TinyLlama-CPT / multilinguality_megatron /ducttape /20B_all_cleaned_parallel.tconf
sonalsannigrahi's picture
Upload 382 files (#1)
a93e458 verified
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
}