
-        }
-    },
-    "predict": {
-        "en": {
-            "label": "Save predictions"
-        },
-        "zh": {
-            "label": "保存预测结果"
-        }
-    },
-    "load_btn": {
-        "en": {
-            "value": "Load model"
-        },
-        "zh": {
-            "value": "加载模型"
-        }
-    },
-    "unload_btn": {
-        "en": {
-            "value": "Unload model"
-        },
-        "zh": {
-            "value": "卸载模型"
-        }
-    },
-    "info_box": {
-        "en": {
-            "value": "Model unloaded, please load a model first."
-        },
-        "zh": {
-            "value": "模型未加载，请先加载模型。"
-        }
-    },
-    "system": {
-        "en": {
-            "placeholder": "System prompt (optional)"
-        },
-        "zh": {
-            "placeholder": "系统提示词（非必填）"
-        }
-    },
-    "tools": {
-        "en": {
-            "placeholder": "Tools (optional)"
-        },
-        "zh": {
-            "placeholder": "工具列表（非必填）"
-        }
-    },
-    "query": {
-        "en": {
-            "placeholder": "Input..."
-        },
-        "zh": {
-            "placeholder": "输入..."
-        }
-    },
-    "submit_btn": {
-        "en": {
-            "value": "Submit"
-        },
-        "zh": {
-            "value": "提交"
-        }
-    },
-    "clear_btn": {
-        "en": {
-            "value": "Clear history"
-        },
-        "zh": {
-            "value": "清空历史"
-        }
-    },
-    "max_length": {
-        "en": {
-            "label": "Maximum length"
-        },
-        "zh": {
-            "label": "最大长度"
-        }
-    },
-    "max_new_tokens": {
-        "en": {
-            "label": "Maximum new tokens"
-        },
-        "zh": {
-            "label": "最大生成长度"
-        }
-    },
-    "top_p": {
-        "en": {
-            "label": "Top-p"
-        },
-        "zh": {
-            "label": "Top-p 采样值"
-        }
-    },
-    "temperature": {
-        "en": {
-            "label": "Temperature"
-        },
-        "zh": {
-            "label": "温度系数"
-        }
+        "en": {"label": "Output dir", "info": "Directory for saving results."},
+        "zh": {"label": "输出目录", "info": "保存结果的路径。"},
     },
+    "output_box": {"en": {"value": "Ready."}, "zh": {"value": "准备就绪。"}},
+    "loss_viewer": {"en": {"label": "Loss"}, "zh": {"label": "损失"}},
+    "predict": {"en": {"label": "Save predictions"}, "zh": {"label": "保存预测结果"}},
+    "load_btn": {"en": {"value": "Load model"}, "zh": {"value": "加载模型"}},
+    "unload_btn": {"en": {"value": "Unload model"}, "zh": {"value": "卸载模型"}},
+    "info_box": {"en": {"value": "Model unloaded, please load a model first."}, "zh": {"value": "模型未加载，请先加载模型。"}},
+    "system": {"en": {"placeholder": "System prompt (optional)"}, "zh": {"placeholder": "系统提示词（非必填）"}},
+    "tools": {"en": {"placeholder": "Tools (optional)"}, "zh": {"placeholder": "工具列表（非必填）"}},
+    "query": {"en": {"placeholder": "Input..."}, "zh": {"placeholder": "输入..."}},
+    "submit_btn": {"en": {"value": "Submit"}, "zh": {"value": "提交"}},
+    "clear_btn": {"en": {"value": "Clear history"}, "zh": {"value": "清空历史"}},
+    "max_length": {"en": {"label": "Maximum length"}, "zh": {"label": "最大长度"}},
+    "max_new_tokens": {"en": {"label": "Maximum new tokens"}, "zh": {"label": "最大生成长度"}},
+    "top_p": {"en": {"label": "Top-p"}, "zh": {"label": "Top-p 采样值"}},
+    "temperature": {"en": {"label": "Temperature"}, "zh": {"label": "温度系数"}},
     "max_shard_size": {
-        "en": {
-            "label": "Max shard size (GB)",
-            "info": "The maximum size for a model file."
-        },
-        "zh": {
-            "label": "最大分块大小（GB）",
-            "info": "单个模型文件的最大大小。"
-        }
+        "en": {"label": "Max shard size (GB)", "info": "The maximum size for a model file."},
+        "zh": {"label": "最大分块大小（GB）", "info": "单个模型文件的最大大小。"},
     },
     "export_quantization_bit": {
-        "en": {
-            "label": "Export quantization bit.",
-            "info": "Quantizing the exported model."
-        },
-        "zh": {
-            "label": "导出量化等级",
-            "info": "量化导出模型。"
-        }
+        "en": {"label": "Export quantization bit.", "info": "Quantizing the exported model."},
+        "zh": {"label": "导出量化等级", "info": "量化导出模型。"},
     },
     "export_quantization_dataset": {
-        "en": {
-            "label": "Export quantization dataset.",
-            "info": "The calibration dataset used for quantization."
-        },
-        "zh": {
-            "label": "导出量化数据集",
-            "info": "量化过程中使用的校准数据集。"
-        }
+        "en": {"label": "Export quantization dataset.", "info": "The calibration dataset used for quantization."},
+        "zh": {"label": "导出量化数据集", "info": "量化过程中使用的校准数据集。"},
     },
     "export_dir": {
-        "en": {
-            "label": "Export dir",
-            "info": "Directory to save exported model."
-        },
-        "zh": {
-            "label": "导出目录",
-            "info": "保存导出模型的文件夹路径。"
-        }
+        "en": {"label": "Export dir", "info": "Directory to save exported model."},
+        "zh": {"label": "导出目录", "info": "保存导出模型的文件夹路径。"},
     },
-    "export_btn": {
-        "en": {
-            "value": "Export"
-        },
-        "zh": {
-            "value": "开始导出"
-        }
-    }
+    "export_btn": {"en": {"value": "Export"}, "zh": {"value": "开始导出"}},
 }
 
 
 ALERTS = {
-    "err_conflict": {
-        "en": "A process is in running, please abort it firstly.",
-        "zh": "任务已存在，请先中断训练。"
-    },
-    "err_exists": {
-        "en": "You have loaded a model, please unload it first.",
-        "zh": "模型已存在，请先卸载模型。"
-    },
-    "err_no_model": {
-        "en": "Please select a model.",
-        "zh": "请选择模型。"
-    },
-    "err_no_path": {
-        "en": "Model not found.",
-        "zh": "模型未找到。"
-    },
-    "err_no_dataset": {
-        "en": "Please choose a dataset.",
-        "zh": "请选择数据集。"
-    },
-    "err_no_adapter": {
-        "en": "Please select an adapter.",
-        "zh": "请选择一个适配器。"
-    },
-    "err_no_export_dir": {
-        "en": "Please provide export dir.",
-        "zh": "请填写导出目录"
-    },
-    "err_failed": {
-        "en": "Failed.",
-        "zh": "训练出错。"
-    },
+    "err_conflict": {"en": "A process is in running, please abort it firstly.", "zh": "任务已存在，请先中断训练。"},
+    "err_exists": {"en": "You have loaded a model, please unload it first.", "zh": "模型已存在，请先卸载模型。"},
+    "err_no_model": {"en": "Please select a model.", "zh": "请选择模型。"},
+    "err_no_path": {"en": "Model not found.", "zh": "模型未找到。"},
+    "err_no_dataset": {"en": "Please choose a dataset.", "zh": "请选择数据集。"},
+    "err_no_adapter": {"en": "Please select an adapter.", "zh": "请选择一个适配器。"},
+    "err_no_export_dir": {"en": "Please provide export dir.", "zh": "请填写导出目录"},
+    "err_failed": {"en": "Failed.", "zh": "训练出错。"},
     "err_demo": {
         "en": "Training is unavailable in demo mode, duplicate the space to a private one first.",
-        "zh": "展示模式不支持训练，请先复制到私人空间。"
+        "zh": "展示模式不支持训练，请先复制到私人空间。",
     },
-    "err_device_count": {
-        "en": "Multiple GPUs are not supported yet.",
-        "zh": "尚不支持多 GPU 训练。"
-    },
-    "info_aborting": {
-        "en": "Aborted, wait for terminating...",
-        "zh": "训练中断，正在等待线程结束……"
-    },
-    "info_aborted": {
-        "en": "Ready.",
-        "zh": "准备就绪。"
-    },
-    "info_finished": {
-        "en": "Finished.",
-        "zh": "训练完毕。"
-    },
-    "info_loading": {
-        "en": "Loading model...",
-        "zh": "加载中……"
-    },
-    "info_unloading": {
-        "en": "Unloading model...",
-        "zh": "卸载中……"
-    },
-    "info_loaded": {
-        "en": "Model loaded, now you can chat with your model!",
-        "zh": "模型已加载，可以开始聊天了！"
-    },
-    "info_unloaded": {
-        "en": "Model unloaded.",
-        "zh": "模型已卸载。"
-    },
-    "info_exporting": {
-        "en": "Exporting model...",
-        "zh": "正在导出模型……"
-    },
-    "info_exported": {
-        "en": "Model exported.",
-        "zh": "模型导出完成。"
-    }
+    "err_device_count": {"en": "Multiple GPUs are not supported yet.", "zh": "尚不支持多 GPU 训练。"},
+    "info_aborting": {"en": "Aborted, wait for terminating...", "zh": "训练中断，正在等待线程结束……"},
+    "info_aborted": {"en": "Ready.", "zh": "准备就绪。"},
+    "info_finished": {"en": "Finished.", "zh": "训练完毕。"},
+    "info_loading": {"en": "Loading model...", "zh": "加载中……"},
+    "info_unloading": {"en": "Unloading model...", "zh": "卸载中……"},
+    "info_loaded": {"en": "Model loaded, now you can chat with your model!", "zh": "模型已加载，可以开始聊天了！"},
+    "info_unloaded": {"en": "Model unloaded.", "zh": "模型已卸载。"},
+    "info_exporting": {"en": "Exporting model...", "zh": "正在导出模型……"},
+    "info_exported": {"en": "Model exported.", "zh": "模型导出完成。"},
 }

src/llmtuner/webui/manager.py

@@ -1,11 +1,11 @@
 from typing import TYPE_CHECKING, Dict, List, Set
 
+
 if TYPE_CHECKING:
     from gradio.components import Component
 
 
 class Manager:
-
     def __init__(self) -> None:
         self.all_elems: Dict[str, Dict[str, "Component"]] = {}
 
@@ -26,7 +26,7 @@ class Manager:
             self.all_elems["top"]["quantization_bit"],
             self.all_elems["top"]["template"],
             self.all_elems["top"]["rope_scaling"],
-            self.all_elems["top"]["booster"]
+            self.all_elems["top"]["booster"],
         }
 
     def list_elems(self) -> List["Component"]:

src/llmtuner/webui/runner.py

@@ -1,12 +1,12 @@
+import logging
 import os
 import time
-import logging
-import gradio as gr
 from threading import Thread
-from gradio.components import Component # cannot use TYPE_CHECKING here
 from typing import TYPE_CHECKING, Any, Dict, Generator, Optional, Tuple
 
+import gradio as gr
 import transformers
+from gradio.components import Component  # cannot use TYPE_CHECKING here
 from transformers.trainer import TRAINING_ARGS_NAME
 
 from ..extras.callbacks import LogCallback
@@ -18,12 +18,12 @@ from .common import get_module, get_save_dir, load_config
 from .locales import ALERTS
 from .utils import gen_cmd, get_eval_results, update_process_bar
 
+
 if TYPE_CHECKING:
     from .manager import Manager
 
 
 class Runner:
-
     def __init__(self, manager: "Manager", demo_mode: Optional[bool] = False) -> None:
         self.manager = manager
         self.demo_mode = demo_mode
@@ -90,9 +90,12 @@ class Runner:
         user_config = load_config()
 
         if get("top.adapter_path"):
-            adapter_name_or_path = ",".join([
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
-            for adapter in get("top.adapter_path")])
+            adapter_name_or_path = ",".join(
+                [
+                    get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
+                    for adapter in get("top.adapter_path")
+                ]
+            )
         else:
             adapter_name_or_path = None
 
@@ -131,12 +134,12 @@ class Runner:
             create_new_adapter=get("train.create_new_adapter"),
             output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("train.output_dir")),
             fp16=(get("train.compute_type") == "fp16"),
-            bf16=(get("train.compute_type") == "bf16")
+            bf16=(get("train.compute_type") == "bf16"),
         )
         args["disable_tqdm"] = True
 
         if TRAINING_STAGES[get("train.training_stage")] in ["rm", "ppo", "dpo"]:
-            args["create_new_adapter"] = (args["quantization_bit"] is None)
+            args["create_new_adapter"] = args["quantization_bit"] is None
 
         if args["stage"] == "ppo":
             args["reward_model"] = get_save_dir(
@@ -161,9 +164,12 @@ class Runner:
         user_config = load_config()
 
         if get("top.adapter_path"):
-            adapter_name_or_path = ",".join([
-                get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
-            for adapter in get("top.adapter_path")])
+            adapter_name_or_path = ",".join(
+                [
+                    get_save_dir(get("top.model_name"), get("top.finetuning_type"), adapter)
+                    for adapter in get("top.adapter_path")
+                ]
+            )
         else:
             adapter_name_or_path = None
 
@@ -187,7 +193,7 @@ class Runner:
             max_new_tokens=get("eval.max_new_tokens"),
             top_p=get("eval.top_p"),
             temperature=get("eval.temperature"),
-            output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("eval.output_dir"))
+            output_dir=get_save_dir(get("top.model_name"), get("top.finetuning_type"), get("eval.output_dir")),
         )
 
         if get("eval.predict"):
@@ -197,7 +203,9 @@ class Runner:
 
         return args
 
-    def _preview(self, data: Dict[Component, Any], do_train: bool) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
+    def _preview(
+        self, data: Dict[Component, Any], do_train: bool
+    ) -> Generator[Tuple[str, Dict[str, Any]], None, None]:
         error = self._initialize(data, do_train, from_preview=True)
         if error:
             gr.Warning(error)
@@ -235,9 +243,11 @@ class Runner:
         get = lambda name: self.running_data[self.manager.get_elem_by_name(name)]
         self.running = True
         lang = get("top.lang")
-        output_dir = get_save_dir(get("top.model_name"), get("top.finetuning_type"), get(
-            "{}.output_dir".format("train" if self.do_train else "eval")
-        ))
+        output_dir = get_save_dir(
+            get("top.model_name"),
+            get("top.finetuning_type"),
+            get("{}.output_dir".format("train" if self.do_train else "eval")),
+        )
 
         while self.thread.is_alive():
             time.sleep(2)

src/llmtuner/webui/utils.py

@@ -1,13 +1,15 @@
-import os
 import json
-import gradio as gr
-from typing import TYPE_CHECKING, Any, Dict
+import os
 from datetime import datetime
+from typing import TYPE_CHECKING, Any, Dict
+
+import gradio as gr
 
 from ..extras.packages import is_matplotlib_available
 from ..extras.ploting import smooth
 from .common import get_save_dir
 
+
 if TYPE_CHECKING:
     from ..extras.callbacks import LogCallback
 
@@ -22,16 +24,13 @@ def update_process_bar(callback: "LogCallback") -> Dict[str, Any]:
 
     percentage = round(100 * callback.cur_steps / callback.max_steps, 0) if callback.max_steps != 0 else 100.0
     label = "Running {:d}/{:d}: {} < {}".format(
-        callback.cur_steps,
-        callback.max_steps,
-        callback.elapsed_time,
-        callback.remaining_time
+        callback.cur_steps, callback.max_steps, callback.elapsed_time, callback.remaining_time
     )
     return gr.update(label=label, value=percentage, visible=True)
 
 
 def get_time() -> str:
-    return datetime.now().strftime('%Y-%m-%d-%H-%M-%S')
+    return datetime.now().strftime("%Y-%m-%d-%H-%M-%S")
 
 
 def can_quantize(finetuning_type: str) -> Dict[str, Any]: