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	0.0056	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
0.7213	1.0	4	1.6676	0.0056	5954.2770	4127.1903	1196.0	2475.0	0.4832	659.0	0.2663	651.0	1196.0	1196.0	1.0	0.5443	0.0	0.0	1267.0	0.0	0.0
1.531	2.0	8	1.8297	0.0056	6533.4012	4528.6086	1196.0	2475.0	0.4832	8.0	0.0032	0.0	1196.0	1196.0	1.0	0.0	0.0	0.0	1267.0	0.0	0.0
0.6658	3.0	12	0.8914	0.0056	3182.8541	2206.1864	1196.0	2475.0	0.4832	8.0	0.0032	0.0	1196.0	1196.0	1.0	0.0	0.0	0.0	1267.0	0.0	0.0
0.6605	4.0	16	0.9261	0.0056	3306.8536	2292.1362	1287.0	2475.0	0.52	81.0	0.0327	0.0	23.0	1196.0	0.0192	0.0	73.0	1264.0	1267.0	0.9976	0.0576
0.4214	5.0	20	0.6675	0.0056	2383.2606	1651.9503	1609.0	2475.0	0.6501	16.0	0.0065	0.0	576.0	1196.0	0.4816	0.0	8.0	1033.0	1267.0	0.8153	0.0063
0.2547	6.0	24	0.9045	0.0056	3229.5997	2238.5879	1563.0	2475.0	0.6315	88.0	0.0356	0.0	509.0	1196.0	0.4256	0.0	80.0	1054.0	1267.0	0.8319	0.0631
0.7195	7.0	28	0.8937	0.0056	3191.0282	2211.8522	1726.0	2475.0	0.6974	206.0	0.0832	26.0	938.0	1196.0	0.7843	0.0217	172.0	788.0	1267.0	0.6219	0.1358
0.0823	8.0	32	1.1088	0.0056	3959.1638	2744.2832	1662.0	2475.0	0.6715	458.0	0.1851	42.0	664.0	1196.0	0.5552	0.0351	408.0	998.0	1267.0	0.7877	0.3220
0.0095	9.0	36	1.3722	0.0056	4899.6036	3396.1464	1597.0	2475.0	0.6453	496.0	0.2004	123.0	701.0	1196.0	0.5861	0.1028	365.0	896.0	1267.0	0.7072	0.2881
0.6595	10.0	40	1.5864	0.0056	5664.5748	3926.3841	1596.0	2475.0	0.6448	1038.0	0.4194	235.0	484.0	1196.0	0.4047	0.1965	795.0	1112.0	1267.0	0.8777	0.6275
0.0032	11.0	44	2.6428	0.0056	9436.7009	6541.0226	1607.0	2475.0	0.6493	1448.0	0.5851	1061.0	1143.0	1196.0	0.9557	0.8871	379.0	464.0	1267.0	0.3662	0.2991
0.0073	12.0	48	1.2794	0.0056	4568.1914	3166.4290	1676.0	2475.0	0.6772	1246.0	0.5034	386.0	669.0	1196.0	0.5594	0.3227	852.0	1007.0	1267.0	0.7948	0.6725
0.6595	13.0	52	1.7627	0.0056	6293.8866	4362.5897	1689.0	2475.0	0.6824	724.0	0.2925	326.0	1020.0	1196.0	0.8528	0.2726	390.0	669.0	1267.0	0.5280	0.3078
0.0004	14.0	56	1.6203	0.0056	5785.5413	4010.2316	1772.0	2475.0	0.7160	1422.0	0.5745	705.0	933.0	1196.0	0.7801	0.5895	709.0	839.0	1267.0	0.6622	0.5596
0.0	15.0	60	1.5202	0.0056	5428.0840	3762.4611	1781.0	2475.0	0.7196	1626.0	0.6570	823.0	922.0	1196.0	0.7709	0.6881	795.0	859.0	1267.0	0.6780	0.6275
0.0	16.0	64	1.6540	0.0056	5905.7779	4093.5733	1745.0	2475.0	0.7051	1648.0	0.6659	880.0	937.0	1196.0	0.7834	0.7358	760.0	808.0	1267.0	0.6377	0.5998
0.0	17.0	68	1.9777	0.0056	7061.7979	4894.8653	1734.0	2475.0	0.7006	1657.0	0.6695	977.0	1019.0	1196.0	0.8520	0.8169	672.0	715.0	1267.0	0.5643	0.5304
0.0	18.0	72	2.0402	0.0056	7284.7708	5049.4183	1728.0	2475.0	0.6982	1671.0	0.6752	974.0	1009.0	1196.0	0.8436	0.8144	689.0	719.0	1267.0	0.5675	0.5438
0.6534	19.0	76	2.0422	0.0056	7292.1840	5054.5568	1724.0	2475.0	0.6966	1682.0	0.6796	965.0	995.0	1196.0	0.8319	0.8069	709.0	729.0	1267.0	0.5754	0.5596
0.0	20.0	80	2.0333	0.0056	7260.3879	5032.5174	1724.0	2475.0	0.6966	1686.0	0.6812	954.0	983.0	1196.0	0.8219	0.7977	724.0	741.0	1267.0	0.5848	0.5714
1.3069	21.0	84	2.0283	0.0056	7242.2577	5019.9505	1717.0	2475.0	0.6937	1682.0	0.6796	945.0	972.0	1196.0	0.8127	0.7901	729.0	745.0	1267.0	0.5880	0.5754
0.0	22.0	88	2.0281	0.0056	7241.5495	5019.4596	1718.0	2475.0	0.6941	1685.0	0.6808	943.0	968.0	1196.0	0.8094	0.7885	734.0	750.0	1267.0	0.5919	0.5793
0.0	23.0	92	2.0230	0.0056	7223.3657	5006.8555	1718.0	2475.0	0.6941	1684.0	0.6804	941.0	967.0	1196.0	0.8085	0.7868	735.0	751.0	1267.0	0.5927	0.5801
0.0	24.0	96	2.0215	0.0056	7218.1816	5003.2623	1719.0	2475.0	0.6945	1688.0	0.6820	943.0	966.0	1196.0	0.8077	0.7885	737.0	753.0	1267.0	0.5943	0.5817
0.6534	25.0	100	2.0177	0.0056	7204.5124	4993.7874	1720.0	2475.0	0.6949	1689.0	0.6824	940.0	963.0	1196.0	0.8052	0.7860	741.0	757.0	1267.0	0.5975	0.5848
0.0	26.0	104	2.0187	0.0056	7208.0183	4996.2176	1712.0	2475.0	0.6917	1679.0	0.6784	932.0	956.0	1196.0	0.7993	0.7793	739.0	756.0	1267.0	0.5967	0.5833
0.6534	27.0	108	2.0148	0.0056	7194.3356	4986.7335	1714.0	2475.0	0.6925	1684.0	0.6804	935.0	958.0	1196.0	0.8010	0.7818	741.0	756.0	1267.0	0.5967	0.5848
0.0	28.0	112	2.0173	0.0056	7203.1509	4992.8437	1719.0	2475.0	0.6945	1689.0	0.6824	933.0	955.0	1196.0	0.7985	0.7801	748.0	764.0	1267.0	0.6030	0.5904
0.0	29.0	116	2.0147	0.0056	7193.9318	4986.4536	1721.0	2475.0	0.6954	1687.0	0.6816	933.0	958.0	1196.0	0.8010	0.7801	746.0	763.0	1267.0	0.6022	0.5888
0.0	30.0	120	2.0154	0.0056	7196.4785	4988.2188	1718.0	2475.0	0.6941	1687.0	0.6816	930.0	954.0	1196.0	0.7977	0.7776	749.0	764.0	1267.0	0.6030	0.5912
0.0	31.0	124	2.0145	0.0056	7193.1358	4985.9018	1712.0	2475.0	0.6917	1680.0	0.6788	927.0	950.0	1196.0	0.7943	0.7751	745.0	762.0	1267.0	0.6014	0.5880
0.0	32.0	128	2.0136	0.0056	7189.8018	4983.5909	1715.0	2475.0	0.6929	1686.0	0.6812	931.0	952.0	1196.0	0.7960	0.7784	747.0	763.0	1267.0	0.6022	0.5896
0.0	33.0	132	2.0152	0.0056	7195.4569	4987.5107	1713.0	2475.0	0.6921	1683.0	0.68	928.0	951.0	1196.0	0.7952	0.7759	747.0	762.0	1267.0	0.6014	0.5896
0.6534	34.0	136	2.0129	0.0056	7187.5236	4982.0117	1715.0	2475.0	0.6929	1683.0	0.68	926.0	948.0	1196.0	0.7926	0.7742	749.0	767.0	1267.0	0.6054	0.5912
0.0	35.0	140	2.0173	0.0056	7203.1617	4992.8512	1717.0	2475.0	0.6937	1686.0	0.6812	924.0	947.0	1196.0	0.7918	0.7726	754.0	770.0	1267.0	0.6077	0.5951
0.6534	36.0	144	2.0156	0.0056	7197.1919	4988.7132	1717.0	2475.0	0.6937	1689.0	0.6824	930.0	951.0	1196.0	0.7952	0.7776	751.0	766.0	1267.0	0.6046	0.5927
0.6534	37.0	148	2.0148	0.0056	7194.3476	4986.7418	1716.0	2475.0	0.6933	1687.0	0.6816	928.0	949.0	1196.0	0.7935	0.7759	751.0	767.0	1267.0	0.6054	0.5927
0.6534	38.0	152	2.0180	0.0056	7205.7094	4994.6171	1718.0	2475.0	0.6941	1687.0	0.6816	926.0	948.0	1196.0	0.7926	0.7742	753.0	770.0	1267.0	0.6077	0.5943
0.6534	39.0	156	2.0146	0.0056	7193.5903	4986.2168	1719.0	2475.0	0.6945	1691.0	0.6832	929.0	949.0	1196.0	0.7935	0.7768	754.0	770.0	1267.0	0.6077	0.5951
0.0	40.0	160	2.0167	0.0056	7201.0656	4991.3983	1717.0	2475.0	0.6937	1687.0	0.6816	928.0	950.0	1196.0	0.7943	0.7759	751.0	767.0	1267.0	0.6054	0.5927
0.6535	41.0	164	2.0178	0.0056	7205.0332	4994.1485	1718.0	2475.0	0.6941	1685.0	0.6808	926.0	948.0	1196.0	0.7926	0.7742	751.0	770.0	1267.0	0.6077	0.5927
0.0	42.0	168	2.0133	0.0056	7188.8401	4982.9242	1718.0	2475.0	0.6941	1688.0	0.6820	927.0	949.0	1196.0	0.7935	0.7751	753.0	769.0	1267.0	0.6069	0.5943
0.0	43.0	172	2.0141	0.0056	7191.6229	4984.8531	1715.0	2475.0	0.6929	1685.0	0.6808	926.0	948.0	1196.0	0.7926	0.7742	751.0	767.0	1267.0	0.6054	0.5927
0.6534	44.0	176	2.0150	0.0056	7194.8591	4987.0963	1717.0	2475.0	0.6937	1687.0	0.6816	926.0	948.0	1196.0	0.7926	0.7742	753.0	769.0	1267.0	0.6069	0.5943
0.0	45.0	180	2.0143	0.0056	7192.3952	4985.3885	1723.0	2475.0	0.6962	1694.0	0.6844	927.0	949.0	1196.0	0.7935	0.7751	759.0	774.0	1267.0	0.6109	0.5991

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-qpuz1des

GSM8K-Binary_Llama-3.2-1B-qpuz1des

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
GSM8K-Binary_Llama-3.2-1B-qpuz1des