global {
    model_type="gemma"
    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B
    repo=/mnt/data/jpombal/multilinguality_megatron

    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B/flavio_checkpoints
    external_model_dir_annealing=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_gemma_2_20B/flavio_checkpoints_annealed
    model_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b/
    tokenizer_path=/mnt/data_2/cache/models--google--gemma-2b/snapshots/9d067f00def958594aaa16b39a65b07d69ca655b

    tokenizer_type=PretrainedFromHF

    dataset=(Dataset: en de fr es it nl pt ru zh ko 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 en_de_pre_annealing de_en_pre_annealing en_fr_pre_annealing fr_en_pre_annealing en_es_pre_annealing es_en_pre_annealing en_it_pre_annealing it_en_pre_annealing en_nl_pre_annealing nl_en_pre_annealing en_pt_pre_annealing pt_en_pre_annealing en_ru_pre_annealing ru_en_pre_annealing en_zh_pre_annealing zh_en_pre_annealing en_ko_pre_annealing ko_en_pre_annealing en_synth es_synth de_synth fr_synth nl_synth pt_synth it_synth ru_synth zh_synth ko_synth instructions)
    dataset_path=(Dataset: 
            en=/mnt/data_2/shared/tower_llm_data/en/data
            en_synth=""
            es=/mnt/data_2/shared/tower_llm_data/es/3/0000.json.gz 
            es_synth=""
            de=/mnt/data_2/shared/tower_llm_data/de/2/0000.json.gz 
            de_synth=""
            fr=/mnt/data_2/shared/tower_llm_data/fr/1/0000.json.gz 
            fr_synth=""
            nl=/mnt/data_2/shared/tower_llm_data/nl/0000.json.gz 
            nl_synth=""
            pt=/mnt/data_2/shared/tower_llm_data/pt/0000.json.gz   
            pt_synth=""           
            it=/mnt/data_2/shared/tower_llm_data/it/0000.json.gz 
            it_synth=""
            ru=/mnt/data_2/shared/tower_llm_data/ru/6/0000.json.gz
            ru_synth=""
            zh=/mnt/data_2/shared/tower_llm_data/zh/0000.json.gz 
            zh_synth=""
            ko=/mnt/data_2/shared/tower_llm_data/ko/0000.json.gz
            ko_synth=""
            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"
            instructions="oi"
            en_de_pre_annealing="oi"
            de_en_pre_annealing="oi"
            en_fr_pre_annealing="oi"
            fr_en_pre_annealing="oi"
            en_es_pre_annealing="oi"
            es_en_pre_annealing="oi"
            en_it_pre_annealing="oi"
            it_en_pre_annealing="oi"
            en_nl_pre_annealing="oi"
            nl_en_pre_annealing="oi"
            en_pt_pre_annealing="oi"
            pt_en_pre_annealing="oi"
            en_ru_pre_annealing="oi"
            ru_en_pre_annealing="oi"
            en_zh_pre_annealing="oi"
            zh_en_pre_annealing="oi"
            en_ko_pre_annealing="oi"
            ko_en_pre_annealing="oi"
        )
    
    is_hf_dataset=(Dataset: 
            en=True 
            es=False
            de=False
            fr=False
            nl=False
            pt=False      
            it=False
            ru=False
            zh=False
            ko=False
            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
            en_synth=False
            es_synth=False
            de_synth=False
            fr_synth=False
            nl_synth=False
            pt_synth=False
            it_synth=False
            ru_synth=False
            zh_synth=False
            ko_synth=False
            instructions="oi"
            en_de_pre_annealing="oi"
            de_en_pre_annealing="oi"
            en_fr_pre_annealing="oi"
            fr_en_pre_annealing="oi"
            en_es_pre_annealing="oi"
            es_en_pre_annealing="oi"
            en_it_pre_annealing="oi"
            it_en_pre_annealing="oi"
            en_nl_pre_annealing="oi"
            nl_en_pre_annealing="oi"
            en_pt_pre_annealing="oi"
            pt_en_pre_annealing="oi"
            en_ru_pre_annealing="oi"
            ru_en_pre_annealing="oi"
            en_zh_pre_annealing="oi"
            zh_en_pre_annealing="oi"
            en_ko_pre_annealing="oi"
            ko_en_pre_annealing="oi"
        )

    threshold=(Dataset:
                en=516       
                es=275
                de=611
                fr=322
                nl=649
                pt=257
                it=332
                ru=334
                zh=2041
                ko=198
                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
                en_synth=100000
                es_synth=100000
                de_synth=100000
                fr_synth=100000
                nl_synth=100000
                pt_synth=100000
                it_synth=100000
                ru_synth=100000
                zh_synth=100000
                ko_synth=100000
                instructions="oi"
                en_de_pre_annealing="oi"
                de_en_pre_annealing="oi"
                en_fr_pre_annealing="oi"
                fr_en_pre_annealing="oi"
                en_es_pre_annealing="oi"
                es_en_pre_annealing="oi"
                en_it_pre_annealing="oi"
                it_en_pre_annealing="oi"
                en_nl_pre_annealing="oi"
                nl_en_pre_annealing="oi"
                en_pt_pre_annealing="oi"
                pt_en_pre_annealing="oi"
                en_ru_pre_annealing="oi"
                ru_en_pre_annealing="oi"
                en_zh_pre_annealing="oi"
                zh_en_pre_annealing="oi"
                en_ko_pre_annealing="oi"
                ko_en_pre_annealing="oi"
            )

    # rougly 67% for mc4, 33% for total parallel data
    datamix_weights=(
        DataMix: 
            mc4_parallel_uniform=(
                Dataset:
                    en=603
                    es=603
                    de=603
                    fr=603
                    nl=603
                    pt=603
                    it=603
                    ru=603
                    zh=603
                    ko=603
                    en_de=0
                    de_en=0 
                    en_fr=0 
                    fr_en=0 
                    en_es=0 
                    es_en=0 
                    en_it=0 
                    it_en=0 
                    en_nl=0 
                    nl_en=0 
                    en_pt=0 
                    pt_en=0 
                    en_ru=0 
                    ru_en=0 
                    en_zh=0 
                    zh_en=0 
                    en_ko=0 
                    ko_en=0 
                    en_synth=67
                    es_synth=67
                    de_synth=67
                    fr_synth=67
                    nl_synth=67
                    pt_synth=67
                    it_synth=67
                    ru_synth=67
                    zh_synth=67
                    ko_synth=67
                    instructions=0
                    en_de_pre_annealing=183
                    de_en_pre_annealing=183
                    en_fr_pre_annealing=183
                    fr_en_pre_annealing=183
                    en_es_pre_annealing=183
                    es_en_pre_annealing=183
                    en_it_pre_annealing=183
                    it_en_pre_annealing=183
                    en_nl_pre_annealing=183
                    nl_en_pre_annealing=183
                    en_pt_pre_annealing=183
                    pt_en_pre_annealing=183
                    en_ru_pre_annealing=183
                    ru_en_pre_annealing=183
                    en_zh_pre_annealing=183
                    zh_en_pre_annealing=183
                    en_ko_pre_annealing=183
                    ko_en_pre_annealing=183
            )
        )

    datamix_weights_annealing=(
        DataMix: 
            mc4_parallel_uniform=(
                Dataset:
                    en=0
                    es=0
                    de=0
                    fr=0
                    nl=0
                    pt=0
                    it=0
                    ru=0
                    zh=0
                    ko=0
                    en_de=833
                    de_en=833
                    en_fr=833 
                    fr_en=833 
                    en_es=833 
                    es_en=833 
                    en_it=833 
                    it_en=833 
                    en_nl=833 
                    nl_en=833 
                    en_pt=833 
                    pt_en=833 
                    en_ru=833 
                    ru_en=833 
                    en_zh=833 
                    zh_en=833 
                    en_ko=833 
                    ko_en=833
                    en_synth=0
                    es_synth=0
                    de_synth=0
                    fr_synth=0
                    nl_synth=0
                    pt_synth=0
                    it_synth=0
                    ru_synth=0
                    zh_synth=0
                    ko_synth=0
                    instructions=85000
                    en_de_pre_annealing=0
                    de_en_pre_annealing=0
                    en_fr_pre_annealing=0
                    fr_en_pre_annealing=0
                    en_es_pre_annealing=0
                    es_en_pre_annealing=0
                    en_it_pre_annealing=0
                    it_en_pre_annealing=0
                    en_nl_pre_annealing=0
                    nl_en_pre_annealing=0
                    en_pt_pre_annealing=0
                    pt_en_pre_annealing=0
                    en_ru_pre_annealing=0
                    ru_en_pre_annealing=0
                    en_zh_pre_annealing=0
                    zh_en_pre_annealing=0
                    en_ko_pre_annealing=0
                    ko_en_pre_annealing=0
            )
        )


    # number such that final tokens for each language are around 1B
    n_tokens=(Dataset:
                en=1000000000
                es=833333330
                de=833333330
                fr=833333330
                nl=833333330
                pt=833333330
                it=833333330
                ru=500000000
                zh=13888888
                ko=250000000
                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
                en_synth=20000000
                es_synth=20000000
                de_synth=20000000
                fr_synth=20000000
                nl_synth=20000000
                pt_synth=20000000
                it_synth=20000000
                ru_synth=20000000
                zh_synth=20000000
                ko_synth=20000000
                instructions="oi"
                en_de_pre_annealing="oi"
                de_en_pre_annealing="oi"
                en_fr_pre_annealing="oi"
                fr_en_pre_annealing="oi"
                en_es_pre_annealing="oi"
                es_en_pre_annealing="oi"
                en_it_pre_annealing="oi"
                it_en_pre_annealing="oi"
                en_nl_pre_annealing="oi"
                nl_en_pre_annealing="oi"
                en_pt_pre_annealing="oi"
                pt_en_pre_annealing="oi"
                en_ru_pre_annealing="oi"
                ru_en_pre_annealing="oi"
                en_zh_pre_annealing="oi"
                zh_en_pre_annealing="oi"
                en_ko_pre_annealing="oi"
                ko_en_pre_annealing="oi"
            )

    is_parallel=(Dataset:
        en=False
        es=False
        de=False
        fr=False
        nl=False
        pt=False
        it=False
        ru=False
        zh=False
        ko=False
        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
        en_synth=False
        es_synth=False
        de_synth=False
        fr_synth=False
        nl_synth=False
        pt_synth=False
        it_synth=False
        ru_synth=False
        zh_synth=False
        ko_synth=False
        instructions="oi"
        en_de_pre_annealing="oi"
        de_en_pre_annealing="oi"
        en_fr_pre_annealing="oi"
        fr_en_pre_annealing="oi"
        en_es_pre_annealing="oi"
        es_en_pre_annealing="oi"
        en_it_pre_annealing="oi"
        it_en_pre_annealing="oi"
        en_nl_pre_annealing="oi"
        nl_en_pre_annealing="oi"
        en_pt_pre_annealing="oi"
        pt_en_pre_annealing="oi"
        en_ru_pre_annealing="oi"
        ru_en_pre_annealing="oi"
        en_zh_pre_annealing="oi"
        zh_en_pre_annealing="oi"
        en_ko_pre_annealing="oi"
        ko_en_pre_annealing="oi"
    )   

    lp=(Dataset:
        en=""
        es=""
        de=""
        fr=""
        nl=""
        pt=""
        it=""
        ru=""
        zh=""
        ko=""
        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"
        en_synth=""
        es_synth=""
        de_synth=""
        fr_synth=""
        nl_synth=""
        pt_synth=""
        it_synth=""
        ru_synth=""
        zh_synth=""
        ko_synth=""
        instructions="oi"
        en_de_pre_annealing="oi"
        de_en_pre_annealing="oi"
        en_fr_pre_annealing="oi"
        fr_en_pre_annealing="oi"
        en_es_pre_annealing="oi"
        es_en_pre_annealing="oi"
        en_it_pre_annealing="oi"
        it_en_pre_annealing="oi"
        en_nl_pre_annealing="oi"
        nl_en_pre_annealing="oi"
        en_pt_pre_annealing="oi"
        pt_en_pre_annealing="oi"
        en_ru_pre_annealing="oi"
        ru_en_pre_annealing="oi"
        en_zh_pre_annealing="oi"
        zh_en_pre_annealing="oi"
        en_ko_pre_annealing="oi"
        ko_en_pre_annealing="oi"
    )   

    min_perplexity=0

    size=(Size: 2)

    log_interval=1
    save_interval=635
    eval_interval=635
    train_steps=11430
    train_steps_annealing=1270
    
    lr_scheduler=constant
    warmup_steps=32
    lr=3e-5
    lr_min=3e-6
    weight_decay=0.1
    
    lr_scheduler_annealing=linear
    warmup_steps_annealing=0
    lr_annealing=3e-5
    lr_min_annealing=3e-6

    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=24
    grad_accum_steps=4
    vocab_size=256000

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

    glu_activation=geglu
    kv_channels=256
    layernorm_epsilon=1e-6

    seq_length=2048
}