global {
    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/llama2_13B_all
    repo=/mnt/data/jpombal/multilinguality_megatron

    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/llama2_13B_all/checkpoints
    model_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/db6b8eb1feabb38985fdf785a89895959e944936
    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-13b-hf/snapshots/db6b8eb1feabb38985fdf785a89895959e944936/tokenizer.model 

    train_language=(TrainLanguage: en de fr es it nl pt ru zh ko)
    
    threshold=(TrainLanguage:
                en=516
                es=275
                de=611
                fr=322
                nl=649
                pt=257
                it=332
                ru=334
                zh=2041
                ko=198
            )

    # number such that final tokens for each language are around 1B
    n_tokens=(TrainLanguage:
                en=1000000000
                es=833333330
                de=833333330
                fr=833333330
                nl=833333330
                pt=833333330
                it=833333330
                ru=500000000
                zh=13888888
                ko=250000000
            )
    
    dataset_path=(TrainLanguage: 
                en=/mnt/data_2/shared/tower_llm_data/en/data
                es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz
                de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz
                fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz
                nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz
                pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz             
                it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz
                ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
                zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz
                ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
            )

    mix="10 10 10 10 10 10 10 10 10 10"                 

    min_perplexity=50

    size=(Size: 7 13)

    log_interval=1
    save_interval=10
    eval_interval=635
    train_steps=10
    
    lr_scheduler=cosine 
    warmup_steps=0
    lr=3e-5
    lr_min=3e-6
    weight_decay=0.1
    
    n_gpus=8
    gpu_ids=0,1,2,3,4,5,6,7
    tp=(TP: 1 2 3 4 5 6 7 8)
    pp=(PP: 1 2 3 4)
    micro_batch_size=4
    grad_accum_steps=12
    vocab_size=32000

    cpu_workers=16
    wandb_run_id="test_llama_13B"
    wikipedia=False
    freeze_layers=""
}