100 lines
3.2 KiB
Python
100 lines
3.2 KiB
Python
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#!/usr/bin/env python3
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"""
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Improved SFT training for GSM8K performance.
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Key improvements:
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1. More training data (247K examples from GSM8K + MetaMathQA)
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2. Multiple epochs with cosine LR schedule
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3. Proper batch size and gradient accumulation for H100
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4. Gradient checkpointing for memory efficiency
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"""
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import torch
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from datasets import load_dataset
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from transformers import AutoModelForCausalLM, AutoTokenizer
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from trl import SFTTrainer, SFTConfig
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import os
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def main():
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# Load model and tokenizer
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print("Loading model and tokenizer...")
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model_name = "Qwen/Qwen3-1.7B"
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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# Ensure pad token is set
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if tokenizer.pad_token is None:
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tokenizer.pad_token = tokenizer.eos_token
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype=torch.bfloat16,
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attn_implementation="sdpa", # Use SDPA instead of flash_attention_2
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device_map="auto",
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)
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# Load dataset
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print("Loading dataset...")
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dataset = load_dataset("json", data_files="combined_math_train.jsonl", split="train")
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print(f"Dataset size: {len(dataset)}")
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# Training config - optimized for H100 and GSM8K task
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# With 247K examples and batch_size 8 * grad_accum 4 = effective batch 32
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# Steps per epoch: 247467 / 32 ≈ 7733 steps
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# 2 epochs ≈ 15466 steps
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training_args = SFTConfig(
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output_dir="./sft_output_improved",
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num_train_epochs=2,
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per_device_train_batch_size=8,
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gradient_accumulation_steps=4,
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learning_rate=2e-5,
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lr_scheduler_type="cosine",
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warmup_ratio=0.03,
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weight_decay=0.01,
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logging_steps=100,
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save_steps=2000,
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save_total_limit=3,
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bf16=True,
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gradient_checkpointing=True,
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gradient_checkpointing_kwargs={"use_reentrant": False},
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max_length=1024, # Math problems don't need very long context
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packing=True,
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report_to="none",
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seed=42,
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dataloader_num_workers=4,
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optim="adamw_torch_fused",
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)
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# Create trainer
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print("Creating trainer...")
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trainer = SFTTrainer(
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model=model,
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args=training_args,
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train_dataset=dataset,
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processing_class=tokenizer,
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)
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# Print training info
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print(f"\n=== Training Configuration ===")
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print(f"Model: {model_name}")
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print(f"Dataset size: {len(dataset)}")
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print(f"Batch size: {training_args.per_device_train_batch_size}")
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print(f"Gradient accumulation: {training_args.gradient_accumulation_steps}")
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print(f"Effective batch size: {training_args.per_device_train_batch_size * training_args.gradient_accumulation_steps}")
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print(f"Learning rate: {training_args.learning_rate}")
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print(f"Epochs: {training_args.num_train_epochs}")
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print(f"Max length: {training_args.max_length}")
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print("="*30)
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# Train
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print("\nStarting training...")
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trainer.train()
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# Save final model
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print("\nSaving model to final_model/...")
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trainer.save_model("final_model")
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tokenizer.save_pretrained("final_model")
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print("Training complete!")
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if __name__ == "__main__":
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main()
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