learning_rate: 2e-05
train_batch_size: 64
eval_batch_size: 112
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: constant
lr_scheduler_warmup_ratio: 0.001
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 32	Correct Preds 32	Total Labels 32	Accuracy 32	Gen Accuracy 32	Correct Gen Preds 33	Correct Preds 33	Total Labels 33	Accuracy 33	Gen Accuracy 33	Correct Gen Preds 34	Correct Preds 34	Total Labels 34	Accuracy 34	Gen Accuracy 34	Correct Gen Preds 35	Correct Preds 35	Total Labels 35	Accuracy 35	Gen Accuracy 35
No log	0	0	1.5354	0.0029	1262.6022	875.1692	172.0	570.0	0.3018	170.0	0.2982	154.0	154.0	158.0	0.9747	0.9747	0.0	0.0	152.0	0.0	0.0	15.0	17.0	142.0	0.1197	0.1056	1.0	1.0	118.0	0.0085	0.0085
0.8841	1.0	19	0.8074	0.0029	663.9330	460.2033	408.0	570.0	0.7158	408.0	0.7158	100.0	100.0	158.0	0.6329	0.6329	108.0	108.0	152.0	0.7105	0.7105	117.0	117.0	142.0	0.8239	0.8239	83.0	83.0	118.0	0.7034	0.7034
0.1498	2.0	38	0.7927	0.0029	651.8592	451.8344	416.0	570.0	0.7298	406.0	0.7123	94.0	100.0	158.0	0.6329	0.5949	110.0	110.0	152.0	0.7237	0.7237	118.0	119.0	142.0	0.8380	0.8310	84.0	87.0	118.0	0.7373	0.7119
0.1299	3.0	57	1.0549	0.0029	867.4895	601.2979	422.0	570.0	0.7404	422.0	0.7404	108.0	108.0	158.0	0.6835	0.6835	116.0	116.0	152.0	0.7632	0.7632	113.0	113.0	142.0	0.7958	0.7958	85.0	85.0	118.0	0.7203	0.7203
0.3028	4.0	76	1.7886	0.0029	1470.7927	1019.4758	429.0	570.0	0.7526	400.0	0.7018	95.0	112.0	158.0	0.7089	0.6013	117.0	117.0	152.0	0.7697	0.7697	111.0	113.0	142.0	0.7958	0.7817	77.0	87.0	118.0	0.7373	0.6525
0.0791	5.0	95	1.9509	0.0029	1604.3195	1112.0295	414.0	570.0	0.7263	414.0	0.7263	126.0	126.0	158.0	0.7975	0.7975	105.0	105.0	152.0	0.6908	0.6908	102.0	102.0	142.0	0.7183	0.7183	81.0	81.0	118.0	0.6864	0.6864
0.0	6.0	114	3.1210	0.0029	2566.4979	1778.9608	420.0	570.0	0.7368	420.0	0.7368	135.0	135.0	158.0	0.8544	0.8544	112.0	112.0	152.0	0.7368	0.7368	104.0	104.0	142.0	0.7324	0.7324	69.0	69.0	118.0	0.5847	0.5847
0.0	7.0	133	3.2118	0.0029	2641.1866	1830.7310	419.0	570.0	0.7351	419.0	0.7351	117.0	117.0	158.0	0.7405	0.7405	118.0	118.0	152.0	0.7763	0.7763	107.0	107.0	142.0	0.7535	0.7535	77.0	77.0	118.0	0.6525	0.6525
0.0	8.0	152	3.2079	0.0029	2637.9722	1828.5030	420.0	570.0	0.7368	420.0	0.7368	116.0	116.0	158.0	0.7342	0.7342	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	9.0	171	3.2459	0.0029	2669.2619	1850.1914	419.0	570.0	0.7351	419.0	0.7351	115.0	115.0	158.0	0.7278	0.7278	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	10.0	190	3.2370	0.0029	2661.9270	1845.1072	421.0	570.0	0.7386	421.0	0.7386	115.0	115.0	158.0	0.7278	0.7278	116.0	116.0	152.0	0.7632	0.7632	112.0	112.0	142.0	0.7887	0.7887	78.0	78.0	118.0	0.6610	0.6610
0.0	11.0	209	3.2289	0.0029	2655.2382	1840.4708	417.0	570.0	0.7316	417.0	0.7316	115.0	115.0	158.0	0.7278	0.7278	114.0	114.0	152.0	0.75	0.75	110.0	110.0	142.0	0.7746	0.7746	78.0	78.0	118.0	0.6610	0.6610
0.0	12.0	228	3.2391	0.0029	2663.6307	1846.2881	420.0	570.0	0.7368	420.0	0.7368	114.0	114.0	158.0	0.7215	0.7215	115.0	115.0	152.0	0.7566	0.7566	112.0	112.0	142.0	0.7887	0.7887	79.0	79.0	118.0	0.6695	0.6695
0.0	13.0	247	3.2552	0.0029	2676.9086	1855.4916	417.0	570.0	0.7316	417.0	0.7316	114.0	114.0	158.0	0.7215	0.7215	114.0	114.0	152.0	0.75	0.75	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	14.0	266	3.2378	0.0029	2662.5893	1845.5663	419.0	570.0	0.7351	419.0	0.7351	115.0	115.0	158.0	0.7278	0.7278	114.0	114.0	152.0	0.75	0.75	111.0	111.0	142.0	0.7817	0.7817	79.0	79.0	118.0	0.6695	0.6695
0.0	15.0	285	3.2417	0.0029	2665.7960	1847.7890	419.0	570.0	0.7351	419.0	0.7351	115.0	115.0	158.0	0.7278	0.7278	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	16.0	304	3.2255	0.0029	2652.4751	1838.5557	419.0	570.0	0.7351	419.0	0.7351	116.0	116.0	158.0	0.7342	0.7342	115.0	115.0	152.0	0.7566	0.7566	110.0	110.0	142.0	0.7746	0.7746	78.0	78.0	118.0	0.6610	0.6610
0.0	17.0	323	3.2438	0.0029	2667.5001	1848.9702	418.0	570.0	0.7333	418.0	0.7333	114.0	114.0	158.0	0.7215	0.7215	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	18.0	342	3.2539	0.0029	2675.8310	1854.7447	420.0	570.0	0.7368	420.0	0.7368	115.0	115.0	158.0	0.7278	0.7278	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	79.0	79.0	118.0	0.6695	0.6695
0.0	19.0	361	3.2494	0.0029	2672.0629	1852.1328	418.0	570.0	0.7333	418.0	0.7333	114.0	114.0	158.0	0.7215	0.7215	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	20.0	380	3.2371	0.0029	2662.0202	1845.1718	418.0	570.0	0.7333	418.0	0.7333	114.0	114.0	158.0	0.7215	0.7215	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	21.0	399	3.2359	0.0029	2661.0214	1844.4795	420.0	570.0	0.7368	420.0	0.7368	116.0	116.0	158.0	0.7342	0.7342	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	78.0	78.0	118.0	0.6610	0.6610
0.0	22.0	418	3.2474	0.0029	2670.4175	1850.9924	420.0	570.0	0.7368	420.0	0.7368	115.0	115.0	158.0	0.7278	0.7278	115.0	115.0	152.0	0.7566	0.7566	111.0	111.0	142.0	0.7817	0.7817	79.0	79.0	118.0	0.6695	0.6695

Framework versions

Transformers 4.51.3
Pytorch 2.6.0+cu124
Datasets 3.5.0
Tokenizers 0.21.1

donoway/ARC-Easy_Llama-3.2-1B-g3heq50u

ARC-Easy_Llama-3.2-1B-g3heq50u

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

donoway/
ARC-Easy_Llama-3.2-1B-g3heq50u