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global {
model_type="llama2"
ducttape_output=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B
repo=/mnt/data/jpombal/multilinguality_megatron
external_model_dir=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/mc4_parallel_synth_pre_annealing_20B_checkpoints_doc_attn
external_model_dir_annealing=/mnt/data/shared/multilingual_llm/experiments_megatron/continue_pretraining_tinyllama_all_20B/mc4_parallel_20B_checkpoints_annealed_doc_attn
model_path=/mnt/data_2/cache/models--TinyLlama--TinyLlama-1.1B-intermediate-step-1431k-3T/snapshots/036fa4651240b9a1487f709833b9e4b96b4c1574/
tokenizer_path=/mnt/data_2/cache/models--TinyLlama--TinyLlama-1.1B-intermediate-step-1431k-3T/snapshots/036fa4651240b9a1487f709833b9e4b96b4c1574
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=637
es=637
de=637
fr=637
nl=637
pt=637
it=637
ru=637
zh=637
ko=637
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=34
es_synth=34
de_synth=34
fr_synth=34
nl_synth=34
pt_synth=34
it_synth=34
ru_synth=34
zh_synth=34
ko_synth=34
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=50
size=(Size: 1 7 13)
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=32000
cpu_workers=16
wikipedia=False
freeze_layers=""
posterior_tokens=False
n_posterior_tokens=0
eval_iters=1
kv_channels=""
glu_activation=swiglu
layernorm_epsilon=1e-5
seq_length=2048
}
|