learning_rate: 2e-05
train_batch_size: 32
eval_batch_size: 64
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: cosine
lr_scheduler_warmup_ratio: 0.01
num_epochs: 100

Training results

Training Loss	Epoch	Step	Validation Loss	Model Preparation Time	Mdl	Accumulated Loss	Correct Preds	Total Preds	Accuracy	Correct Gen Preds	Gen Accuracy	Correct Gen Preds 34192	Correct Preds 34192	Total Labels 34192	Accuracy 34192	Gen Accuracy 34192	Correct Gen Preds 41568	Correct Preds 41568	Total Labels 41568	Accuracy 41568	Gen Accuracy 41568
No log	0	0	1.4656	0.0059	5233.1723	3627.3586	1196.0	2475.0	0.4832	1204.0	0.4865	1196.0	1196.0	1196.0	1.0	1.0	0.0	0.0	1267.0	0.0	0.0
0.7326	1.0	10	0.7570	0.0059	2702.9154	1873.5182	1282.0	2475.0	0.5180	8.0	0.0032	0.0	33.0	1196.0	0.0276	0.0	0.0	1249.0	1267.0	0.9858	0.0
0.9778	2.0	20	0.8808	0.0059	3144.9448	2179.9096	1267.0	2475.0	0.5119	9.0	0.0036	0.0	0.0	1196.0	0.0	0.0	0.0	1267.0	1267.0	1.0	0.0
0.4933	3.0	30	0.6766	0.0059	2415.9199	1674.5881	1548.0	2475.0	0.6255	8.0	0.0032	0.0	1160.0	1196.0	0.9699	0.0	0.0	388.0	1267.0	0.3062	0.0
1.1978	4.0	40	0.6905	0.0059	2465.4758	1708.9376	1727.0	2475.0	0.6978	27.0	0.0109	0.0	1109.0	1196.0	0.9273	0.0	18.0	618.0	1267.0	0.4878	0.0142
0.4315	5.0	50	0.6687	0.0059	2387.7910	1655.0906	1826.0	2475.0	0.7378	373.0	0.1507	54.0	754.0	1196.0	0.6304	0.0452	311.0	1072.0	1267.0	0.8461	0.2455
0.3427	6.0	60	0.7262	0.0059	2592.9923	1797.3253	1831.0	2475.0	0.7398	272.0	0.1099	100.0	1007.0	1196.0	0.8420	0.0836	164.0	824.0	1267.0	0.6504	0.1294
0.0081	7.0	70	1.4295	0.0059	5104.1074	3537.8977	1826.0	2475.0	0.7378	1745.0	0.7051	1066.0	1090.0	1196.0	0.9114	0.8913	671.0	736.0	1267.0	0.5809	0.5296
0.0085	8.0	80	1.0812	0.0059	3860.6583	2676.0044	1875.0	2475.0	0.7576	1447.0	0.5846	707.0	989.0	1196.0	0.8269	0.5911	731.0	886.0	1267.0	0.6993	0.5770
0.0003	9.0	90	1.4846	0.0059	5300.9803	3674.3596	1882.0	2475.0	0.7604	1872.0	0.7564	835.0	843.0	1196.0	0.7048	0.6982	1030.0	1039.0	1267.0	0.8200	0.8129
0.1005	10.0	100	1.4367	0.0059	5130.0125	3555.8537	1885.0	2475.0	0.7616	1674.0	0.6764	909.0	1020.0	1196.0	0.8528	0.7600	758.0	865.0	1267.0	0.6827	0.5983
0.0	11.0	110	1.9148	0.0059	6837.0276	4739.0664	1807.0	2475.0	0.7301	1758.0	0.7103	1077.0	1103.0	1196.0	0.9222	0.9005	673.0	704.0	1267.0	0.5556	0.5312
0.0813	12.0	120	1.8798	0.0059	6712.1494	4652.5074	1781.0	2475.0	0.7196	1715.0	0.6929	1117.0	1142.0	1196.0	0.9548	0.9339	590.0	639.0	1267.0	0.5043	0.4657
0.0064	13.0	130	1.8761	0.0059	6698.9090	4643.3299	1809.0	2475.0	0.7309	1772.0	0.7160	1102.0	1114.0	1196.0	0.9314	0.9214	662.0	695.0	1267.0	0.5485	0.5225
0.0039	14.0	140	1.5355	0.0059	5482.8873	3800.4479	1901.0	2475.0	0.7681	1902.0	0.7685	1031.0	1036.0	1196.0	0.8662	0.8620	863.0	865.0	1267.0	0.6827	0.6811
0.0	15.0	150	1.9186	0.0059	6850.6854	4748.5333	1880.0	2475.0	0.7596	1876.0	0.7580	1058.0	1061.0	1196.0	0.8871	0.8846	810.0	819.0	1267.0	0.6464	0.6393
0.8402	16.0	160	1.8719	0.0059	6684.1083	4633.0708	1881.0	2475.0	0.76	1878.0	0.7588	1048.0	1052.0	1196.0	0.8796	0.8763	822.0	829.0	1267.0	0.6543	0.6488
0.0	17.0	170	1.8653	0.0059	6660.5351	4616.7311	1880.0	2475.0	0.7596	1875.0	0.7576	1046.0	1051.0	1196.0	0.8788	0.8746	821.0	829.0	1267.0	0.6543	0.6480
0.0	18.0	180	1.8660	0.0059	6662.8011	4618.3018	1883.0	2475.0	0.7608	1879.0	0.7592	1048.0	1052.0	1196.0	0.8796	0.8763	823.0	831.0	1267.0	0.6559	0.6496
0.0	19.0	190	1.8677	0.0059	6669.1108	4622.6753	1881.0	2475.0	0.76	1878.0	0.7588	1047.0	1051.0	1196.0	0.8788	0.8754	823.0	830.0	1267.0	0.6551	0.6496
0.8401	20.0	200	1.8674	0.0059	6667.9216	4621.8510	1879.0	2475.0	0.7592	1877.0	0.7584	1047.0	1051.0	1196.0	0.8788	0.8754	822.0	828.0	1267.0	0.6535	0.6488
0.0	21.0	210	1.8694	0.0059	6675.1438	4626.8571	1880.0	2475.0	0.7596	1877.0	0.7584	1047.0	1051.0	1196.0	0.8788	0.8754	822.0	829.0	1267.0	0.6543	0.6488
0.0	22.0	220	1.8717	0.0059	6683.3395	4632.5379	1884.0	2475.0	0.7612	1880.0	0.7596	1048.0	1052.0	1196.0	0.8796	0.8763	824.0	832.0	1267.0	0.6567	0.6504
0.0	23.0	230	1.8715	0.0059	6682.6701	4632.0739	1879.0	2475.0	0.7592	1876.0	0.7580	1047.0	1051.0	1196.0	0.8788	0.8754	821.0	828.0	1267.0	0.6535	0.6480
0.0	24.0	240	1.8731	0.0059	6688.0780	4635.8224	1882.0	2475.0	0.7604	1879.0	0.7592	1048.0	1052.0	1196.0	0.8796	0.8763	823.0	830.0	1267.0	0.6551	0.6496
0.8401	25.0	250	1.8756	0.0059	6697.1790	4642.1307	1881.0	2475.0	0.76	1877.0	0.7584	1049.0	1053.0	1196.0	0.8804	0.8771	820.0	828.0	1267.0	0.6535	0.6472
0.0	26.0	260	1.8749	0.0059	6694.5782	4640.3280	1883.0	2475.0	0.7608	1880.0	0.7596	1048.0	1052.0	1196.0	0.8796	0.8763	824.0	831.0	1267.0	0.6559	0.6504
0.0	27.0	270	1.8745	0.0059	6693.2298	4639.3933	1883.0	2475.0	0.7608	1880.0	0.7596	1049.0	1053.0	1196.0	0.8804	0.8771	823.0	830.0	1267.0	0.6551	0.6496
0.0	28.0	280	1.8745	0.0059	6693.1717	4639.3531	1881.0	2475.0	0.76	1877.0	0.7584	1048.0	1052.0	1196.0	0.8796	0.8763	821.0	829.0	1267.0	0.6543	0.6480
0.0	29.0	290	1.8742	0.0059	6692.1128	4638.6191	1879.0	2475.0	0.7592	1876.0	0.7580	1047.0	1051.0	1196.0	0.8788	0.8754	821.0	828.0	1267.0	0.6535	0.6480
0.8401	30.0	300	1.8717	0.0059	6683.2398	4632.4689	1882.0	2475.0	0.7604	1878.0	0.7588	1047.0	1051.0	1196.0	0.8788	0.8754	823.0	831.0	1267.0	0.6559	0.6496
0.0	31.0	310	1.8724	0.0059	6685.8100	4634.2504	1884.0	2475.0	0.7612	1882.0	0.7604	1049.0	1053.0	1196.0	0.8804	0.8771	825.0	831.0	1267.0	0.6559	0.6511
0.0	32.0	320	1.8741	0.0059	6691.6236	4638.2800	1880.0	2475.0	0.7596	1877.0	0.7584	1047.0	1051.0	1196.0	0.8788	0.8754	822.0	829.0	1267.0	0.6543	0.6488
0.0	33.0	330	1.8752	0.0059	6695.7522	4641.1417	1881.0	2475.0	0.76	1877.0	0.7584	1046.0	1050.0	1196.0	0.8779	0.8746	823.0	831.0	1267.0	0.6559	0.6496
0.0	34.0	340	1.8739	0.0059	6691.0397	4637.8753	1883.0	2475.0	0.7608	1880.0	0.7596	1049.0	1053.0	1196.0	0.8804	0.8771	823.0	830.0	1267.0	0.6551	0.6496
0.0	35.0	350	1.8770	0.0059	6702.0900	4645.5348	1880.0	2475.0	0.7596	1876.0	0.7580	1046.0	1050.0	1196.0	0.8779	0.8746	822.0	830.0	1267.0	0.6551	0.6488
0.8401	36.0	360	1.8746	0.0059	6693.5160	4639.5917	1881.0	2475.0	0.76	1878.0	0.7588	1046.0	1050.0	1196.0	0.8779	0.8746	824.0	831.0	1267.0	0.6559	0.6504
0.0	37.0	370	1.8764	0.0059	6699.8720	4643.9974	1882.0	2475.0	0.7604	1879.0	0.7592	1049.0	1053.0	1196.0	0.8804	0.8771	822.0	829.0	1267.0	0.6543	0.6488
0.0	38.0	380	1.8760	0.0059	6698.4345	4643.0010	1883.0	2475.0	0.7608	1880.0	0.7596	1049.0	1053.0	1196.0	0.8804	0.8771	823.0	830.0	1267.0	0.6551	0.6496
0.8401	39.0	390	1.8762	0.0059	6699.1595	4643.5035	1882.0	2475.0	0.7604	1876.0	0.7580	1047.0	1051.0	1196.0	0.8788	0.8754	821.0	831.0	1267.0	0.6559	0.6480
0.0	40.0	400	1.8766	0.0059	6700.5968	4644.4997	1880.0	2475.0	0.7596	1876.0	0.7580	1046.0	1050.0	1196.0	0.8779	0.8746	822.0	830.0	1267.0	0.6551	0.6488
0.0	41.0	410	1.8781	0.0059	6706.0725	4648.2953	1883.0	2475.0	0.7608	1880.0	0.7596	1048.0	1052.0	1196.0	0.8796	0.8763	824.0	831.0	1267.0	0.6559	0.6504
0.0	42.0	420	1.8763	0.0059	6699.8219	4643.9626	1882.0	2475.0	0.7604	1878.0	0.7588	1047.0	1051.0	1196.0	0.8788	0.8754	823.0	831.0	1267.0	0.6559	0.6496
0.0	43.0	430	1.8763	0.0059	6699.6423	4643.8382	1880.0	2475.0	0.7596	1876.0	0.7580	1048.0	1052.0	1196.0	0.8796	0.8763	820.0	828.0	1267.0	0.6535	0.6472
0.0	44.0	440	1.8772	0.0059	6702.7796	4646.0128	1881.0	2475.0	0.76	1878.0	0.7588	1046.0	1050.0	1196.0	0.8779	0.8746	824.0	831.0	1267.0	0.6559	0.6504

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/GSM8K-Binary_Llama-3.2-1B-nbi456jq

GSM8K-Binary_Llama-3.2-1B-nbi456jq

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
GSM8K-Binary_Llama-3.2-1B-nbi456jq