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LOG_ARGS="--log_interval 1 --save_interval 2500 --eval_interval 2500 --eval_iters 10"
TRAIN_ARGS="--train_iters 50000 --lr_decay_style cosine
--lr_warmup_iters 50 --lr 1e-5 --min_lr 1e-6 --use_flash_attn
--attention_dropout 0.0 --adam_beta1 0.9 --adam_beta2 0.95 --adam_eps 1e-5"
### for one node, 8GPUs
DISTRIBUTED_ARGS="--nproc_per_node 8 --nnodes 1 --node_rank 0 --master_addr localhost --master_port 8000"
# ### for multi nodes, 8GPUs
# DISTRIBUTED_ARGS="--nproc_per_node 8 --nnodes 2 --node_rank $RANK --master_addr $MASTER_ADDR --master_port $MASTER_PORT"
LLAMA2_ARGS=" --no_bias_gelu_fusion --no_bias_dropout_fusion
--seq_length 4096 --max_position_embeddings 4096
--hidden_dropout 0.0 --rope_scaling_factor 1.0"
CUDA_DEVICE_MAX_CONNECTIONS=1 \
torchrun $DISTRIBUTED_ARGS finetune.py \
--tensor_model_parallel_size 8 \
--pipeline_model_parallel_size 1 \
--load megatron_test/llama2-megatron-t8p1 \
--save megatron_test/model_test \
--tensorboard_dir megatron_test/model_test/logs/ \
--data_path megatron_test/indonesian_mix_data/indonesian_mix_data_text_document \
--model_name llama2 \
--tokenizer_type SentencePieceTokenizer \
--vocab_file megatron_test/llama2_tokenizer.model \
--bf16 \
--micro_batch_size 4 \
--global_batch_size 128 \
--sequence_parallel \
--recompute_granularity selective \
--use_checkpoint_args \
$COMMON_ARGS $LOG_ARGS $TRAIN_ARGS $LLAMA2_ARGS
### Increase the micro_batch_size, if you have 80G GPU
### Decrease the micro_batch_size, if you need to train larger model |