When to use What it achieves Dataset format Hyperparameters

SFT

Supervised Fine-Tuning trains a model on labeled input-output pairs to align its behavior with specific examples. The model learns to produce responses that match your training data.

When to use

You have high-quality prompt-response pairs for your target task
You want the model to learn a specific style, format, or domain knowledge
You need consistent behavior on well-defined tasks (summarization, classification, Q&A)

What it achieves

The model learns to mimic the patterns in your training examples, producing outputs that match the style, format, and content of your labeled data.

Dataset format

SFT supports two dataset formats. All datasets must be in JSONL format (one JSON object per line). A system message is optional in both formats.

Format 1: `messages`

Represent the conversation as a list of role-tagged messages. If included, the system message must be the first element.


{
  "messages": [
    {"role": "system", "content": "..."},
    {"role": "user", "content": "..."},
    {"role": "assistant", "content": "..."}
  ]
}

Format 2: `prompt`/`completion`

Provide the input and expected output as separate fields, with an optional top-level system field.


{
  "system": "...",
  "prompt": "...",
  "completion": "..."
}

Note

The system message is optional. Ensure diversity in prompts and response styles.

Hyperparameters

Note

The tables below show the hyperparameters available when you use serverless model customization. Other hyperparameters are preset by Amazon SageMaker AI using optimized defaults. When you use SageMaker AI Training Jobs or HyperPod, you can access the full list of hyperparameters available in the recipes. See the SageMaker AI Recipes repository to get a recipe and access all hyperparameters.

SFT LoRA

Parameter	Type	Required?	Range / Values	Description
`max_epochs`	integer	Required	1–100	Number of complete passes through the training dataset.
`global_batch_size`	integer	Required	8, 16, 32, 64, 128, 256, 512, 1024	Total samples processed per optimizer step across all instances.
`learning_rate`	float	Required	5e-07–1e-04	Step size for weight updates during optimization.
`lr_scheduler`	string	Required	cosine, constant	Learning rate decay schedule over training.
`lr_warmup_steps_ratio`	float	Required	0–1	Fraction of total steps spent ramping learning rate from 0.
`weight_decay`	float	Required	0.0–1.0	L2 regularization coefficient. Helps prevent overfitting.
`gradient_clipping`	boolean	Required	true, false	Scale down gradients if norm exceeds threshold.
`gradient_clipping_threshold`	float	Required	0.0–5.0	Maximum allowed gradient norm.
`dataset_max_len`	integer	Required	256–131072	Maximum sequence length in tokens. Longer sequences are truncated.
`seed`	integer	Required	0–2147483647	Random seed for reproducibility.
`logging_steps`	integer	Required	1–100	Frequency of metric logging in optimizer steps.
`lora_rank`	integer	Required	8, 16, 32, 64, 128	Dimensionality of low-rank matrices. Lower = fewer trainable parameters.
`lora_dropout`	float	Required	0.0–1.0	Dropout probability for LoRA adapter layers.
`lora_alpha`	integer	Required	16, 32, 64, 128, 256	LoRA scaling factor. Effective LR scales as alpha/rank.
`merge_weights`	boolean	Required	true, false	Merge LoRA weights into base model after training.
`train_val_split_ratio`	float	Optional	0.0–1.0	Fraction allocated to training vs validation.
`temperature`	float	Required	0.0–2.0	Sampling temperature for evaluation.

SFT FFT

Parameter	Type	Required?	Range / Values	Description
`max_epochs`	integer	Required	1–100	Number of complete passes through the training dataset.
`global_batch_size`	integer	Required	8, 16, 32, 64, 128, 256, 512, 1024	Total samples processed per optimizer step.
`learning_rate`	float	Required	5e-07–1e-04	Step size for weight updates.
`lr_scheduler`	string	Required	cosine, constant	Learning rate decay schedule.
`lr_warmup_steps_ratio`	float	Required	0–1	Fraction of steps for LR warmup.
`weight_decay`	float	Required	0.0–1.0	L2 regularization coefficient.
`gradient_clipping`	boolean	Required	true, false	Scale down gradients if norm exceeds threshold.
`gradient_clipping_threshold`	float	Required	0.0–5.0	Maximum allowed gradient norm.
`dataset_max_len`	integer	Required	256–131072	Maximum sequence length in tokens.
`max_response_length`	integer	Required	100–200000	Maximum tokens for generated response.
`seed`	integer	Required	0–2147483647	Random seed for reproducibility.
`logging_steps`	integer	Required	1–100	Frequency of metric logging.
`train_val_split_ratio`	float	Optional	0.0–1.0	Fraction allocated to training vs validation.
`temperature`	float	Required	0.0–2.0	Sampling temperature for evaluation.

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Document Conventions

Techniques

DPO

SFT

When to use

What it achieves

Dataset format

Format 1: messages

Format 2: prompt/completion

Note

Hyperparameters

Note

SFT LoRA

SFT FFT

Format 1: `messages`

Format 2: `prompt`/`completion`