frontend/__init__.py

@@ -1,6 +1,6 @@
-from .train_page import *
 from .chat_page import *
 from .setting_page import *
+from .train_page import *
 from .model_manage_page import *
 from .dataset_manage_page import *
 from .dataset_generate_page import *

frontend/train_page.py

@@ -1,12 +1,9 @@
-import unsloth
 import gradio as gr
 import sys
 import torch
-import pandas as pd
 from tinydb import Query
 from pathlib import Path
-from datasets import Dataset as HFDataset
+from datasets import Dataset as HFDataset  
-from transformers import TrainerCallback
 
 from unsloth import FastLanguageModel
 from trl import SFTTrainer  # 用于监督微调的训练器
@@ -46,16 +43,6 @@ def train_page():
         # 训练状态输出
         output = gr.Textbox(label="训练日志", interactive=False)
         
-        # 添加loss曲线展示
-        loss_plot = gr.LinePlot(
-            x="step",
-            y="loss",
-            title="训练Loss曲线",
-            interactive=True,
-            width=600,
-            height=300
-        )
-        
         def train_model(dataset_name, learning_rate, per_device_train_batch_size, epoch, save_steps, lora_rank):
             # 使用动态传入的超参数
             learning_rate = float(learning_rate)
@@ -118,33 +105,6 @@ def train_page():
                                 fn_kwargs={"tokenizer": tokenizer},
                                 batched=True)
 
-            # 创建回调类
-            class GradioLossCallback(TrainerCallback):
-                def __init__(self):
-                    self.loss_data = []
-                    self.log_text = ""
-                    self.last_output = {"text": "", "plot": None}
-                
-                def on_log(self, args, state, control, logs=None, **kwargs):
-                    print(f"on_log called with logs: {logs}")  # 调试输出
-                    if "loss" in logs:
-                        print(f"Recording loss: {logs['loss']} at step {state.global_step}")  # 调试输出
-                        self.loss_data.append({
-                            "step": state.global_step,
-                            "loss": float(logs["loss"])  # 确保转换为float
-                        })
-                        self.log_text += f"Step {state.global_step}: loss={logs['loss']:.4f}\n"
-                        df = pd.DataFrame(self.loss_data)
-                        print(f"DataFrame created: {df}")  # 调试输出
-                        self.last_output = {
-                            "text": self.log_text,
-                            "plot": df
-                        }
-                    return control
-
-            # 初始化回调
-            callback = GradioLossCallback()
-
             # 初始化SFT训练器
             trainer = SFTTrainer(
                 model=model, # 待训练的模型
@@ -174,7 +134,6 @@ def train_page():
                     # report_to="tensorboard",  # 将信息输出到tensorboard
                 ),
             )
-            trainer.add_callback(callback)
 
             trainer = train_on_responses_only(
                 trainer,
@@ -184,28 +143,17 @@ def train_page():
 
             # 开始训练
             trainer_stats = trainer.train(resume_from_checkpoint=False)
-            return callback.last_output
 
-        def wrapped_train_model(*args):
-            print("Starting training...")  # 调试输出
-            result = train_model(*args)
-            print(f"Training completed with result: {result}")  # 调试输出
-            # 确保返回格式正确
-            if result and "text" in result and "plot" in result:
-                return result["text"], result["plot"]
-            return "", pd.DataFrame()  # 返回默认值
-            
         train_button.click(
-            fn=wrapped_train_model,
+            fn=train_model,
             inputs=[
                 dataset_dropdown,
                 learning_rate_input,
                 per_device_train_batch_size_input,
                 epoch_input,
-                save_steps_input,
+                save_steps_input
-                lora_rank_input
             ],
-            outputs=[output, loss_plot]
+            outputs=output
         )
 
     return demo