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.
Baichuan 2#
Released in 2023, Baichuan Intelligence Inc.’s Baichuan 2 is a large multilingual language model meant to fill a gap in the English-dominated LLM ecosystem. Based on the prevailing transformer decoder framework, Baichuan 2 makes a few interesting adjustments, including SwiGLU activations, memory-efficient attention from xFormers, and Layer Normalization on the input of the transformer block. Additionally, the 7B model uses RoPE; while the 13B model uses Attention with Linear Biases. More information is available in the paper “Baichuan 2: Open Large-scale Language Models”.
Feature |
Status |
|---|---|
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 |
✓ |
Fully Shared Data Parallel |
N/A |