Important

NeMo 2.0 is an experimental feature and currently released in the dev container only: nvcr.io/nvidia/nemo:dev. Please refer to NeMo 2.0 overview for information on getting started.

mT5

Released in 2020, Google’s mT5 is a multilingual language model based on the transformer encoder-decoder framework. Leveraging a massive multilingual dataset with over 100 languages, mT5 achieved state-of-the-art benchmark scores across multiple languages. With offerings in “small”, “base”, “large”, “xl”, and “xxl”, mt5 has a variety of model sizes to suit a variety of needs. More information is available in the companion paper “mT5: A massively multilingual pre-trained text-to-text transformer”.

• Data Preparation
• Training
• Training with Predefined Configurations
• Checkpoint Conversion
• Model Evaluation
• PEFT Training and Inference
• Model Fine-Tuning
• MT5 Results

Feature	T5/mT5
Data parallelism	✓
Tensor parallelism	✓
Pipeline parallelism	✓
Interleaved Pipeline Parallelism Sched	N/A
Sequence parallelism	✗
Selective activation checkpointing	✗
Gradient checkpointing	✓
Partial gradient checkpointing	✓
FP32/TF32	✓
AMP/FP16	✗
BF16	✓
TransformerEngine/FP8	✗
Multi-GPU	✓
Multi-Node	✓
Inference	N/A
Slurm	✓
Base Command Manager	✓
Base Command Platform	✓
Distributed data preprcessing	✓
NVfuser	✗
P-Tuning and Prompt Tuning	✓
IA3 and Adapter learning	✓
Distributed Optimizer	✓
Distributed Checkpoint	N/A
Fully Shared Data Parallel	N/A