metadata
license: mit
pipeline_tag: text-generation
library_name: transformers
language:
- en
- am
- ar
- as
- az
- be
- bg
- bn
- br
- bs
- ca
- cs
- cy
- da
- de
- el
- eo
- es
- et
- eu
- fa
- ff
- fi
- fr
- fy
- ga
- gd
- gl
- gn
- gu
- ha
- he
- hi
- hr
- ht
- hu
- hy
- id
- ig
- is
- it
- ja
- jv
- ka
- kk
- km
- kn
- ko
- ku
- ky
- la
- lg
- li
- ln
- lo
- lt
- lv
- mg
- mk
- ml
- mn
- mr
- ms
- my
- ne
- nl
- 'no'
- ns
- om
- or
- pa
- pl
- ps
- pt
- qu
- rm
- ro
- ru
- sa
- si
- sc
- sd
- sk
- sl
- so
- sq
- sr
- ss
- su
- sv
- sw
- ta
- te
- th
- tl
- tn
- tr
- ug
- uk
- ur
- uz
- vi
- wo
- xh
- yi
- yo
- zu
datasets:
- ontocord/fineweb-permissive-multilingual-2m
- distily/c4_multilingual_1M
- data-silence/sumnews
- xu-song/cc100-samples
- badrex/llm-emoji-dataset
- fblgit/simple-math
- Gusarich/math-expressions-1m
- neuralwork/arxiver
- christopher/rosetta-code
- nampdn-ai/tiny-codes
- JeanKaddour/minipile
- NousResearch/hermes-function-calling-v1
- simplescaling/s1K-1.1
- mlabonne/open-perfectblend
- allenai/tulu-3-sft-mixture
- rombodawg/Everything_Instruct_Multilingual
- open-r1/OpenR1-Math-220k
- open-thoughts/OpenThoughts-114k
- cognitivecomputations/dolphin-r1
- simplescaling/s1K-1.1
tags:
- chat
- core
- base
- instruct
- reason
tangled-alpha-0.1-core
time python -B prepare_core_datasets.py
Progress: 100%|████████| 220/220 [23:15<00:00, 6.34s/it]
Workers are finished.██| 220/220 [23:15<00:00, 6.34s/it]
Finished data processing!
i=0, block_size=8192, chunk_size=16384000, len(dataset)=893355, len(dataset) * block_size=7318364160
Total number of tokens in the optimized dataset '../core-data-0-8192-2000' is 7318364160
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain-core-model.yaml
Seed set to 23
Time to instantiate model: 0.24 seconds.
Total parameters: 182,125,056
Verifying settings ...
Measured TFLOPs: 7041.81
Epoch 1 | iter 256 step 1 | loss train: 10.529, val: n/a | iter time: 1696.67 ms (step) remaining time: 4 days, 7:44:36
Epoch 1 | iter 512 step 2 | loss train: 10.200, val: n/a | iter time: 1260.46 ms (step) remaining time: 4 days, 2:29:51
Epoch 1 | iter 768 step 3 | loss train: 9.875, val: n/a | iter time: 1246.06 ms (step) remaining time: 4 days, 0:59:11
Epoch 1 | iter 1024 step 4 | loss train: 9.634, val: n/a | iter time: 1245.91 ms (step) remaining time: 4 days, 0:38:01
Epoch 1 | iter 1280 step 5 | loss train: 9.504, val: n/a | iter time: 1248.04 ms (step) remaining time: 4 days, 0:28:49
Epoch 1 | iter 1536 step 6 | loss train: 9.371, val: n/a | iter time: 1220.81 ms (step) remaining time: 4 days, 0:32:52
Epoch 1 | iter 1792 step 7 | loss train: 9.269, val: n/a | iter time: 1238.00 ms (step) remaining time: 4 days, 0:30:03
Epoch 1 | iter 2048 step 8 | loss train: 9.214, val: n/a | iter time: 1244.22 ms (step) remaining time: 4 days, 0:30:30
Epoch 1 | iter 2304 step 9 | loss train: 9.109, val: n/a | iter time: 1220.57 ms (step) remaining time: 4 days, 0:25:37
Epoch 1 | iter 2560 step 10 | loss train: 9.061, val: n/a | iter time: 1251.13 ms (step) remaining time: 4 days, 0:12:57
Epoch 1 | iter 2816 step 11 | loss train: 9.031, val: n/a | iter time: 1241.17 ms (step) remaining time: 4 days, 0:05:06
Epoch 1 | iter 3072 step 12 | loss train: 8.944, val: n/a | iter time: 1280.45 ms (step) remaining time: 4 days, 0:00:31
Epoch 1 | iter 3328 step 13 | loss train: 8.931, val: n/a | iter time: 1241.07 ms (step) remaining time: 4 days, 0:00:08
Epoch 1 | iter 3584 step 14 | loss train: 8.910, val: n/a | iter time: 1229.04 ms (step) remaining time: 3 days, 23:59:03
Epoch 1 | iter 3840 step 15 | loss train: 8.823, val: n/a | iter time: 1239.92 ms (step) remaining time: 3 days, 23:55:02
Epoch 1 | iter 4096 step 16 | loss train: 8.745, val: n/a | iter time: 1239.53 ms (step) remaining time: 3 days, 23:50:02
Epoch 1 | iter 4352 step 17 | loss train: 8.679, val: n/a | iter time: 1271.10 ms (step) remaining time: 3 days, 23:46:19
Epoch 1 | iter 4608 step 18 | loss train: 8.654, val: n/a | iter time: 1246.47 ms (step) remaining time: 3 days, 23:43:27
Epoch 1 | iter 4864 step 19 | loss train: 8.651, val: n/a | iter time: 1246.56 ms (step) remaining time: 3 days, 23:41:11
Epoch 1 | iter 5120 step 20 | loss train: 8.639, val: n/a | iter time: 1219.66 ms (step) remaining time: 3 days, 23:35:38
# ...
Epoch 1 | iter 442880 step 1730 | loss train: 2.740, val: 2.863 | iter time: 1340.98 ms (step) remaining time: 0:51:28
Epoch 1 | iter 443136 step 1731 | loss train: 2.734, val: 2.863 | iter time: 1387.92 ms (step) remaining time: 0:48:00
Epoch 1 | iter 443392 step 1732 | loss train: 2.730, val: 2.863 | iter time: 1309.36 ms (step) remaining time: 0:44:31
Epoch 1 | iter 443648 step 1733 | loss train: 2.715, val: 2.863 | iter time: 1292.23 ms (step) remaining time: 0:41:03
Epoch 1 | iter 443904 step 1734 | loss train: 2.718, val: 2.863 | iter time: 1311.24 ms (step) remaining time: 0:37:35
Epoch 1 | iter 444160 step 1735 | loss train: 2.709, val: 2.863 | iter time: 1291.09 ms (step) remaining time: 0:34:07
Epoch 1 | iter 444416 step 1736 | loss train: 2.723, val: 2.863 | iter time: 1304.14 ms (step) remaining time: 0:30:39
Epoch 1 | iter 444672 step 1737 | loss train: 2.721, val: 2.863 | iter time: 1278.33 ms (step) remaining time: 0:27:10
Epoch 1 | iter 444928 step 1738 | loss train: 2.697, val: 2.863 | iter time: 1292.86 ms (step) remaining time: 0:23:42
Epoch 1 | iter 445184 step 1739 | loss train: 2.763, val: 2.863 | iter time: 1284.40 ms (step) remaining time: 0:20:14
Epoch 1 | iter 445440 step 1740 | loss train: 2.775, val: 2.863 | iter time: 1302.58 ms (step) remaining time: 0:16:46
Epoch 1 | iter 445696 step 1741 | loss train: 2.756, val: 2.863 | iter time: 1298.86 ms (step) remaining time: 0:13:18
Epoch 1 | iter 445952 step 1742 | loss train: 2.728, val: 2.863 | iter time: 1279.11 ms (step) remaining time: 0:09:49
Epoch 1 | iter 446208 step 1743 | loss train: 2.637, val: 2.863 | iter time: 1308.11 ms (step) remaining time: 0:06:21
Epoch 1 | iter 446464 step 1744 | loss train: 2.638, val: 2.863 | iter time: 1294.08 ms (step) remaining time: 0:02:53
Validating ...
Final evaluation | val loss: 2.862 | val ppl: 17.494
Saving checkpoint to '../out/pretrain-core/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens : 7,318,355,968
| - Training Time : 363457.29 s
| - Tok/sec : 2103064.60 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used : 20.93 GB
----------------------------------------
Backup wandb:
mv wandb wandb-pretrain-core
Chat with model:
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core/final
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True time litgpt evaluate --tasks 'leaderboard' --out_dir '../evaluate/pretrain-core/leaderboard/' --batch_size 1 --dtype 'bfloat16' '../out/pretrain-core/final'
# ...