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	0.0058	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
1.0365	1.0	41	0.7442	0.0058	2657.1555	1841.7998	1698.0	2475.0	0.6861	1609.0	0.6501	552.0	633.0	1196.0	0.5293	0.4615	1049.0	1065.0	1267.0	0.8406	0.8279
0.8382	2.0	82	0.5588	0.0058	1995.1639	1382.9423	1815.0	2475.0	0.7333	354.0	0.1430	15.0	1146.0	1196.0	0.9582	0.0125	331.0	669.0	1267.0	0.5280	0.2612
0.8522	3.0	123	0.6851	0.0058	2446.3901	1695.7084	1847.0	2475.0	0.7463	349.0	0.1410	111.0	1149.0	1196.0	0.9607	0.0928	230.0	698.0	1267.0	0.5509	0.1815
0.4153	4.0	164	0.5204	0.0058	1858.1740	1287.9881	1977.0	2475.0	0.7988	1351.0	0.5459	499.0	944.0	1196.0	0.7893	0.4172	844.0	1033.0	1267.0	0.8153	0.6661
0.7977	5.0	205	0.7190	0.0058	2567.2475	1779.4804	1980.0	2475.0	0.8	1245.0	0.5030	561.0	1080.0	1196.0	0.9030	0.4691	675.0	900.0	1267.0	0.7103	0.5328
0.001	6.0	246	1.2247	0.0058	4372.8577	3031.0340	1947.0	2475.0	0.7867	1648.0	0.6659	936.0	1104.0	1196.0	0.9231	0.7826	703.0	843.0	1267.0	0.6654	0.5549
0.0026	7.0	287	1.0541	0.0058	3763.8213	2608.8821	1966.0	2475.0	0.7943	1568.0	0.6335	873.0	1066.0	1196.0	0.8913	0.7299	686.0	900.0	1267.0	0.7103	0.5414
0.0366	8.0	328	1.2770	0.0058	4559.6621	3160.5169	1948.0	2475.0	0.7871	1751.0	0.7075	847.0	950.0	1196.0	0.7943	0.7082	895.0	998.0	1267.0	0.7877	0.7064
0.0	9.0	369	1.6923	0.0058	6042.5959	4188.4083	1954.0	2475.0	0.7895	1837.0	0.7422	1026.0	1117.0	1196.0	0.9339	0.8579	801.0	837.0	1267.0	0.6606	0.6322
0.0002	10.0	410	1.8498	0.0058	6605.0876	4578.2979	1946.0	2475.0	0.7863	1926.0	0.7782	1104.0	1118.0	1196.0	0.9348	0.9231	813.0	828.0	1267.0	0.6535	0.6417
0.7352	11.0	451	1.9100	0.0058	6819.8267	4727.1437	1969.0	2475.0	0.7956	1964.0	0.7935	1099.0	1106.0	1196.0	0.9247	0.9189	855.0	863.0	1267.0	0.6811	0.6748
0.0	12.0	492	1.5599	0.0058	5570.0379	3860.8561	2009.0	2475.0	0.8117	2008.0	0.8113	1022.0	1026.0	1196.0	0.8579	0.8545	977.0	983.0	1267.0	0.7758	0.7711
0.0	13.0	533	1.5753	0.0058	5624.9198	3898.8973	2008.0	2475.0	0.8113	2005.0	0.8101	1027.0	1033.0	1196.0	0.8637	0.8587	969.0	975.0	1267.0	0.7695	0.7648
0.0	14.0	574	1.5762	0.0058	5628.2132	3901.1801	2008.0	2475.0	0.8113	2006.0	0.8105	1029.0	1034.0	1196.0	0.8645	0.8604	968.0	974.0	1267.0	0.7687	0.7640
1.4702	15.0	615	1.5799	0.0058	5641.1668	3910.1588	2009.0	2475.0	0.8117	2007.0	0.8109	1027.0	1032.0	1196.0	0.8629	0.8587	971.0	977.0	1267.0	0.7711	0.7664
0.0	16.0	656	1.5812	0.0058	5645.8715	3913.4199	2009.0	2475.0	0.8117	2007.0	0.8109	1029.0	1035.0	1196.0	0.8654	0.8604	969.0	974.0	1267.0	0.7687	0.7648
0.0	17.0	697	1.5816	0.0058	5647.2796	3914.3960	2009.0	2475.0	0.8117	2008.0	0.8113	1029.0	1034.0	1196.0	0.8645	0.8604	970.0	975.0	1267.0	0.7695	0.7656
0.0	18.0	738	1.5835	0.0058	5653.9903	3919.0474	2011.0	2475.0	0.8125	2009.0	0.8117	1030.0	1035.0	1196.0	0.8654	0.8612	970.0	976.0	1267.0	0.7703	0.7656
0.0	19.0	779	1.5847	0.0058	5658.5138	3922.1829	2006.0	2475.0	0.8105	2003.0	0.8093	1027.0	1033.0	1196.0	0.8637	0.8587	967.0	973.0	1267.0	0.7680	0.7632
0.0	20.0	820	1.5855	0.0058	5661.4129	3924.1924	2009.0	2475.0	0.8117	2007.0	0.8109	1029.0	1035.0	1196.0	0.8654	0.8604	969.0	974.0	1267.0	0.7687	0.7648
0.0	21.0	861	1.5872	0.0058	5667.5375	3928.4376	2008.0	2475.0	0.8113	2006.0	0.8105	1028.0	1034.0	1196.0	0.8645	0.8595	969.0	974.0	1267.0	0.7687	0.7648
0.0	22.0	902	1.5892	0.0058	5674.6021	3933.3344	2009.0	2475.0	0.8117	2008.0	0.8113	1030.0	1035.0	1196.0	0.8654	0.8612	969.0	974.0	1267.0	0.7687	0.7648
0.0	23.0	943	1.5898	0.0058	5676.7926	3934.8528	2010.0	2475.0	0.8121	2008.0	0.8113	1030.0	1036.0	1196.0	0.8662	0.8612	969.0	974.0	1267.0	0.7687	0.7648
0.0	24.0	984	1.5899	0.0058	5676.8491	3934.8920	2011.0	2475.0	0.8125	2010.0	0.8121	1031.0	1037.0	1196.0	0.8671	0.8620	970.0	974.0	1267.0	0.7687	0.7656
0.7351	25.0	1025	1.5905	0.0058	5679.0066	3936.3874	2006.0	2475.0	0.8105	2006.0	0.8105	1029.0	1034.0	1196.0	0.8645	0.8604	968.0	972.0	1267.0	0.7672	0.7640
0.0	26.0	1066	1.5921	0.0058	5684.9764	3940.5253	2008.0	2475.0	0.8113	2006.0	0.8105	1029.0	1035.0	1196.0	0.8654	0.8604	968.0	973.0	1267.0	0.7680	0.7640
0.0	27.0	1107	1.5941	0.0058	5691.8627	3945.2986	2008.0	2475.0	0.8113	2007.0	0.8109	1029.0	1034.0	1196.0	0.8645	0.8604	969.0	974.0	1267.0	0.7687	0.7648
0.0	28.0	1148	1.5959	0.0058	5698.4968	3949.8970	2011.0	2475.0	0.8125	2009.0	0.8117	1031.0	1037.0	1196.0	0.8671	0.8620	969.0	974.0	1267.0	0.7687	0.7648
0.0	29.0	1189	1.5953	0.0058	5696.1814	3948.2921	2010.0	2475.0	0.8121	2007.0	0.8109	1029.0	1035.0	1196.0	0.8654	0.8604	969.0	975.0	1267.0	0.7695	0.7648
0.0	30.0	1230	1.5954	0.0058	5696.5394	3948.5402	2009.0	2475.0	0.8117	2006.0	0.8105	1029.0	1035.0	1196.0	0.8654	0.8604	968.0	974.0	1267.0	0.7687	0.7640

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-28w952gf

GSM8K-Binary_Llama-3.2-1B-28w952gf

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
GSM8K-Binary_Llama-3.2-1B-28w952gf