global {
    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B
    repo=/mnt/data/jpombal/multilinguality_megatron

    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_20B/parallel_checkpoints
    model_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852
    tokenizer_path=/mnt/data/cache/models--meta-llama--Llama-2-7b-hf/snapshots/8cca527612d856d7d32bd94f8103728d614eb852/tokenizer.model 

    dataset=(Dataset: en_de de_en en_fr fr_en en_es es_en en_it it_en en_nl nl_en en_pt pt_en en_ru ru_en en_zh zh_en en_ko ko_en)

    dataset_path=(Dataset:
            en_de="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
            de_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-de/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_fr="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            fr_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-fr/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_es="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            es_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-es/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_it="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            it_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-it/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_nl="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            nl_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-nl/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_pt="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            pt_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-pt/bicleaner_0.6_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_ru="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            ru_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ru/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_zh="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            zh_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-zh/no_bicleaner_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            en_ko="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75" 
            ko_en="/mnt/data/shared/tower_llm_data/bilingual_data/v1/en-ko/bicleaner_0.5_cometkiwi-wmt22-cometkiwi-da/threshold_0.75"
        )
    
    is_hf_dataset=(Dataset:
            en_de=False
            de_en=False 
            en_fr=False 
            fr_en=False 
            en_es=False 
            es_en=False 
            en_it=False 
            it_en=False 
            en_nl=False 
            nl_en=False
            en_pt=False 
            pt_en=False 
            en_ru=False 
            ru_en=False 
            en_zh=False 
            zh_en=False 
            en_ko=False 
            ko_en=False
        )

    threshold=(Dataset:
                en_de=100000
                de_en=100000 
                en_fr=100000 
                fr_en=100000 
                en_es=100000 
                es_en=100000 
                en_it=100000 
                it_en=100000 
                en_nl=100000 
                nl_en=100000 
                en_pt=100000 
                pt_en=100000 
                en_ru=100000 
                ru_en=100000 
                en_zh=100000 
                zh_en=100000 
                en_ko=100000 
                ko_en=100000
            )

    # rougly 67% for mc4, 33% for total parallel data
    datamix_weights=(
        DataMix: 
            mc4_parallel_uniform=(
                Dataset:
                    en_de=1
                    de_en=1
                    en_fr=1
                    fr_en=1
                    en_es=1
                    es_en=1
                    en_it=1
                    it_en=1
                    en_nl=1
                    nl_en=1
                    en_pt=1
                    pt_en=1
                    en_ru=1
                    ru_en=1
                    en_zh=1
                    zh_en=1
                    en_ko=1
                    ko_en=1
            )
        )

    # number such that final tokens for each language are around 1B
    n_tokens=(Dataset:
                en_de=20000000
                de_en=20000000 
                en_fr=20000000 
                fr_en=20000000 
                en_es=20000000 
                es_en=20000000
                en_it=20000000 
                it_en=20000000 
                en_nl=20000000 
                nl_en=20000000 
                en_pt=20000000 
                pt_en=20000000 
                en_ru=20000000 
                ru_en=20000000 
                en_zh=20000000
                zh_en=20000000 
                en_ko=20000000
                ko_en=20000000
            )

    is_parallel=(Dataset:
        en_de=True
        de_en=True
        en_fr=True
        fr_en=True
        en_es=True
        es_en=True
        en_it=True
        it_en=True
        en_nl=True
        nl_en=True
        en_pt=True
        pt_en=True
        en_ru=True
        ru_en=True
        en_zh=True
        zh_en=True
        en_ko=True
        ko_en=True
    )   

    lp=(Dataset:
        en_de="en-de"
        de_en="de-en"
        en_fr="en-fr"
        fr_en="fr-en"
        en_es="en-es"
        es_en="es-en"
        en_it="en-it"
        it_en="it-en"
        en_nl="en-nl"
        nl_en="nl-en"
        en_pt="en-pt"
        pt_en="pt-en"
        en_ru="en-ru"
        ru_en="ru-en"
        en_zh="en-zh"
        zh_en="zh-en"
        en_ko="en-ko"
        ko_en="ko-en"
    )   

    min_perplexity=50

    size=(Size: 7 13)

    log_interval=1
    save_interval=635
    eval_interval=635
    train_steps=12700
    
    lr_scheduler=cosine
    warmup_steps=127
    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)
    pp=(PP: 1 2 3 4)
    micro_batch_size=4
    grad_accum_steps=12
    vocab_size=32000

    cpu_workers=16
    wandb_run_id="llama2_7B_20b_base_vocab_uniform_cleaned_ppl_thresh_516_275_611_322_649_257_332_334_2041_198_and_parallel_33"
    wikipedia=False
    freeze_layers=""
    posterior_tokens=False
    n_posterior_tokens=0
    eval_iters=1
}