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TinyLlama-CPT

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TinyLlama-CPT
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global {
    ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_test
    repo=/mnt/data/jpombal/multilinguality_megatron

    external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_llama2_all_10B_test/checkpoints
    model_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf
    tokenizer_path=/mnt/data_2/cache/models--meta-llama--Llama-2-7b-hf/snapshots/6fdf2e60f86ff2481f2241aaee459f85b5b0bbb9/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
            )

    # less for zh (inefficient tokenizer)
    n_tokens=(TrainLanguage:
                en=250000000
                es=83333333
                de=83333333
                fr=83333333
                nl=83333333
                pt=83333333
                it=83333333
                ru=83333333
                zh=8333333
                ko=83333333
            )
    
    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=10
    save_interval=318
    eval_interval=158
    train_steps=1272
    
    lr_scheduler=cosine
    warmup_steps=13
    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=6

    cpu_workers=16

}