微调 Nova 2.0

先决条件

在开始训练作业之前，请注意具备以下内容：

什么是 SFT？

监督式微调（SFT）使用带标注的输入-输出对训练语言模型。模型从包含提示和响应的示范示例中学习，优化能力以适配特定任务、指令或预期行为。

数据准备

概述

Nova 2.0 SFT 数据采用与 Nova 1.0 相同的 Converse API 格式，并新增了可选的推理内容字段。如需完整格式规范，请参阅：

支持的功能

目前的局限性

支持的媒体格式

数据格式示例

Text-only (Nova 1.0 compatible)

{  
  "schemaVersion": "bedrock-conversation-2024",  
  "system": [  
    {  
      "text": "You are a digital assistant with a friendly personality"  
    }  
  ],  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "text": "What country is right next to Australia?"  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "text": "The closest country is New Zealand"  
        }  
      ]  
    }  
  ]  
}

Text with reasoning (Nova 2.0)

{  
  "schemaVersion": "bedrock-conversation-2024",  
  "system": [  
    {  
      "text": "You are a digital assistant with a friendly personality"  
    }  
  ],  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "text": "What country is right next to Australia?"  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "reasoningContent": {  
            "reasoningText": {  
              "text": "I need to use my world knowledge of geography to answer this question"  
            }  
          }  
        },  
        {  
          "text": "The closest country to Australia is New Zealand, located to the southeast across the Tasman Sea."  
        }  
      ]  
    }  
  ]  
}

Image + text input

{  
  "schemaVersion": "bedrock-conversation-2024",  
  "system": [  
    {  
      "text": "You are a helpful assistant."  
    }  
  ],  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "image": {  
            "format": "jpeg",  
            "source": {  
              "s3Location": {  
                "uri": "s3://your-bucket/your-path/your-image.jpg",  
                "bucketOwner": "your-aws-account-id"  
              }  
            }  
          }  
        },  
        {  
          "text": "Which country is highlighted in the image?"  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "reasoningContent": {  
            "reasoningText": {  
              "text": "I will determine the highlighted country by examining its location on the map and using my geographical knowledge"  
            }  
          }  
        },  
        {  
          "text": "The highlighted country is New Zealand"  
        }  
      ]  
    }  
  ]  
}

Video + text input

{  
  "schemaVersion": "bedrock-conversation-2024",  
  "system": [  
    {  
      "text": "You are a helpful assistant."  
    }  
  ],  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "video": {  
            "format": "mp4",  
            "source": {  
              "s3Location": {  
                "uri": "s3://your-bucket/your-path/your-video.mp4",  
                "bucketOwner": "your-aws-account-id"  
              }  
            }  
          }  
        },  
        {  
          "text": "What is shown in this video?"  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "reasoningContent": {  
            "reasoningText": {  
              "text": "I will analyze the video content to identify key elements"  
            }  
          }  
        },  
        {  
          "text": "The video shows a map with New Zealand highlighted"  
        }  
      ]  
    }  
  ]  
}

工具调用

Nova 2.0 SFT 支持基于工具调用模式进行模型训练，使模型能够学习何时以及如何调用外部工具或函数。

工具调用的数据格式

工具调用训练数据包含用于定义可用工具的 toolConfig 部分，以及展示工具使用模式的对话轮次内容。

示例输入

{  
  "schemaVersion": "bedrock-conversation-2024",  
  "system": [  
    {  
      "text": "You are an expert in composing function calls."  
    }  
  ],  
  "toolConfig": {  
    "tools": [  
      {  
        "toolSpec": {  
          "name": "getItemCost",  
          "description": "Retrieve the cost of an item from the catalog",  
          "inputSchema": {  
            "json": {  
              "type": "object",  
              "properties": {  
                "item_name": {  
                  "type": "string",  
                  "description": "The name of the item to retrieve cost for"  
                },  
                "item_id": {  
                  "type": "string",  
                  "description": "The ASIN of item to retrieve cost for"  
                }  
              },  
              "required": [  
                "item_id"  
              ]  
            }  
          }  
        }  
      },  
      {  
        "toolSpec": {  
          "name": "getItemAvailability",  
          "description": "Retrieve whether an item is available in a given location",  
          "inputSchema": {  
            "json": {  
              "type": "object",  
              "properties": {  
                "zipcode": {  
                  "type": "string",  
                  "description": "The zipcode of the location to check in"  
                },  
                "quantity": {  
                  "type": "integer",  
                  "description": "The number of items to check availability for"  
                },  
                "item_id": {  
                  "type": "string",  
                  "description": "The ASIN of item to check availability for"  
                }  
              },  
              "required": [  
                "item_id", "zipcode"  
              ]  
            }  
          }  
        }  
      }  
    ]  
  },  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "text": "I need to check whether there are twenty pieces of the following item available. Here is the item ASIN on Amazon: id-123. Please check for the zipcode 94086"  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "reasoningContent": {  
            "reasoningText": {  
              "text": "The user wants to check how many pieces of the item with ASIN id-123 are available in the zipcode 94086"  
            }  
          }  
        },  
        {  
          "toolUse": {  
            "toolUseId": "getItemAvailability_0",  
            "name": "getItemAvailability",  
            "input": {  
              "zipcode": "94086",  
              "quantity": 20,  
              "item_id": "id-123"  
            }  
          }  
        }  
      ]  
    },  
    {  
      "role": "user",  
      "content": [  
        {  
          "toolResult": {  
            "toolUseId": "getItemAvailability_0",  
            "content": [  
              {  
                "text": "[{\"name\": \"getItemAvailability\", \"results\": {\"availability\": true}}]"  
              }  
            ]  
          }  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {  
          "text": "Yes, there are twenty pieces of item id-123 available at 94086. Would you like to place an order or know the total cost?"  
        }  
      ]  
    }  
  ]  
}

工具调用要求

创建工具调用训练数据时，请遵循以下要求：

重要提示

文档理解

Nova 2.0 SFT 支持基于文档任务的训练，使模型能够学习如何分析并回答与 PDF 文档相关的问题。

文档理解数据格式

文档理解训练数据在用户内容块中包含文档引用，模型将学习对文档内容进行提取与推理。

示例输入

{  
{  
  "schemaVersion": "bedrock-conversation-2024",  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "text": "What are the ways in which a customer can experience issues during checkout on Amazon?"  
        },  
        {  
          "document": {  
            "format": "pdf",  
            "source": {  
              "s3Location": {  
                "uri": "s3://my-bucket-name/path/to/documents/customer_service_debugging.pdf",  
                "bucketOwner": "123456789012"  
              }  
            }  
          }  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {
          "reasoningContent": {  
            "reasoningText": {  
              "text": "I need to find the relevant section in the document to answer the question."  
            }  
          }
        },
        {  
          "text": "Customers can experience issues with 1. Data entry, 2. Payment methods, 3. Connectivity while placing the order. Which one would you like to dive into?"  
        }   
      ]
    }  
  ]  
}
}

文档理解限制

文档理解最佳实践

视频理解

Nova 2.0 SFT 支持基于视频任务的训练，使模型能够学习如何分析并回答与视频内容相关的问题。

视频理解数据格式

视频理解训练数据在用户内容块中包含视频引用，模型将学习从视频内容中提取信息并进行推理。

示例输入

  
{  
  "schemaVersion": "bedrock-conversation-2024",  
  "messages": [  
    {  
      "role": "user",  
      "content": [  
        {  
          "text": "What are the ways in which a customer can experience issues during checkout on Amazon?"  
        },  
        {  
          "video": {  
            "format": "mp4",  
            "source": {  
              "s3Location": {  
                "uri": "s3://my-bucket-name/path/to/videos/customer_service_debugging.mp4",  
                "bucketOwner": "123456789012"  
              }  
            }  
          }  
        }  
      ]  
    },  
    {  
      "role": "assistant",  
      "content": [  
        {
          "reasoningContent": {  
            "reasoningText": {  
              "text": "I need to find the relevant section in the video to answer the question."  
            }  
          }
        },
        {  
          "text": "Customers can experience issues with 1. Data entry, 2. Payment methods, 3. Connectivity while placing the order. Which one would you like to dive into?"  
        }   
      ]  
    }  
  ]  
}

视频理解限制

支持的视频格式

视频理解最佳实践

推理模式与非推理模式

理解推理内容

推理内容（亦称思维链）会记录模型在生成最终答案前的中间思考步骤。在 assistant 轮次中，可通过 reasoningContent 字段加入这些推理轨迹。

损失计算方式

您可在多轮对话的多个助手轮次中添加 reasoningContent。

格式规范

何时启用推理模式

在以下场景中，在训练配置里设置 reasoning_enabled: true：

允许在 reasoning_enabled = true 的情况下，使用非推理数据集训练 Nova。但是，这样做可能会导致模型丧失推理能力，因为 Nova 主要学习数据中呈现的应答方式，而非执行推理过程。如果希望使用非推理数据集训练，同时在推理阶段保留推理能力，则可在训练时关闭推理 (reasoning_enabled = false)，并在推理时启用。这种方式虽能在推理时使用推理，但无法保证效果优于不启用推理的推理方式。一般建议：使用推理数据集时，训练与推理均启用推理；使用非推理数据集时，两者均关闭推理。

在以下场景中，设置 reasoning_enabled: false：

生成推理数据

如果数据集缺失推理轨迹，可借助 Nova Premier 等具备推理能力的模型来生成。将输入-输出对提供给模型，记录其推理过程，从而构建包含推理内容的增强型数据集。

使用推理词元进行训练

启用推理模式进行训练时，模型会学习将内部推理过程与最终答案分离开。训练过程：

优质推理内容的要素

高质量的推理内容应包含以下要素：

这些要素有助于模型掌握“先思考，后作答”的能力。

数据集准备指南

规模与质量

多样性

样本需具备多样性，满足以下要求：

输出格式

在助手响应中明确指定所需输出格式：

多回合对话

使用多轮对话数据集时请注意：

质量检查清单

上传数据

数据集需上传到 SageMaker 训练作业可访问的存储桶。有关设置相应权限的信息，请参阅先决条件。

启动训练作业

选择超参数并更新配方

Nova 2.0 的设置方式与 Nova 1.0 基本一致。将输入数据上传到 S3 后，使用 SageMaker HyperPod Recipes 中 fine-tuning 文件夹下的配方。对于 Nova 2.0，以下是一些可以根据使用案例更新的关键超参数。以下是 Nova 2.0 SFT PEFT 配方的样本。对于容器映像 URI，请使用 708977205387.dkr.ecr.us-east-1.amazonaws.com/nova-fine-tune-repo:SM-TJ-SFT-V2-latest 来运行 SFT 微调作业。

请使用 v2.254.1 版本的 SageMaker AI PySDK，确保与 Nova 训练严格兼容。将 SDK 升级到 v3.0 版本会导致破坏性变更。对 SageMaker AI PySDK v3 的支持即将推出。

示例输入

!pip install sagemaker==2.254.1

run:  
  name: {peft_recipe_job_name}  
  model_type: amazon.nova-2-lite-v1:0:256k  
  model_name_or_path: {peft_model_name_or_path}  
  data_s3_path: {train_dataset_s3_path} # SageMaker HyperPod (SMHP) only and not compatible with SageMaker Training jobs. Note replace my-bucket-name with your real bucket name for SMHP job  
  replicas: 4                      # Number of compute instances for training, allowed values are 4, 8, 16, 32  
  output_s3_path: ""               # Output artifact path (Hyperpod job-specific; not compatible with standard SageMaker Training jobs). Note replace my-bucket-name with your real bucket name for SMHP job  
  
training_config:  
  max_steps: 10                   # Maximum training steps. Minimal is 4.  
  save_steps: 10                      # How many training steps the checkpoint will be saved. Should be less than or equal to max_steps  
  save_top_k: 1                    # Keep top K best checkpoints. Note supported only for SageMaker HyperPod jobs. Minimal is 1.  
  max_length: 32768                # Sequence length (options: 8192, 16384, 32768 [default], 65536)  
  global_batch_size: 32            # Global batch size (options: 32, 64, 128)  
  reasoning_enabled: true          # If data has reasoningContent, set to true; otherwise False  
  
  lr_scheduler:  
    warmup_steps: 15               # Learning rate warmup steps. Recommend 15% of max_steps  
    min_lr: 1e-6                   # Minimum learning rate, must be between 0.0 and 1.0  
  
  optim_config:                    # Optimizer settings  
    lr: 1e-5                       # Learning rate, must be between 0.0 and 1.0  
    weight_decay: 0.0              # L2 regularization strength, must be between 0.0 and 1.0  
    adam_beta1: 0.9                # Exponential decay rate for first-moment estimates, must be between 0.0 and 1.0  
    adam_beta2: 0.95               # Exponential decay rate for second-moment estimates, must be between 0.0 and 1.0  
  
  peft:                            # Parameter-efficient fine-tuning (LoRA)  
    peft_scheme: "lora"            # Enable LoRA for PEFT  
    lora_tuning:  
      alpha: 64                    # Scaling factor for LoRA weights ( options: 32, 64, 96, 128, 160, 192),  
      lora_plus_lr_ratio: 64.0

该配方包含的超参数与 Nova 1.0 基本一致。核心超参数如下：

启动训练作业

from sagemaker.pytorch import PyTorch  
  
# define OutputDataConfig path  
if default_prefix:  
    output_path = f"s3://{bucket_name}/{default_prefix}/{sm_training_job_name}"  
else:  
    output_path = f"s3://{bucket_name}/{sm_training_job_name}"  

output_kms_key = "<KMS key arn to encrypt trained model in Amazon-owned S3 bucket>" # optional, leave blank for Amazon managed encryption
  
recipe_overrides = {  
    "run": {  
        "replicas": instance_count,  # Required  
        "output_s3_path": output_path  
    },  
}  
  
estimator = PyTorch(  
    output_path=output_path,  
    base_job_name=sm_training_job_name,  
    role=role,  
    disable_profiler=True,  
    debugger_hook_config=False,  
    instance_count=instance_count,  
    instance_type=instance_type,  
    training_recipe=training_recipe,  
    recipe_overrides=recipe_overrides,  
    max_run=432000,  
    sagemaker_session=sagemaker_session,  
    image_uri=image_uri,
    output_kms_key=output_kms_key,
    tags=[  
        {'Key': 'model_name_or_path', 'Value': model_name_or_path},  
    ]  
)  
  
print(f"\nsm_training_job_name:\n{sm_training_job_name}\n")  
print(f"output_path:\n{output_path}")

from sagemaker.inputs import TrainingInput  
  
train_input = TrainingInput(  
    s3_data=train_dataset_s3_path,  
    distribution="FullyReplicated",  
    s3_data_type="Converse",  
)  
  
estimator.fit(inputs={"validation": val_input}, wait=False)

要启动作业，请执行以下操作：

笔记本将自动处理作业提交，并提供状态跟踪功能。