global { model_type="mistral" ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_mistral_all_20B repo=/mnt/data/jpombal/multilinguality_megatron external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_mistral_all_20B/mc4_parallel_checkpoints model_path=/mnt/data/cache/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24 tokenizer_path=/mnt/data/cache/models--mistralai--Mistral-7B-v0.1/snapshots/26bca36bde8333b5d7f72e9ed20ccda6a618af24/tokenizer.model 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) 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 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=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 ) 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 ) # rougly 67% for mc4, 33% for total parallel data datamix_weights=( DataMix: mc4_parallel_uniform=( Dataset: en=670 es=670 de=670 fr=670 nl=670 pt=670 it=670 ru=670 zh=670 ko=670 en_de=183 de_en=183 en_fr=183 fr_en=183 en_es=183 es_en=183 en_it=183 it_en=183 en_nl=183 nl_en=183 en_pt=183 pt_en=183 en_ru=183 ru_en=183 en_zh=183 zh_en=183 en_ko=183 ko_en=183 ) ) # 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 ) 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 ) 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" ) 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 wikipedia=False freeze_layers="" posterior_tokens=False n_posterior_tokens=0 eval_iters=1 }