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#! /bin/bash
# Put your WANDB API key here to enable logging to wandb.
export WANDB_API_KEY=''
# TPU specific flags to improve training throughput
export LIBTPU_INIT_ARGS='--xla_jf_spmd_threshold_for_windowed_einsum_mib=0 --xla_tpu_spmd_threshold_for_allgather_cse=10000 --xla_tpu_spmd_rewrite_einsum_with_reshape=true --jax_enable_async_collective_offload=true --xla_tpu_enable_latency_hiding_scheduler=true TPU_MEGACORE=MEGACORE_DENSE'
python3 -m EasyLM.models.llama.llama_train \
--jax_distributed.initialize_jax_distributed=True \
--mesh_dim='1,-1,4' \
--dtype='bf16' \
--total_steps=1000000 \
--eval_freq=50000 \
--log_freq=1000 \
--save_model_freq=2000 \
--save_milestone_freq=50000 \
--load_llama_config='7b' \
--update_llama_config='' \
--load_dataset_state='' \
--load_checkpoint='' \
--tokenizer.pretrained_model_name_or_path='./' \
--optimizer.type='lion' \
--optimizer.lion_optimizer.weight_decay=1.0 \
--optimizer.lion_optimizer.lr=3e-5 \
--optimizer.lion_optimizer.end_lr=3e-6 \
--optimizer.lion_optimizer.lr_warmup_steps=2000 \
--optimizer.lion_optimizer.lr_decay_steps=1000000 \
--optimizer.lion_optimizer.bf16_momentum=True \
--train_dataset.type='huggingface' \
--train_dataset.text_processor.fields='text' \
--train_dataset.huggingface_dataset.path='/researchdisk/lm_training_dataset_v2_filtered' \
--train_dataset.huggingface_dataset.split='train' \
--train_dataset.huggingface_dataset.seq_length=2048 \
--train_dataset.huggingface_dataset.batch_size=64 \
--eval_dataset.type='huggingface' \
--eval_dataset.text_processor.fields='text' \
--eval_dataset.huggingface_dataset.path='/researchdisk/lm_training_dataset_v2_filtered' \
--eval_dataset.huggingface_dataset.split='validation' \
--eval_dataset.huggingface_dataset.seq_length=2048 \
--eval_dataset.huggingface_dataset.batch_size=64 \
--checkpointer.save_optimizer_state=True \
--logger.online=True \
--logger.prefix='EasyLM' \
--logger.project="open_llama_7b" \
--logger.output_dir="gs://finnish-nlp-research/llama-7b-checkpoint" \
--logger.wandb_dir="./"
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