AI BriefingGuide to Efficient Fine-tuning of Llama 2 70B
Key point
It covers how to fine-tune the Llama 2 70B model using PyTorch FSDP while solving memory and checkpoint saving issues.
Details
It introduces how to efficiently fine-tune large-scale models such as Llama 2 70B using PyTorch FSDP (Fully Sharded Data Parallelism), along with best practices.
It presents three major challenges that arise when training large-scale models, along with their solutions:
- CPU RAM shortage issue: To resolve the memory overflow that occurs when every process loads the model individually, a meta device is used to create the model without weights, after which weights are loaded only on Rank 0 and broadcast to the other ranks via the
sync_module_states=Truesetting. - Checkpoint saving issue: It covers how to manage the long save times and NCCL timeout errors that occur when using
FULL_STATE_DICT. - Resource optimization: It includes optimization methods that apply Flash Attention V2 to reduce VRAM usage and increase training speed.
This guide utilizes Hugging Face's Transformers, Accelerate, and TRL libraries, and also provides instructions for running it in a SLURM environment.
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