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-0.9-core
time python -B prepare_core_datasets.py
i=0, min_len=0, max_len=1073741824, block_size=1025, chunk_size=16400000, len(dataset)=5146620, len(dataset) * block_size=5275285500
Total number of tokens in the optimized dataset '../core-data-0-0-1073741824-1025-16000' is 5275285500
i=1, min_len=1025, max_len=2049, block_size=2049, chunk_size=16392000, len(dataset)=309838, len(dataset) * block_size=634858062
Total number of tokens in the optimized dataset '../core-data-1-1025-2049-2049-8000' is 634858062
i=2, min_len=2049, max_len=4097, block_size=4097, chunk_size=16388000, len(dataset)=113843, len(dataset) * block_size=466414771
Total number of tokens in the optimized dataset '../core-data-2-2049-4097-4097-4000' is 466414771
i=3, min_len=4097, max_len=8193, block_size=8193, chunk_size=16386000, len(dataset)=56713, len(dataset) * block_size=464649609
Total number of tokens in the optimized dataset '../core-data-3-4097-8193-8193-2000' is 464649609
i=4, min_len=8193, max_len=16385, block_size=16385, chunk_size=16385000, len(dataset)=37406, len(dataset) * block_size=612897310
Total number of tokens in the optimized dataset '../core-data-4-8193-16385-16385-1000' is 612897310
i=5, min_len=16385, max_len=32769, block_size=32769, chunk_size=16384500, len(dataset)=12737, len(dataset) * block_size=417378753
Total number of tokens in the optimized dataset '../core-data-5-16385-32769-32769-500' is 417378753
i=6, min_len=32769, max_len=65537, block_size=65537, chunk_size=16384250, len(dataset)=2824, len(dataset) * block_size=185076488
Total number of tokens in the optimized dataset '../core-data-6-32769-65537-65537-250' is 185076488
i=7, min_len=65537, max_len=131073, block_size=131073, chunk_size=16384125, len(dataset)=634, len(dataset) * block_size=83100282
Total number of tokens in the optimized dataset '../core-data-7-65537-131073-131073-125' is 83100282
real 292m54.341s
user 2118m1.154s
sys 12m2.746s
20G tangled-alpha-0.9-core/core-data-0-0-1073741824-1025-16000
2.4G tangled-alpha-0.9-core/core-data-1-1025-2049-2049-8000
1.8G tangled-alpha-0.9-core/core-data-2-2049-4097-4097-4000
1.8G tangled-alpha-0.9-core/core-data-3-4097-8193-8193-2000
2.3G tangled-alpha-0.9-core/core-data-4-8193-16385-16385-1000
1.6G tangled-alpha-0.9-core/core-data-5-16385-32769-32769-500
709M tangled-alpha-0.9-core/core-data-6-32769-65537-65537-250
321M tangled-alpha-0.9-core/core-data-7-65537-131073-131073-125
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_core_model_0.yaml
Seed set to 23
Time to instantiate model: 0.44 seconds.
Total parameters: 234,914,304
Verifying settings ...
Measured TFLOPs: 55520.94
Epoch 1 | iter 64 step 1 | loss train: 11.977, val: n/a | iter time: 490.27 ms (step) remaining time: 6 days, 22:47:04
Epoch 1 | iter 128 step 2 | loss train: 11.970, val: n/a | iter time: 351.11 ms (step) remaining time: 4 days, 16:53:01
Epoch 1 | iter 192 step 3 | loss train: 11.971, val: n/a | iter time: 353.74 ms (step) remaining time: 3 days, 23:43:23
Epoch 1 | iter 256 step 4 | loss train: 11.974, val: n/a | iter time: 355.03 ms (step) remaining time: 3 days, 14:41:57
Epoch 1 | iter 320 step 5 | loss train: 11.964, val: n/a | iter time: 357.36 ms (step) remaining time: 3 days, 9:21:54
Epoch 1 | iter 384 step 6 | loss train: 11.957, val: n/a | iter time: 362.27 ms (step) remaining time: 3 days, 5:53:20
Epoch 1 | iter 448 step 7 | loss train: 11.948, val: n/a | iter time: 359.89 ms (step) remaining time: 3 days, 3:26:34
Epoch 1 | iter 512 step 8 | loss train: 11.938, val: n/a | iter time: 363.84 ms (step) remaining time: 3 days, 1:37:54
Epoch 1 | iter 576 step 9 | loss train: 11.920, val: n/a | iter time: 362.75 ms (step) remaining time: 3 days, 0:13:59
Epoch 1 | iter 640 step 10 | loss train: 11.900, val: n/a | iter time: 363.46 ms (step) remaining time: 2 days, 23:07:06
# ...
Epoch 1 | iter 643264 step 10051 | loss train: 2.834, val: 2.669 | iter time: 360.50 ms (step) remaining time: 0:03:59
Epoch 2 | iter 643328 step 10052 | loss train: 2.837, val: 2.669 | iter time: 359.53 ms (step) remaining time: 0:03:37
Epoch 2 | iter 643392 step 10053 | loss train: 2.768, val: 2.669 | iter time: 362.83 ms (step) remaining time: 0:03:15
Epoch 2 | iter 643456 step 10054 | loss train: 2.695, val: 2.669 | iter time: 363.85 ms (step) remaining time: 0:02:53
Epoch 2 | iter 643520 step 10055 | loss train: 2.768, val: 2.669 | iter time: 365.40 ms (step) remaining time: 0:02:30
Epoch 2 | iter 643584 step 10056 | loss train: 2.710, val: 2.669 | iter time: 364.72 ms (step) remaining time: 0:02:08
Epoch 2 | iter 643648 step 10057 | loss train: 2.749, val: 2.669 | iter time: 365.00 ms (step) remaining time: 0:01:46
Epoch 2 | iter 643712 step 10058 | loss train: 2.748, val: 2.669 | iter time: 363.42 ms (step) remaining time: 0:01:24
Epoch 2 | iter 643776 step 10059 | loss train: 2.710, val: 2.669 | iter time: 364.49 ms (step) remaining time: 0:01:02
Epoch 2 | iter 643840 step 10060 | loss train: 2.738, val: 2.669 | iter time: 364.43 ms (step) remaining time: 0:00:39
Epoch 2 | iter 643904 step 10061 | loss train: 2.734, val: 2.669 | iter time: 364.94 ms (step) remaining time: 0:00:17
Validating ...
Final evaluation | val loss: 2.669 | val ppl: 14.422
Saving checkpoint to '../out/pretrain-core-0/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens : 5,275,279,360
| - Training Time : 223314.37 s
| - Tok/sec : 5541.09 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used : 22.33 GB
----------------------------------------
Backup wandb:
mv wandb wandb-pretrain-core-0
Copy config:
cp ../config-0.json ../out/pretrain-core-0/final/config.json
Chat with model:
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core-0/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-0/leaderboard/' --batch_size '4' --dtype 'bfloat16' '../out/pretrain-core-0/final'
| Tasks |Version|Filter|n-shot| Metric | |Value | |Stderr|
|-----------------------------------------------------------|-------|------|-----:|-----------------------|---|-----:|---|------|
|leaderboard | N/A| | | | | | | |
| - leaderboard_bbh | N/A| | | | | | | |
| - leaderboard_bbh_boolean_expressions | 1|none | 3|acc_norm |↑ |0.4600|± |0.0316|
| - leaderboard_bbh_causal_judgement | 1|none | 3|acc_norm |↑ |0.5134|± |0.0366|
| - leaderboard_bbh_date_understanding | 1|none | 3|acc_norm |↑ |0.1960|± |0.0252|
| - leaderboard_bbh_disambiguation_qa | 1|none | 3|acc_norm |↑ |0.3320|± |0.0298|
| - leaderboard_bbh_formal_fallacies | 1|none | 3|acc_norm |↑ |0.4680|± |0.0316|
| - leaderboard_bbh_geometric_shapes | 1|none | 3|acc_norm |↑ |0.2400|± |0.0271|
| - leaderboard_bbh_hyperbaton | 1|none | 3|acc_norm |↑ |0.5160|± |0.0317|
| - leaderboard_bbh_logical_deduction_five_objects | 1|none | 3|acc_norm |↑ |0.2040|± |0.0255|
| - leaderboard_bbh_logical_deduction_seven_objects | 1|none | 3|acc_norm |↑ |0.1320|± |0.0215|
| - leaderboard_bbh_logical_deduction_three_objects | 1|none | 3|acc_norm |↑ |0.3440|± |0.0301|
| - leaderboard_bbh_movie_recommendation | 1|none | 3|acc_norm |↑ |0.2680|± |0.0281|
| - leaderboard_bbh_navigate | 1|none | 3|acc_norm |↑ |0.5720|± |0.0314|
| - leaderboard_bbh_object_counting | 1|none | 3|acc_norm |↑ |0.0680|± |0.0160|
| - leaderboard_bbh_penguins_in_a_table | 1|none | 3|acc_norm |↑ |0.2055|± |0.0336|
| - leaderboard_bbh_reasoning_about_colored_objects | 1|none | 3|acc_norm |↑ |0.1760|± |0.0241|
| - leaderboard_bbh_ruin_names | 1|none | 3|acc_norm |↑ |0.2120|± |0.0259|
| - leaderboard_bbh_salient_translation_error_detection | 1|none | 3|acc_norm |↑ |0.2240|± |0.0264|
| - leaderboard_bbh_snarks | 1|none | 3|acc_norm |↑ |0.5393|± |0.0375|
| - leaderboard_bbh_sports_understanding | 1|none | 3|acc_norm |↑ |0.4600|± |0.0316|
| - leaderboard_bbh_temporal_sequences | 1|none | 3|acc_norm |↑ |0.2760|± |0.0283|
| - leaderboard_bbh_tracking_shuffled_objects_five_objects | 1|none | 3|acc_norm |↑ |0.1720|± |0.0239|
| - leaderboard_bbh_tracking_shuffled_objects_seven_objects| 1|none | 3|acc_norm |↑ |0.1360|± |0.0217|
| - leaderboard_bbh_tracking_shuffled_objects_three_objects| 1|none | 3|acc_norm |↑ |0.3320|± |0.0298|
| - leaderboard_bbh_web_of_lies | 1|none | 3|acc_norm |↑ |0.4880|± |0.0317|
| - leaderboard_gpqa | N/A| | | | | | | |
| - leaderboard_gpqa_diamond | 1|none | 0|acc_norm |↑ |0.2071|± |0.0289|
| - leaderboard_gpqa_extended | 1|none | 0|acc_norm |↑ |0.2637|± |0.0189|
| - leaderboard_gpqa_main | 1|none | 0|acc_norm |↑ |0.2612|± |0.0208|
| - leaderboard_ifeval | 3|none | 0|inst_level_loose_acc |↑ |0.2770|± | N/A|
| | |none | 0|inst_level_strict_acc |↑ |0.2710|± | N/A|
| | |none | 0|prompt_level_loose_acc |↑ |0.1534|± |0.0155|
| | |none | 0|prompt_level_strict_acc|↑ |0.1497|± |0.0154|
| - leaderboard_math_hard | N/A| | | | | | | |
| - leaderboard_math_algebra_hard | 2|none | 4|exact_match |↑ |0.0017|± |0.0012|
| - leaderboard_math_counting_and_prob_hard | 2|none | 4|exact_match |↑ |0.0000|± | 0|
| - leaderboard_math_geometry_hard | 2|none | 4|exact_match |↑ |0.0000|± | 0|
| - leaderboard_math_intermediate_algebra_hard | 2|none | 4|exact_match |↑ |0.0033|± |0.0019|
| - leaderboard_math_num_theory_hard | 2|none | 4|exact_match |↑ |0.0037|± |0.0026|
| - leaderboard_math_prealgebra_hard | 2|none | 4|exact_match |↑ |0.0046|± |0.0023|
| - leaderboard_math_precalculus_hard | 2|none | 4|exact_match |↑ |0.0000|± | 0|
| - leaderboard_mmlu_pro | 0.1|none | 5|acc |↑ |0.1068|± |0.0028|
| - leaderboard_musr | N/A| | | | | | | |
| - leaderboard_musr_murder_mysteries | 1|none | 0|acc_norm |↑ |0.5160|± |0.0317|
| - leaderboard_musr_object_placements | 1|none | 0|acc_norm |↑ |0.2344|± |0.0265|
| - leaderboard_musr_team_allocation | 1|none | 0|acc_norm |↑ |0.3200|± |0.0296|
litgpt convert_pretrained_checkpoint ../out/pretrain-core-0/final ../out/pretrain-core-0/checkpoint
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_core_model_1.yaml
Seed set to 23
Time to instantiate model: 0.32 seconds.
Total parameters: 234,914,304
Validating ...
Measured TFLOPs: 27760.47
Epoch 1 | iter 128 step 1 | loss train: 3.205, val: 3.457 | iter time: 450.87 ms (step) remaining time: 10:13:33
Epoch 1 | iter 256 step 2 | loss train: 3.175, val: 3.457 | iter time: 386.03 ms (step) remaining time: 9:00:10
Epoch 1 | iter 384 step 3 | loss train: 3.144, val: 3.457 | iter time: 388.88 ms (step) remaining time: 8:35:53
Epoch 1 | iter 512 step 4 | loss train: 3.260, val: 3.457 | iter time: 390.66 ms (step) remaining time: 8:24:17
Epoch 1 | iter 640 step 5 | loss train: 3.247, val: 3.457 | iter time: 392.99 ms (step) remaining time: 8:17:54
Epoch 1 | iter 768 step 6 | loss train: 3.264, val: 3.457 | iter time: 397.01 ms (step) remaining time: 8:13:51
Epoch 1 | iter 896 step 7 | loss train: 3.232, val: 3.457 | iter time: 396.00 ms (step) remaining time: 8:11:00
Epoch 1 | iter 1024 step 8 | loss train: 3.287, val: 3.457 | iter time: 396.93 ms (step) remaining time: 8:08:43
Epoch 1 | iter 1152 step 9 | loss train: 3.236, val: 3.457 | iter time: 398.67 ms (step) remaining time: 8:06:49
Epoch 1 | iter 1280 step 10 | loss train: 3.274, val: 3.457 | iter time: 399.49 ms (step) remaining time: 8:05:09
# ...
Epoch 1 | iter 76928 step 601 | loss train: 3.177, val: 3.304 | iter time: 400.61 ms (step) remaining time: 0:03:35
Epoch 1 | iter 77056 step 602 | loss train: 3.191, val: 3.304 | iter time: 396.14 ms (step) remaining time: 0:02:46
Epoch 1 | iter 77184 step 603 | loss train: 3.173, val: 3.304 | iter time: 399.39 ms (step) remaining time: 0:01:58
Epoch 1 | iter 77312 step 604 | loss train: 3.211, val: 3.304 | iter time: 398.61 ms (step) remaining time: 0:01:09
Epoch 1 | iter 77440 step 605 | loss train: 3.203, val: 3.304 | iter time: 399.31 ms (step) remaining time: 0:00:21
Validating ...
Final evaluation | val loss: 3.304 | val ppl: 27.217
Saving checkpoint to '../out/pretrain-core-1/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens : 634,855,424
| - Training Time : 29361.39 s
| - Tok/sec : 524.18 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used : 22.33 GB
----------------------------------------
mv wandb wandb-pretrain-core-1
cp ../config-1.json ../out/pretrain-core-1/final/config.json
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core-1/final
litgpt convert_pretrained_checkpoint ../out/pretrain-core-1/final ../out/pretrain-core-1/checkpoint
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_core_model_2.yaml
Seed set to 23
Time to instantiate model: 0.30 seconds.
Total parameters: 234,914,304
Validating ...
Measured TFLOPs: 13880.23
Epoch 1 | iter 256 step 1 | loss train: 2.857, val: 3.078 | iter time: 518.53 ms (step) remaining time: 8:49:46
Epoch 1 | iter 512 step 2 | loss train: 2.839, val: 3.078 | iter time: 461.04 ms (step) remaining time: 7:47:05
Epoch 1 | iter 768 step 3 | loss train: 2.835, val: 3.078 | iter time: 462.82 ms (step) remaining time: 7:27:16
Epoch 1 | iter 1024 step 4 | loss train: 2.872, val: 3.078 | iter time: 464.55 ms (step) remaining time: 7:16:22
Epoch 1 | iter 1280 step 5 | loss train: 2.867, val: 3.078 | iter time: 462.06 ms (step) remaining time: 7:09:02
Epoch 1 | iter 1536 step 6 | loss train: 2.899, val: 3.078 | iter time: 465.26 ms (step) remaining time: 7:03:30
Epoch 1 | iter 1792 step 7 | loss train: 2.878, val: 3.078 | iter time: 465.57 ms (step) remaining time: 6:59:00
Epoch 1 | iter 2048 step 8 | loss train: 2.919, val: 3.078 | iter time: 464.37 ms (step) remaining time: 6:55:10
Epoch 1 | iter 2304 step 9 | loss train: 2.922, val: 3.078 | iter time: 464.24 ms (step) remaining time: 6:51:45
Epoch 1 | iter 2560 step 10 | loss train: 2.924, val: 3.078 | iter time: 464.71 ms (step) remaining time: 6:48:39
# ...
Epoch 1 | iter 53760 step 210 | loss train: 2.904, val: 3.013 | iter time: 468.66 ms (step) remaining time: 0:23:26
Epoch 1 | iter 54016 step 211 | loss train: 2.903, val: 3.013 | iter time: 468.81 ms (step) remaining time: 0:21:32
Epoch 1 | iter 54272 step 212 | loss train: 2.951, val: 3.013 | iter time: 463.52 ms (step) remaining time: 0:19:39
Epoch 1 | iter 54528 step 213 | loss train: 2.941, val: 3.013 | iter time: 466.12 ms (step) remaining time: 0:17:45
Epoch 1 | iter 54784 step 214 | loss train: 2.950, val: 3.013 | iter time: 468.24 ms (step) remaining time: 0:15:52
Epoch 1 | iter 55040 step 215 | loss train: 2.943, val: 3.013 | iter time: 466.65 ms (step) remaining time: 0:13:59
Epoch 1 | iter 55296 step 216 | loss train: 2.903, val: 3.013 | iter time: 464.44 ms (step) remaining time: 0:12:05
Epoch 1 | iter 55552 step 217 | loss train: 2.954, val: 3.013 | iter time: 465.12 ms (step) remaining time: 0:10:12
Epoch 1 | iter 55808 step 218 | loss train: 2.907, val: 3.013 | iter time: 464.96 ms (step) remaining time: 0:08:19
Epoch 1 | iter 56064 step 219 | loss train: 2.909, val: 3.013 | iter time: 467.12 ms (step) remaining time: 0:06:25
Epoch 1 | iter 56320 step 220 | loss train: 2.908, val: 3.013 | iter time: 466.43 ms (step) remaining time: 0:04:32
Epoch 1 | iter 56576 step 221 | loss train: 2.894, val: 3.013 | iter time: 469.70 ms (step) remaining time: 0:02:38
Epoch 1 | iter 56832 step 222 | loss train: 2.809, val: 3.013 | iter time: 463.88 ms (step) remaining time: 0:00:45
Validating ...
Final evaluation | val loss: 3.011 | val ppl: 20.306
Saving checkpoint to '../out/pretrain-core-1/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens : 466,411,520
| - Training Time : 25263.31 s
| - Tok/sec : 371.33 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used : 22.33 GB
----------------------------------------
cp ../config-2.json ../out/pretrain-core-2/final/config.json
mv wandb wandb-pretrain-core-2
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core-2/final
litgpt convert_pretrained_checkpoint ../out/pretrain-core-2/final ../out/pretrain-core-2/checkpoint
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt pretrain --config pretrain_core_model_3.yaml
Seed set to 23
Time to instantiate model: 0.30 seconds.
Total parameters: 234,914,304
Validating ...
Measured TFLOPs: 6940.12
Epoch 1 | iter 512 step 1 | loss train: 2.698, val: 2.522 | iter time: 675.96 ms (step) remaining time: 9:49:31
Epoch 1 | iter 1024 step 2 | loss train: 2.627, val: 2.522 | iter time: 603.66 ms (step) remaining time: 9:19:41
Epoch 1 | iter 1536 step 3 | loss train: 2.653, val: 2.522 | iter time: 604.66 ms (step) remaining time: 9:06:15
Epoch 1 | iter 2048 step 4 | loss train: 2.608, val: 2.522 | iter time: 606.23 ms (step) remaining time: 8:57:08
Epoch 1 | iter 2560 step 5 | loss train: 2.604, val: 2.522 | iter time: 605.04 ms (step) remaining time: 8:49:43
Epoch 1 | iter 3072 step 6 | loss train: 2.578, val: 2.522 | iter time: 606.32 ms (step) remaining time: 8:43:08
Epoch 1 | iter 3584 step 7 | loss train: 2.692, val: 2.522 | iter time: 605.08 ms (step) remaining time: 8:37:01
Epoch 1 | iter 4096 step 8 | loss train: 2.570, val: 2.522 | iter time: 607.54 ms (step) remaining time: 8:31:20
Epoch 1 | iter 4608 step 9 | loss train: 2.646, val: 2.522 | iter time: 607.19 ms (step) remaining time: 8:25:47
Epoch 1 | iter 5120 step 10 | loss train: 2.565, val: 2.522 | iter time: 604.76 ms (step) remaining time: 8:20:23
# ...
Epoch 1 | iter 51712 step 101 | loss train: 2.562, val: 2.453 | iter time: 607.12 ms (step) remaining time: 0:48:29
Epoch 1 | iter 52224 step 102 | loss train: 2.637, val: 2.453 | iter time: 605.46 ms (step) remaining time: 0:43:31
Epoch 1 | iter 52736 step 103 | loss train: 2.629, val: 2.453 | iter time: 604.15 ms (step) remaining time: 0:38:34
Epoch 1 | iter 53248 step 104 | loss train: 2.629, val: 2.453 | iter time: 605.92 ms (step) remaining time: 0:33:36
Epoch 1 | iter 53760 step 105 | loss train: 2.606, val: 2.453 | iter time: 604.48 ms (step) remaining time: 0:28:38
Epoch 1 | iter 54272 step 106 | loss train: 2.581, val: 2.453 | iter time: 603.78 ms (step) remaining time: 0:23:41
Epoch 1 | iter 54784 step 107 | loss train: 2.580, val: 2.453 | iter time: 605.41 ms (step) remaining time: 0:18:43
Epoch 1 | iter 55296 step 108 | loss train: 2.602, val: 2.453 | iter time: 607.38 ms (step) remaining time: 0:13:46
Epoch 1 | iter 55808 step 109 | loss train: 2.633, val: 2.453 | iter time: 606.06 ms (step) remaining time: 0:08:49
Epoch 1 | iter 56320 step 110 | loss train: 2.631, val: 2.453 | iter time: 608.68 ms (step) remaining time: 0:03:51
Validating ...
iter 56320: val loss 2.4515, val time: 19303.40 ms
Saving checkpoint to '../out/pretrain-core-3/step-00000110/lit_model.pth'
Validating ...
Final evaluation | val loss: 2.451 | val ppl: 11.605
Saving checkpoint to '../out/pretrain-core-3/final/lit_model.pth'
----------------------------------------
| Performance
| - Total tokens : 464,642,048
| - Training Time : 33018.19 s
| - Tok/sec : 362.46 tok/s
| ----------------------------------------
| Memory Usage
| - Memory Used : 22.33 GB
----------------------------------------
cp ../config-3.json ../out/pretrain-core-3/final/config.json
mv wandb wandb-pretrain-core-3
CUDA_VISIBLE_DEVICES=0 CUDA_LAUNCH_BLOCKING=0 PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True litgpt chat ../out/pretrain-core-3/final
litgpt convert_pretrained_checkpoint ../out/pretrain-core-3/final ../out/pretrain-core-3/checkpoint
litgpt convert_from_litgpt ../out/pretrain-core-3/final ../out/cpt-core-pre-4
cp ../config-4.json ../out/cpt-core-pre-4/config.json
cp -rv ../tokenizer/* ../out/cpt-core-pre-4
python -B convert_pth_to_safetensors.py
CUDA_VISIBLE_DEVICES=0 python -B cpt_core_model_4.py
🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.
🦥 Unsloth Zoo will now patch everything to make training faster!
==((====))== Unsloth 2025.3.14: Fast Llama patching. Transformers: 4.49.0.
\\ /| NVIDIA GeForce RTX 3090. Num GPUs = 1. Max memory: 23.58 GB. Platform: Linux.
O^O/ \_/ \ Torch: 2.6.0+cu124. CUDA: 8.6. CUDA Toolkit: 12.4. Triton: 3.2.0
\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post3. FA2 = False]
"-____-" Free license: http://github.com/unslothai/unsloth
Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!
Unsloth 2025.3.14 patched 32 layers with 32 QKV layers, 32 O layers and 32 MLP layers.
Unsloth: Training embed_tokens in mixed precision to save VRAM
Unsloth: Training lm_head in mixed precision to save VRAM
==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1
\\ /| Num examples = 37,031 | Num Epochs = 2 | Total steps = 37,406
O^O/ \_/ \ Batch size per device = 1 | Gradient accumulation steps = 1
\ / Data Parallel GPUs = 1 | Total batch size (1 x 1 x 1) = 1
"-____-" Trainable parameters = 247,463,936/482,378,240 (51.30% trained)
wandb: Using wandb-core as the SDK backend. Please refer to https://wandb.me/wandb-core for more information.
wandb: Currently logged in as: mtasic85 to https://api.wandb.ai. Use `wandb login --relogin` to force relogin
wandb: Tracking run with wandb version 0.19.8
wandb: Run data is saved locally in /home/tangled/tangled-alpha-0.9-core/scripts/wandb/run-20250315_170030-4hx8rh26
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run cpt-core-4
wandb: ⭐️ View project at https://wandb.ai/mtasic85/tangled-alpha-0.9-core
wandb: 🚀 View run at https://wandb.ai/mtasic85/tangled-alpha-0.9-core/runs/4hx8rh26
{'loss': 1.7227, 'grad_norm': 0.4360397756099701, 'learning_rate': 4.999999991182871e-05, 'epoch': 0.0}
0%| | 1/37406 [00:03<32:10:32, 3.10s/it]
Unsloth: Will smartly offload gradients to save VRAM!
{'loss': 3.296, 'grad_norm': 0.6972701549530029, 'learning_rate': 4.999999964731482e-05, 'epoch': 0.0}
{'loss': 1.7377, 'grad_norm': 0.5415557026863098, 'learning_rate': 4.9999999206458345e-05, 'epoch': 0.0}
{'loss': 1.774, 'grad_norm': 0.3695605993270874, 'learning_rate': 4.99999985892593e-05, 'epoch': 0.0}
0%| | 4/37406 [00:09<24:03:55, 2.32s/it]
# ...