Model requirements for training and validation datasets - Amazon Bedrock

Model requirements for training and validation datasets

The following sections list the requirements for training and validation datasets for a model. For information about dataset constraints for Amazon Nova models, see Fine-tuning Amazon Nova models.

Description Maximum (Fine-tuning)
Sum of input and output tokens when batch size is 1 4,096
Sum of input and output tokens when batch size is 2, 3, or 4 N/A
Character quota per sample in dataset Token quota x 6 (estimated)
Training dataset file size 1 GB
Validation dataset file size 100 MB
Description Maximum (Continued Pre-training) Maximum (Fine-tuning)
Sum of input and output tokens when batch size is 1 4,096 4,096
Sum of input and output tokens when batch size is 2, 3, or 4 2,048 2,048
Character quota per sample in dataset Token quota x 6 (estimated) Token quota x 6 (estimated)
Training dataset file size 10 GB 1 GB
Validation dataset file size 100 MB 100 MB
Description Maximum (Continued Pre-training) Maximum (Fine-tuning)
Sum of input and output tokens when batch size is 1 or 2 4,096 4,096
Sum of input and output tokens when batch size is 3, 4, 5, or 6 2,048 2,048
Character quota per sample in dataset Token quota x 6 (estimated) Token quota x 6 (estimated)
Training dataset file size 10 GB 1 GB
Validation dataset file size 100 MB 100 MB
Description Minimum (Fine-tuning) Maximum (Fine-tuning)
Text prompt length in training sample, in characters 3 1,024
Records in a training dataset 5 10,000
Input image size 0 50 MB
Input image height in pixels 512 4,096
Input image width in pixels 512 4,096
Input image total pixels 0 12,582,912
Input image aspect ratio 1:4 4:1
Description Minimum (Fine-tuning) Maximum (Fine-tuning)
Text prompt length in training sample, in characters 0 2,560
Records in a training dataset 1,000 500,000
Input image size 0 5 MB
Input image height in pixels 128 4096
Input image width in pixels 128 4096
Input image total pixels 0 12,528,912
Input image aspect ratio 1:4 4:1
Description Minimum (Fine-tuning) Maximum (Fine-tuning)
Input tokens 0 16,000
Output tokens 0 16,000
Character quota per sample in dataset 0 Token quota x 6 (estimated)
Sum of Input and Output tokens 0 16,000
Sum of training and validation records 100 10,000 (adjustable using service quotas)

Supported image formats for Meta Llama-3.2 11B Vision Instruct and Meta Llama-3.2 90B Vision Instruct include: gif, jpeg, png, and webp. For estimating the image-to-token conversion during fine-tuning of these models, you can use this formula as an approximation: Tokens = min(2, max(Height // 560, 1)) * min(2, max(Width // 560, 1)) * 1601. Images are converted into approximately 1,601 to 6,404 tokens based on their size.

Description Minimum (Fine-tuning) Maximum (Fine-tuning)
Sum of Input and Output tokens 0 16,000 (10000 for Meta Llama 3.2 90B)
Sum of training and validation records 100 10,000 (adjustable using service quotas)
Input image size for Meta Llama 11B and 90B instruct models) 0 10 MB
Input image height in pixels for Meta Llama 11B and 90B instruct models 10 8192
Input image width in pixels for Meta Llama 11B and 90B90B instruct models 10 8192
Description Minimum (Fine-tuning) Maximum (Fine-tuning)
Sum of Input and output tokens 0 16000
Sum of training and validation records 100 10,000 (adjustable using Service Quotas)
Description Maximum (Fine-tuning)
Input tokens 4,096
Output tokens 2,048
Character quota per sample in dataset Token quota x 6 (estimated)
Records in a training dataset 10,000
Records in a validation dataset 1,000
Description Maximum (Fine-tuning)
Minimum number of records 32
Maximum training records 10,000
Maximum validation records 1,000
Maximum total records 10,000 (adjustable using service quotas)
Maximum tokens 32,000
Maximum training dataset size 10 GB
Maximum validation dataset size 1 GB