SFT on Nova 2.0

Amazon Nova Lite 2.0 brings enhanced capabilities for supervised fine-tuning, including advanced reasoning mode, improved multimodal understanding, and extended context handling. SFT on Nova 2.0 enables you to adapt these powerful capabilities to your specific use cases while maintaining the model's superior performance on complex tasks.

Key features of SFT on Nova 2.0 include:

run:
  name: my-full-rank-sft-run
  model_type: amazon.nova-2-lite-v1:0:256k
  model_name_or_path: nova-lite-2/prod
  data_s3_path: s3://my-bucket-name/train.jsonl  #  only and not compatible with SageMaker Training Jobs
  replicas: 4                                     # Number of compute instances for training, allowed values are 4, 8, 16, 32
  output_s3_path: s3://my-bucket-name/outputs/    # Output artifact path (HyperPod job-specific; not compatible with standard SageMaker Training Jobs)
  mlflow_tracking_uri: ""                         # Required for MLFlow
  mlflow_experiment_name: "my-full-rank-sft-experiment"  # Optional for MLFlow. Note: leave this field non-empty
  mlflow_run_name: "my-full-rank-sft-run"         # Optional for MLFlow. Note: leave this field non-empty

training_config:
  max_steps: 100                    # Maximum training steps. Minimal is 4.
  save_steps: ${oc.select:training_config.max_steps}  # How many training steps the checkpoint will be saved
  save_top_k: 5                     # Keep top K best checkpoints. Note supported only for  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
    adam_beta2: 0.95                 # Exponential decay rate for second-moment estimates

  peft:                             # Parameter-efficient fine-tuning (LoRA)
    peft_scheme: "null"             # Disable LoRA for PEFT

Reasoning Mode Selection (Nova 2.0 Only)

Amazon Nova 2.0 supports reasoning mode for enhanced analytical capabilities:

Tool calling data format

SFT supports training models to use tools (function calling). Below is a sample input format for tool calling:

Sample input:

{
  "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?"
        }
      ]
    }
  ]
}

Important considerations for tool calling data:

Document understanding data format

SFT supports training models on document understanding tasks. Below is a sample input format:

Sample input

{
  "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": [
        {
          "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?"
        }
      ],
      "reasoning_content": [
        {
          "text": "I need to find the relevant section in the document to answer the question.",
          "type": "text"
        }
      ]
    }
  ]
}

Important considerations for document understanding:

Video Understanding for SFT

SFT supports fine-tuning models for video understanding tasks. Below is a sample input format:

Sample input

{
  "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": [
        {
          "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?"
        }
      ],
      "reasoning_content": [
        {
          "text": "I need to find the relevant section in the video to answer the question.",
          "type": "text"
        }
      ]
    }
  ]
}

Important considerations for video understanding:

Data Upload Instructions

Upload training and validation datasets to an S3 bucket. Specify these locations in the recipe's run block:

## Run config
run:
  ...
  data_s3_path: "s3://<bucket-name>/<training-directory>/<training-file>.jsonl"
  

Note: Replace <bucket-name>, <training-directory>, <validation-directory>, <training-file>, and <validation-file> with actual S3 paths.

Note: Validation datasets are not currently supported for SFT with Amazon Nova 2.0. If a validation dataset is provided, it will be ignored.

Creating a Fine-Tuning Job

Define the base model using the model_type and model_name_or_path fields in the run block:

## Run config
run:
  ...
  model_type: amazon.nova-2-lite-v1:0:256k
  model_name_or_path: nova-lite-2/prod
  ...

SFT Tuning Parameters

The parameters that are available for tuning with SFT include:

Run Configuration

Training Configuration

Learning Rate Scheduler

Optimizer Configuration

PEFT Configuration

LoRA Tuning (when peft_scheme is 'lora')

Hyperparameter Guidance

Use the following recommended hyperparameters based on the training approach:

Full Rank Training

LoRA (Low-Rank Adaptation)

Note: Adjust these values based on dataset size and validation performance. Monitor training metrics to prevent overfitting.