FlagScale/examples/llama2/conf/train/7b.yaml at main · 1235v/FlagScale · GitHub

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system:
  tensor_model_parallel_size: 1
  pipeline_model_parallel_size: 1
  disable_bias_linear: True
  use_flash_attn: True
  sequence_parallel: True
  use_distributed_optimizer: True
  precision:
    fp16: True
    initial_loss_scale: 16384
    min_loss_scale: 1.0
  logging:
    log_interval: 1
    tensorboard_log_interval: 1
    wandb_project: "train-llama-7B"
    wandb_exp_name: "train-test-7B"
  checkpoint:
    save_interval: 10

model:
  use_mcore_models: True
  transformer_impl: transformer_engine
  num_layers: 32
  hidden_size: 4096
  ffn_hidden_size: 28672
  num_attention_heads: 32
  seq_length: 2048
  group_query_attention: False
  num_query_groups: 8
  max_position_embeddings: 4096
  norm_epsilon: 1e-5
  use_rotary_position_embeddings: True
  no_position_embedding: True
  swiglu: True
  multiple_of: 256
  normalization: RMSNorm
  # rotary_interleaved_patch: True
  untie_embeddings_and_output_weights: True
  init_method_std: 0.02
  attention_dropout: 0.0
  hidden_dropout: 0.0
  weight_decay: 0.1
  clip_grad: 1.0
  train_iters: 30
  eval_iters: 10
  eval_interval: 100
  micro_batch_size: 1
  global_batch_size: 1024

  optimizer:
    weight_decay: 1e-2
    adam_beta1: 0.9
    adam_beta2: 0.95
    lr_scheduler:
      lr: 0.00015
      min_lr: 1.0e-5
      lr_warmup_fraction: .01
      lr_decay_iters: 1
      lr_decay_style: cosine

data:
  data_path: ${data_path:??}
  split: 1
  tokenizer:
    tokenizer_type: Llama2Tokenizer
    tokenizer_model: examples/llama/tokenizer.model
    vocab_size: 32000