config.yaml

# script parameters
model_id: "meta-llama/Meta-Llama-3-70b" # Hugging Face model id
dataset_path: "."                      # path to dataset
max_seq_len:  3072 # 2048              # max sequence length for model and packing of the dataset
# training parameters
output_dir: "./llama-3-70b-hf-no-robot" # Temporary output directory for model checkpoints
report_to: "tensorboard"               # report metrics to tensorboard
learning_rate: 0.0002                  # learning rate 2e-4
lr_scheduler_type: "constant"          # learning rate scheduler
num_train_epochs: 3                    # number of training epochs
per_device_train_batch_size: 1         # batch size per device during training
per_device_eval_batch_size: 1          # batch size for evaluation
gradient_accumulation_steps: 2         # number of steps before performing a backward/update pass
optim: adamw_torch                     # use torch adamw optimizer
logging_steps: 10                      # log every 10 steps
save_strategy: epoch                   # save checkpoint every epoch
evaluation_strategy: epoch             # evaluate every epoch
max_grad_norm: 0.3                     # max gradient norm
warmup_ratio: 0.03                     # warmup ratio
bf16: true                             # use bfloat16 precision
tf32: true                             # use tf32 precision
gradient_checkpointing: true           # use gradient checkpointing to save memory
# FSDP parameters: https://huggingface.co/docs/transformers/main/en/fsdp
fsdp: "full_shard auto_wrap offload" # remove offload if enough GPU memory
fsdp_config:
  - backward_prefetch: "backward_pre"
  - forward_prefetch: "false"
  - use_orig_params: "false"