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