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Chapter 7-Fine-tuning to follow instructions

7.6 Finetuning the LLM on instruction data

• 本节我们开始微调当前模型

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• 让我们在开始训练之前计算初始训练和验证集损失（与前几章一样，目标是最小化损失）

  model.to(device)torch.manual_seed(123)with torch.no_grad():train_loss = calc_loss_loader(train_loader, model, device, num_batches=5)val_loss = calc_loss_loader(val_loader, model, device, num_batches=5)print("Training loss:", train_loss)
  print("Validation loss:", val_loss)"""输出"""
  Training loss: 3.8259095668792726
  Validation loss: 3.761933755874634
  

• 请注意，这次的训练比之前的章节要稍微昂贵一些，因为我们正在使用一个更大的模型（有 3.55 亿个参数，而不是 1.24 亿个参数）。

  各种设备的运行时间如下所示以供参考（在兼容的 GPU 设备上运行此笔记本无需对代码进行任何更改）。

Model	Device	Runtime for 2 Epochs
gpt2-medium (355M)	CPU (M3 MacBook Air)	15.78 minutes
gpt2-medium (355M)	GPU (M3 MacBook Air)	10.77 minutes
gpt2-medium (355M)	GPU (L4)	1.83 minutes
gpt2-medium (355M)	GPU (A100)	0.86 minutes
gpt2-small (124M)	CPU (M3 MacBook Air)	5.74 minutes
gpt2-small (124M)	GPU (M3 MacBook Air)	3.73 minutes
gpt2-small (124M)	GPU (L4)	0.69 minutes
gpt2-small (124M)	GPU (A100)	0.39 minutes

• 3060 12G运行

  import timestart_time = time.time()torch.manual_seed(123)optimizer = torch.optim.AdamW(model.parameters(), lr=0.00005, weight_decay=0.1)num_epochs = 2train_losses, val_losses, tokens_seen = train_model_simple(model, train_loader, val_loader, optimizer, device,num_epochs=num_epochs, eval_freq=5, eval_iter=5,start_context=format_input(val_data[0]), tokenizer=tokenizer
  )end_time = time.time()
  execution_time_minutes = (end_time - start_time) / 60
  print(f"Training completed in {execution_time_minutes:.2f} minutes.")"""输出"""
  Ep 1 (Step 000000): Train loss 2.637, Val loss 2.626
  Ep 1 (Step 000005): Train loss 1.174, Val loss 1.102
  Ep 1 (Step 000010): Train loss 0.872, Val loss 0.944
  Ep 1 (Step 000015): Train loss 0.857, Val loss 0.906
  Ep 1 (Step 000020): Train loss 0.776, Val loss 0.881
  Ep 1 (Step 000025): Train loss 0.754, Val loss 0.859
  Ep 1 (Step 000030): Train loss 0.799, Val loss 0.836
  Ep 1 (Step 000035): Train loss 0.714, Val loss 0.808
  Ep 1 (Step 000040): Train loss 0.672, Val loss 0.806
  Ep 1 (Step 000045): Train loss 0.633, Val loss 0.789
  Ep 1 (Step 000050): Train loss 0.663, Val loss 0.783
  Ep 1 (Step 000055): Train loss 0.760, Val loss 0.763
  Ep 1 (Step 000060): Train loss 0.719, Val loss 0.743
  Ep 1 (Step 000065): Train loss 0.653, Val loss 0.735
  Ep 1 (Step 000070): Train loss 0.532, Val loss 0.729
  Ep 1 (Step 000075): Train loss 0.569, Val loss 0.728
  Ep 1 (Step 000080): Train loss 0.605, Val loss 0.725
  Ep 1 (Step 000085): Train loss 0.509, Val loss 0.709
  Ep 1 (Step 000090): Train loss 0.562, Val loss 0.691
  Ep 1 (Step 000095): Train loss 0.501, Val loss 0.682
  Ep 1 (Step 000100): Train loss 0.503, Val loss 0.677
  Ep 1 (Step 000105): Train loss 0.564, Val loss 0.670
  Ep 1 (Step 000110): Train loss 0.555, Val loss 0.666
  Ep 1 (Step 000115): Train loss 0.508, Val loss 0.664
  Below is an instruction that describes a task. Write a response that appropriately completes the request.  ### Instruction: Convert the active sentence to passive: 'The chef cooks the meal every day.'  ### Response: The meal is prepared every day by the chef.<|endoftext|>The following is an instruction that describes a task. Write a response that appropriately completes the request.  ### Instruction: Convert the active sentence to passive:
  Ep 2 (Step 000120): Train loss 0.435, Val loss 0.672
  Ep 2 (Step 000125): Train loss 0.451, Val loss 0.686
  Ep 2 (Step 000130): Train loss 0.447, Val loss 0.682
  Ep 2 (Step 000135): Train loss 0.405, Val loss 0.681
  Ep 2 (Step 000140): Train loss 0.409, Val loss 0.681
  Ep 2 (Step 000145): Train loss 0.368, Val loss 0.681
  Ep 2 (Step 000150): Train loss 0.382, Val loss 0.675
  Ep 2 (Step 000155): Train loss 0.413, Val loss 0.675
  Ep 2 (Step 000160): Train loss 0.415, Val loss 0.683
  Ep 2 (Step 000165): Train loss 0.379, Val loss 0.685
  Ep 2 (Step 000170): Train loss 0.323, Val loss 0.681
  Ep 2 (Step 000175): Train loss 0.337, Val loss 0.669
  Ep 2 (Step 000180): Train loss 0.392, Val loss 0.656
  Ep 2 (Step 000185): Train loss 0.415, Val loss 0.657
  Ep 2 (Step 000190): Train loss 0.341, Val loss 0.648
  Ep 2 (Step 000195): Train loss 0.329, Val loss 0.635
  Ep 2 (Step 000200): Train loss 0.310, Val loss 0.635
  Ep 2 (Step 000205): Train loss 0.352, Val loss 0.631
  Ep 2 (Step 000210): Train loss 0.367, Val loss 0.630
  Ep 2 (Step 000215): Train loss 0.395, Val loss 0.634
  Ep 2 (Step 000220): Train loss 0.302, Val loss 0.648
  Ep 2 (Step 000225): Train loss 0.348, Val loss 0.661
  Ep 2 (Step 000230): Train loss 0.295, Val loss 0.656
  Below is an instruction that describes a task. Write a response that appropriately completes the request.  ### Instruction: Convert the active sentence to passive: 'The chef cooks the meal every day.'  ### Response: The meal is cooked every day by the chef.<|endoftext|>The following is an instruction that describes a task. Write a response that appropriately completes the request.  ### Instruction: What is the capital of the United Kingdom
  Training completed in 2.23 minutes.
  

  共使用2.23分钟

  正如我们根据上面的输出所看到的，模型训练得很好，我们可以根据不断减少的训练损失和验证损失值来判断
  此外，根据每个epoch后打印的响应文本，我们可以看到模型正确地遵循指令将输入句子 `'The chef cooks the meal every day.‘转换为被动语态’The meal is cooked every day by the chef.’（我们将在后面的部分中正确格式化和评估响应）

• 最后，让我们看一下训练和验证损失曲线

  from previous_chapters import plot_lossesepochs_tensor = torch.linspace(0, num_epochs, len(train_losses))
  plot_losses(epochs_tensor, tokens_seen, train_losses, val_losses)
  

  

  如我们所见，损失在第一个epoch开始时急剧减少，这意味着模型开始快速学习.我们可以看到在第一个epoch后轻微的过度拟合。