初始化项目，由ModelHub XC社区提供模型

Model: HuggingFaceTB/qwen3-1.7b-gsm8k-sft
Source: Original Platform

scripts/train_continued.py

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+#!/usr/bin/env python3
+"""
+Continue training the already fine-tuned model with lower learning rate for additional refinement.
+"""
+
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+import os
+
+def main():
+    # Load from our previously trained model
+    print("Loading previously trained model from final_model/...")
+    model_name = "./final_model"
+    tokenizer = AutoTokenizer.from_pretrained(model_name)
+    
+    # Ensure pad token is set
+    if tokenizer.pad_token is None:
+        tokenizer.pad_token = tokenizer.eos_token
+    
+    model = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=torch.bfloat16,
+        attn_implementation="sdpa",
+        device_map="auto",
+    )
+    
+    # Load dataset
+    print("Loading dataset...")
+    dataset = load_dataset("json", data_files="combined_math_train.jsonl", split="train")
+    print(f"Dataset size: {len(dataset)}")
+    
+    # Training config - lower LR for refinement, 1 more epoch
+    training_args = SFTConfig(
+        output_dir="./sft_output_continued",
+        num_train_epochs=1,
+        per_device_train_batch_size=8,
+        gradient_accumulation_steps=4,
+        learning_rate=5e-6,  # Lower LR for continued training
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.01,
+        weight_decay=0.01,
+        logging_steps=100,
+        save_steps=2000,
+        save_total_limit=2,
+        bf16=True,
+        gradient_checkpointing=True,
+        gradient_checkpointing_kwargs={"use_reentrant": False},
+        max_length=1024,
+        packing=True,
+        report_to="none",
+        seed=42,
+        dataloader_num_workers=4,
+        optim="adamw_torch_fused",
+    )
+    
+    # Create trainer
+    print("Creating trainer...")
+    trainer = SFTTrainer(
+        model=model,
+        args=training_args,
+        train_dataset=dataset,
+        processing_class=tokenizer,
+    )
+    
+    # Print training info
+    print(f"\n=== Continued Training Configuration ===")
+    print(f"Model: {model_name} (previously fine-tuned)")
+    print(f"Dataset size: {len(dataset)}")
+    print(f"Batch size: {training_args.per_device_train_batch_size}")
+    print(f"Gradient accumulation: {training_args.gradient_accumulation_steps}")
+    print(f"Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
+    print(f"Learning rate: {training_args.learning_rate}")
+    print(f"Epochs: {training_args.num_train_epochs}")
+    print("="*40)
+    
+    # Train
+    print("\nStarting continued training...")
+    trainer.train()
+    
+    # Save final model
+    print("\nSaving model to final_model/...")
+    trainer.save_model("final_model")
+    tokenizer.save_pretrained("final_model")
+    
+    print("Continued training complete!")
+
+if __name__ == "__main__":
+    main()