multilinguality_megatron/ducttape/20B_all_cleaned_mc4.tconf

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/mc4_checkpoints
    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9
    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/tokenizer.model 

    dataset=(Dataset: en de fr es it nl pt ru zh ko)

    dataset_path=(Dataset: 
            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
        )
    
    is_hf_dataset=(Dataset: 
            en=True 
            es=False
            de=False
            fr=False
            nl=False
            pt=False      
            it=False
            ru=False
            zh=False
            ko=False
        )

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

    datamix_weights=(
        DataMix:
            mc4_uniform=(
                Dataset:
                    en=100
                    es=100
                    de=100
                    fr=100
                    nl=100
                    pt=100
                    it=100
                    ru=100
                    zh=100
                    ko=100
            )
        )

    # 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
            )            

    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_wiki_33"
    wikipedia=False
    freeze_layers=""
    posterior_tokens=False
    n_posterior_tokens=False
    eval_iters=1
    is_parallel=False
    lp=""
}