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_improved.py

@@ -0,0 +1,99 @@
+#!/usr/bin/env python3
+"""
+Improved SFT training for GSM8K performance.
+Key improvements:
+1. More training data (247K examples from GSM8K + MetaMathQA)
+2. Multiple epochs with cosine LR schedule
+3. Proper batch size and gradient accumulation for H100
+4. Gradient checkpointing for memory efficiency
+"""
+
+import torch
+from datasets import load_dataset
+from transformers import AutoModelForCausalLM, AutoTokenizer
+from trl import SFTTrainer, SFTConfig
+import os
+
+def main():
+    # Load model and tokenizer
+    print("Loading model and tokenizer...")
+    model_name = "Qwen/Qwen3-1.7B"
+    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",  # Use SDPA instead of flash_attention_2
+        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 - optimized for H100 and GSM8K task
+    # With 247K examples and batch_size 8 * grad_accum 4 = effective batch 32
+    # Steps per epoch: 247467 / 32 ≈ 7733 steps
+    # 2 epochs ≈ 15466 steps
+    training_args = SFTConfig(
+        output_dir="./sft_output_improved",
+        num_train_epochs=2,
+        per_device_train_batch_size=8,
+        gradient_accumulation_steps=4,
+        learning_rate=2e-5,
+        lr_scheduler_type="cosine",
+        warmup_ratio=0.03,
+        weight_decay=0.01,
+        logging_steps=100,
+        save_steps=2000,
+        save_total_limit=3,
+        bf16=True,
+        gradient_checkpointing=True,
+        gradient_checkpointing_kwargs={"use_reentrant": False},
+        max_length=1024,  # Math problems don't need very long context
+        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=== Training Configuration ===")
+    print(f"Model: {model_name}")
+    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(f"Max length: {training_args.max_length}")
+    print("="*30)
+    
+    # Train
+    print("\nStarting training...")
+    trainer.train()
+    
+    # Save final model
+    print("\nSaving model to final_model/...")
+    trainer.save_model("final_model")
+    tokenizer.save_pretrained("final_model")
+    
+    print("Training complete!")
+
+if __name__ == "__main__":
+    main()